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Adediwura VA, Koirala K, Do HN, Wang J, Miao Y. Understanding the impact of binding free energy and kinetics calculations in modern drug discovery. Expert Opin Drug Discov 2024; 19:671-682. [PMID: 38722032 PMCID: PMC11108734 DOI: 10.1080/17460441.2024.2349149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.
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Affiliation(s)
- Victor A. Adediwura
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kushal Koirala
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hung N. Do
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
- Present address: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Jinan Wang
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yinglong Miao
- Department of Pharmacology and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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2
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Champion C, Hünenberger PH, Riniker S. Multistate Method to Efficiently Account for Tautomerism and Protonation in Alchemical Free-Energy Calculations. J Chem Theory Comput 2024; 20:4350-4362. [PMID: 38742760 DOI: 10.1021/acs.jctc.4c00370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The majority of drug-like molecules contain at least one ionizable group, and many common drug scaffolds are subject to tautomeric equilibria. Thus, these compounds are found in a mixture of protonation and/or tautomeric states at physiological pH. Intrinsically, standard classical molecular dynamics (MD) simulations cannot describe such equilibria between states, which negatively impacts the prediction of key molecular properties in silico. Following the formalism described by de Oliveira and co-workers (J. Chem. Theory Comput. 2019, 15, 424-435) to consider the influence of all states on the binding process based on alchemical free-energy calculations, we demonstrate in this work that the multistate method replica-exchange enveloping distribution sampling (RE-EDS) is well suited to describe molecules with multiple protonation and/or tautomeric states in a single simulation. We apply our methodology to a series of eight inhibitors of factor Xa with two protonation states and a series of eight inhibitors of glycogen synthase kinase 3β (GSK3β) with two tautomeric states. In particular, we show that given a sufficient phase-space overlap between the states, RE-EDS is computationally more efficient than standard pairwise free-energy methods.
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Affiliation(s)
- Candide Champion
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Philippe H Hünenberger
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Sereina Riniker
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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3
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Lagardère L, Maurin L, Adjoua O, El Hage K, Monmarché P, Piquemal JP, Hénin J. Lambda-ABF: Simplified, Portable, Accurate, and Cost-Effective Alchemical Free-Energy Computation. J Chem Theory Comput 2024. [PMID: 38805379 DOI: 10.1021/acs.jctc.3c01249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
We introduce the lambda-Adaptive Biasing Force (lambda-ABF) method for the computation of alchemical free-energy differences. We propose a software implementation and showcase it on biomolecular systems. The method arises from coupling multiple-walker adaptive biasing force with λ-dynamics. The sampling of the alchemical variable is continuous and converges toward a uniform distribution, making manual optimization of the λ schedule unnecessary. Contrary to most other approaches, alchemical free-energy estimates are obtained immediately without any postprocessing. Free diffusion of λ improves orthogonal relaxation compared to fixed-λ thermodynamic integration or free-energy perturbation. Furthermore, multiple walkers provide generic orthogonal space coverage with minimal user input and negligible computational overhead. We show that our high-performance implementations coupling the Colvars library with NAMD and Tinker-HP can address real-world cases including ligand-receptor binding with both fixed-charge and polarizable models, with a demonstrably richer sampling than fixed-λ methods. The implementation is fully open-source, publicly available, and readily usable by practitioners of current alchemical methods. Thanks to the portable Colvars library, lambda-ABF presents a unified user interface regardless of the back-end (NAMD, Tinker-HP, or any software to be interfaced in the future), sparing users the effort of learning multiple interfaces. Finally, the Colvars Dashboard extension of the visual molecular dynamics (VMD) software provides an interactive monitoring and diagnostic tool for lambda-ABF simulations.
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Affiliation(s)
- Louis Lagardère
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Sorbonne Université, Institut Parisien de Chimie Physique et Théorique, FR2622 CNRS, 75005 Paris, France
- Qubit Pharmaceuticals, 29 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Lise Maurin
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Sorbonne Université, Laboratoire Jacques-Louis Lions, UMR 7589 CNRS, 75005 Paris, France
| | - Olivier Adjoua
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
| | - Krystel El Hage
- Qubit Pharmaceuticals, 29 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Pierre Monmarché
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Sorbonne Université, Laboratoire Jacques-Louis Lions, UMR 7589 CNRS, 75005 Paris, France
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, Paris 75005, France
- Qubit Pharmaceuticals, 29 rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Jérôme Hénin
- Laboratoire de Biochimie Théorique, Université Paris Cité, CNRS, UPR 9080, 75005 Paris, France
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4
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Bosio S, Bernetti M, Rocchia W, Masetti M. Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin. J Chem Inf Model 2024. [PMID: 38800845 DOI: 10.1021/acs.jcim.4c00420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.
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Affiliation(s)
- Stefano Bosio
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Mattia Bernetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy
| | - Walter Rocchia
- Computational mOdelling of NanosCalE and bioPhysical sysTems (CONCEPT) Lab, Istituto Italiano di Tecnologia, Via Melen - 83, B Block, 16152 Genova, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum - University of Bologna, Via Belmeloro 6, 40126 Bologna, Italy
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5
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Burger PB, Hu X, Balabin I, Muller M, Stanley M, Joubert F, Kaiser TM. FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology. J Chem Inf Model 2024; 64:3812-3825. [PMID: 38651738 PMCID: PMC11094716 DOI: 10.1021/acs.jcim.4c00071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.
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Affiliation(s)
- Pieter B. Burger
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Xiaohu Hu
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ilya Balabin
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Morné Muller
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
| | - Megan Stanley
- Microsoft
Research AI4Science, 21 Station Road, Cambridge CB1 2FB, U.K.
| | - Fourie Joubert
- Centre
for Bioinformatics and Computational Biology, Department of Biochemistry,
Genetics and Microbiology, University of
Pretoria, Pretoria 0001, South Africa
| | - Thomas M. Kaiser
- Avicenna
Biosciences Inc., 101
W. Chapel Hill Street, Suite 210, Durham, North Carolina 27001, United States
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6
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Chung MKJ, Ponder JW. Beyond isotropic repulsion: Classical anisotropic repulsion by inclusion of p orbitals. J Chem Phys 2024; 160:174118. [PMID: 38748037 PMCID: PMC11078554 DOI: 10.1063/5.0203678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/15/2024] [Indexed: 05/19/2024] Open
Abstract
Accurate modeling of intermolecular repulsion is an integral component in force field development. Although repulsion can be explicitly calculated by applying the Pauli exclusion principle, this approach is computationally viable only for systems of limited sizes. Instead, it has previously been shown that repulsion can be reformulated in a "classical" picture: the Pauli exclusion principle prohibits electrons from occupying the same state, leading to a depletion of electronic charge between atoms, giving rise to an enhanced nuclear-nuclear electrostatic repulsion. This classical picture is called the isotropic S2/R approximation, where S is the overlap and R is the interatomic distance. This approximation accurately captures the repulsion of isotropic atoms such as noble gas dimers; however, a key deficiency is that it fails to capture the angular dependence of the repulsion of anisotropic molecules. To include directionality, the wave function must at least be a linear combination of s and p orbitals. We derive a new anisotropic S2/R repulsion model through the inclusion of the anisotropic p orbital term in the total wave function. Because repulsion is pairwise and decays rapidly, it can be truncated at a short range, making it amenable for efficient calculation of energy and forces in complex biomolecular systems. We present a parameterization of the S101 dimer database against the ab initio benchmark symmetry-adapted perturbation theory, which yields an rms error of only 0.9 kcal/mol. The importance of the anisotropic term is demonstrated through angular scans of water-water dimers and dimers involving halobenzene. Simulation of liquid water shows that the model can be computed efficiently for realistic system sizes.
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Affiliation(s)
| | - Jay W. Ponder
- Author to whom correspondence should be addressed: . Tel.: 314-935-4275
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7
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Zeng J, Qian Y. Adaptive lambda schemes for efficient relative binding free energy calculation. J Comput Chem 2024; 45:855-862. [PMID: 38153254 DOI: 10.1002/jcc.27287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/13/2023] [Accepted: 12/03/2023] [Indexed: 12/29/2023]
Abstract
The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.
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Affiliation(s)
- Jin Zeng
- AIxplorerBio Biotech Co., Ltd., Jiaxing, Zhejiang Province, China
| | - Yue Qian
- Viva Biotech (Shanghai) Ltd., Shanghai, China
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8
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Chen M, Jiang X, Zhang L, Chen X, Wen Y, Gu Z, Li X, Zheng M. The emergence of machine learning force fields in drug design. Med Res Rev 2024; 44:1147-1182. [PMID: 38173298 DOI: 10.1002/med.22008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.
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Affiliation(s)
- Mingan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Xinyu Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Lehan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoxu Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Yiming Wen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Zhiyong Gu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
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9
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Sampson JM, Cannon DA, Duan J, Epstein JCK, Sergeeva AP, Katsamba PS, Mannepalli SM, Bahna FA, Adihou H, Guéret SM, Gopalakrishnan R, Geschwindner S, Rees DG, Sigurdardottir A, Wilkinson T, Dodd RB, De Maria L, Mobarec JC, Shapiro L, Honig B, Buchanan A, Friesner RA, Wang L. Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590325. [PMID: 38712280 PMCID: PMC11071377 DOI: 10.1101/2024.04.22.590325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.
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Affiliation(s)
| | | | - Jianxin Duan
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | | | - Alina P. Sergeeva
- Columbia University, Department of Systems Biology, New York, NY, USA
| | | | - Seetha M. Mannepalli
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
| | - Fabiana A. Bahna
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
| | - Hélène Adihou
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stéphanie M. Guéret
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Ranganath Gopalakrishnan
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stefan Geschwindner
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | | | | | | | - Roger B. Dodd
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | - Leonardo De Maria
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Juan Carlos Mobarec
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | - Lawrence Shapiro
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
- Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
| | - Barry Honig
- Columbia University, Department of Systems Biology, New York, NY, USA
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
- Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
- Columbia University, Department of Medicine, New York, NY, USA
| | | | | | - Lingle Wang
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA
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10
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Jiang W. Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics. J Chem Theory Comput 2024; 20:3085-3095. [PMID: 38568961 DOI: 10.1021/acs.jctc.4c00191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the λ-exchange of free energy perturbation (FEP). Two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/λ-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.
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Affiliation(s)
- Wei Jiang
- Computational Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Argonne, Illinois 60439, United States
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11
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Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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12
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Summa CM, Langford DP, Dinshaw SH, Webb J, Rick SW. Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding. J Chem Theory Comput 2024; 20:2812-2819. [PMID: 38538531 DOI: 10.1021/acs.jctc.4c00057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.
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Affiliation(s)
- Christopher M Summa
- Department of Computer Science, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Dillon P Langford
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Sam H Dinshaw
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Jennifer Webb
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Steven W Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
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13
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Lu L, Gou X, Tan SK, Mann SI, Yang H, Zhong X, Gazgalis D, Valdiviezo J, Jo H, Wu Y, Diolaiti ME, Ashworth A, Polizzi NF, DeGrado WF. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 2024; 384:106-112. [PMID: 38574125 DOI: 10.1126/science.adl5364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/28/2024] [Indexed: 04/06/2024]
Abstract
The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.
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Affiliation(s)
- Lei Lu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xuxu Gou
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Sophia K Tan
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Samuel I Mann
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
- Department of Chemistry, University of California, Riverside, CA 92521, USA
| | - Hyunjun Yang
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Xiaofang Zhong
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
| | - Dimitrios Gazgalis
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Jesús Valdiviezo
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - Hyunil Jo
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Yibing Wu
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Morgan E Diolaiti
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
| | - Nicholas F Polizzi
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02215, USA
| | - William F DeGrado
- Department of Pharmaceutical Chemistry & Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
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14
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Carney DW, Leffler AE, Bell JA, Chandrasinghe AS, Cheng C, Chang E, Dornford A, Dougan DR, Frye LL, Grimes ME, Knehans T, Knight JL, Komandla M, Lane W, Li H, Newman SR, Phimister K, Saikatendu KS, Silverstein H, Vafaei S. Exploiting high-energy hydration sites for the discovery of potent peptide aldehyde inhibitors of the SARS-CoV-2 main protease with cellular antiviral activity. Bioorg Med Chem 2024; 103:117577. [PMID: 38518735 DOI: 10.1016/j.bmc.2023.117577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 03/24/2024]
Abstract
Small-molecule antivirals that prevent the replication of the SARS-CoV-2 virus by blocking the enzymatic activity of its main protease (Mpro) are and will be a tenet of pandemic preparedness. However, the peptidic nature of such compounds often precludes the design of compounds within favorable physical property ranges, limiting cellular activity. Here we describe the discovery of peptide aldehyde Mpro inhibitors with potent enzymatic and cellular antiviral activity. This structure-activity relationship (SAR) exploration was guided by the use of calculated hydration site thermodynamic maps (WaterMap) to drive potency via displacement of waters from high-energy sites. Thousands of diverse compounds were designed to target these high-energy hydration sites and then prioritized for synthesis by physics- and structure-based Free-Energy Perturbation (FEP+) simulations, which accurately predicted biochemical potencies. This approach ultimately led to the rapid discovery of lead compounds with unique SAR that exhibited potent enzymatic and cellular activity with excellent pan-coronavirus coverage.
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Affiliation(s)
- Daniel W Carney
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States.
| | - Abba E Leffler
- Schrödinger, Inc, 1540 Broadway, New York, NY 10036, United States.
| | - Jeffrey A Bell
- Schrödinger, Inc, 1540 Broadway, New York, NY 10036, United States
| | | | - Cecilia Cheng
- Schrödinger, Inc, 9868 Scranton Road, Suite 3200, San Diego, CA 92121, United States
| | - Edcon Chang
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Adam Dornford
- Schrödinger, Inc, 1 Main St, 11th Floor, Cambridge, MA 02142, United States
| | - Douglas R Dougan
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Leah L Frye
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | - Mary E Grimes
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | - Tim Knehans
- Schrödinger GmbH, Glücksteinallee 25, 68163 Mannheim, Germany
| | | | - Mallareddy Komandla
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Weston Lane
- Treeline Biosciences, 500 Arsenal Way, Watertown, MA 02472, United States
| | - Hubert Li
- Schrödinger, Inc, 9868 Scranton Road, Suite 3200, San Diego, CA 92121, United States
| | - Sophia R Newman
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | - Katalin Phimister
- Schrödinger Technologies Limited, 1st Floor West, Davidson House, Forbury Square, Reading RG1 3EU, United Kingdom
| | - Kumar S Saikatendu
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Hercules Silverstein
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
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15
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Luo D, Liu D, Qu X, Dong L, Wang B. Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning. J Chem Inf Model 2024; 64:1892-1906. [PMID: 38441880 DOI: 10.1021/acs.jcim.3c01961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.
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Affiliation(s)
- Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Dandan Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Xiaoyang Qu
- School of Pharmacy and Medical Technology, Putian University, Putian 351100, P. R. China
- Key Laboratory of Pharmaceutical Analysis and Laboratory Medicine (Putian University), Fujian Province University, Putian 351100, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China
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16
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Barron MP, Vilseck JZ. A λ-dynamics investigation of insulin Wakayama and other A3 variant binding affinities to the insulin receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585233. [PMID: 38559010 PMCID: PMC10979964 DOI: 10.1101/2024.03.15.585233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Insulin Wakayama is a clinical insulin variant where a conserved valine at the third residue on insulin's A chain (ValA3) is replaced with a leucine (LeuA3), impairing insulin receptor (IR) binding by 140-500 fold. This severe impact on binding from such a subtle modification has posed an intriguing problem for decades. Although experimental investigations of natural and unnatural A3 mutations have highlighted the sensitivity of insulin-IR binding to minor changes at this site, an atomistic explanation of these binding trends has remained elusive. We investigate this problem computationally using λ-dynamics free energy calculations to model structural changes in response to perturbations of the ValA3 side chain and to calculate associated relative changes in binding free energy (ΔΔGbind). The Wakayama LeuA3 mutation and seven other A3 substitutions were studied in this work. The calculated ΔΔGbind results showed high agreement compared to experimental binding potencies with a Pearson correlation of 0.88 and a mean unsigned error of 0.68 kcal/mol. Extensive structural analyses of λ-dynamics trajectories revealed that critical interactions were disrupted between insulin and the insulin receptor as a result of the A3 mutations. This investigation also quantifies the effect that adding an A3 Cδ atom or losing an A3 Cγ atom has on insulin's binding affinity to the IR. Thus, λ-dynamics was able to successfully model the effects of subtle modifications to insulin's A3 side chain on its protein-protein interactions with the IR and shed new light on a decades-old mystery: the exquisite sensitivity of hormone-receptor binding to a subtle modification of an invariant insulin residue.
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Affiliation(s)
- Monica P. Barron
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Jonah Z. Vilseck
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
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17
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Caparotta M, Perez A. Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology. J Phys Chem B 2024; 128:2219-2227. [PMID: 38418288 DOI: 10.1021/acs.jpcb.3c04823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.
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Affiliation(s)
- Marcelo Caparotta
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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18
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Gilleran JA, Ashraf K, Delvillar M, Eck T, Fondekar R, Miller EB, Hutchinson A, Dong A, Seitova A, De Souza ML, Augeri D, Halabelian L, Siekierka J, Rotella DP, Gordon J, Childers WE, Grier MC, Staker BL, Roberge JY, Bhanot P. Structure-Activity Relationship of a Pyrrole Based Series of PfPKG Inhibitors as Anti-Malarials. J Med Chem 2024; 67:3467-3503. [PMID: 38372781 DOI: 10.1021/acs.jmedchem.3c01795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Controlling malaria requires new drugs against Plasmodium falciparum. The P. falciparum cGMP-dependent protein kinase (PfPKG) is a validated target whose inhibitors could block multiple steps of the parasite's life cycle. We defined the structure-activity relationship (SAR) of a pyrrole series for PfPKG inhibition. Key pharmacophores were modified to enable full exploration of chemical diversity and to gain knowledge about an ideal core scaffold. In vitro potency against recombinant PfPKG and human PKG were used to determine compound selectivity for the parasite enzyme. P. berghei sporozoites and P. falciparum asexual blood stages were used to assay multistage antiparasitic activity. Cellular specificity of compounds was evaluated using transgenic parasites expressing PfPKG carrying a substituted "gatekeeper" residue. The structure of PfPKG bound to an inhibitor was solved, and modeling using this structure together with computational tools was utilized to understand SAR and establish a rational strategy for subsequent lead optimization.
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Affiliation(s)
- John A Gilleran
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Kutub Ashraf
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
| | - Melvin Delvillar
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
| | - Tyler Eck
- Department of Chemistry and Biochemistry and Sokol Institute of Pharmaceutical Life Sciences, Montclair State University, Montclair, New Jersey 07043, United States
| | - Raheel Fondekar
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
- Rutgers School of Pharmacy, 160 Frelinghuysen Road, Piscataway, New Jersey 08854, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Ashley Hutchinson
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Aiping Dong
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Alma Seitova
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Mariana Laureano De Souza
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
| | - David Augeri
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Levon Halabelian
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - John Siekierka
- Department of Chemistry and Biochemistry and Sokol Institute of Pharmaceutical Life Sciences, Montclair State University, Montclair, New Jersey 07043, United States
| | - David P Rotella
- Department of Chemistry and Biochemistry and Sokol Institute of Pharmaceutical Life Sciences, Montclair State University, Montclair, New Jersey 07043, United States
| | - John Gordon
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia, Pennsylvania 19140, United States
| | - Wayne E Childers
- Moulder Center for Drug Discovery Research, Temple University School of Pharmacy, Philadelphia, Pennsylvania 19140, United States
| | - Mark C Grier
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Bart L Staker
- Seattle Structural Genomics Center for Infectious Disease, Seattle, Washington 98109, United States
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, Washington 98109, United States
| | - Jacques Y Roberge
- Rutgers Molecular Design and Synthesis Core, Office for Research, Rutgers University, 610 Taylor Road, Piscataway, New Jersey 08854, United States
| | - Purnima Bhanot
- Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers New Jersey Medical School, 225 Warren Street, Newark, New Jersey 07103, United States
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19
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Dodds M, Guo J, Löhr T, Tibo A, Engkvist O, Janet JP. Sample efficient reinforcement learning with active learning for molecular design. Chem Sci 2024; 15:4146-4160. [PMID: 38487235 PMCID: PMC10935729 DOI: 10.1039/d3sc04653b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/07/2024] [Indexed: 03/17/2024] Open
Abstract
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.
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Affiliation(s)
- Michael Dodds
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jeff Guo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Thomas Löhr
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
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20
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Ries B, Alibay I, Swenson DWH, Baumann HM, Henry MM, Eastwood JRB, Gowers RJ. Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations. J Chem Theory Comput 2024; 20:1862-1877. [PMID: 38330251 PMCID: PMC10941767 DOI: 10.1021/acs.jctc.3c01206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.
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Affiliation(s)
- Benjamin Ries
- Medicinal
Chemistry, Boehringer Ingelheim Pharma GmbH
& Co KG, Birkendorfer Str 65, 88397 Biberach an der Riss, Germany
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Irfan Alibay
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - David W. H. Swenson
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Hannah M. Baumann
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Michael M. Henry
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
- Computational
and Systems Biology Program, Sloan Kettering
Institute, Memorial Sloan Kettering Cancer Center, New York, 1275 New York, United States
| | - James R. B. Eastwood
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
| | - Richard J. Gowers
- Open
Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States
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21
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Sabanés Zariquiey F, Galvelis R, Gallicchio E, Chodera JD, Markland TE, De Fabritiis G. Enhancing Protein-Ligand Binding Affinity Predictions Using Neural Network Potentials. J Chem Inf Model 2024; 64:1481-1485. [PMID: 38376463 DOI: 10.1021/acs.jcim.3c02031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies with the Alchemical Transfer Method and validate its performance against established benchmarks and find significant enhancements compared with conventional MM force fields like GAFF2.
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Affiliation(s)
- Francesc Sabanés Zariquiey
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Raimondas Galvelis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
| | - Emilio Gallicchio
- Department of Chemistry, Graduate Center, Brooklyn College, City University of New York, New York, New York 11210, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, 337 Campus Drive, Stanford, California 94305, United States
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
- Acellera Labs, C Dr Trueta 183, 08005 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
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22
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Atkinson SJ, Bagal SK, Argyrou A, Askin S, Cheung T, Chiarparin E, Coen M, Collie IT, Dale IL, De Fusco C, Dillman K, Evans L, Feron LJ, Foster AJ, Grondine M, Kantae V, Lamont GM, Lamont S, Lynch JT, Nilsson Lill S, Robb GR, Saeh J, Schimpl M, Scott JS, Smith J, Srinivasan B, Tentarelli S, Vazquez-Chantada M, Wagner D, Walsh JJ, Watson D, Williamson B. Development of a Series of Pyrrolopyridone MAT2A Inhibitors. J Med Chem 2024. [PMID: 38466661 DOI: 10.1021/acs.jmedchem.3c01860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The optimization of an allosteric fragment, discovered by differential scanning fluorimetry, to an in vivo MAT2a tool inhibitor is discussed. The structure-based drug discovery approach, aided by relative binding free energy calculations, resulted in AZ'9567 (21), a potent inhibitor in vitro with excellent preclinical pharmacokinetic properties. This tool showed a selective antiproliferative effect on methylthioadenosine phosphorylase (MTAP) KO cells, both in vitro and in vivo, providing further evidence to support the utility of MAT2a inhibitors as potential anticancer therapies for MTAP-deficient tumors.
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Affiliation(s)
- Stephen J Atkinson
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Sharan K Bagal
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Argyrides Argyrou
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Sean Askin
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Tony Cheung
- Oncology R&D, AstraZeneca, R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Elisabetta Chiarparin
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Muireann Coen
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Iain T Collie
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Ian L Dale
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Claudia De Fusco
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Keith Dillman
- Oncology R&D, AstraZeneca, R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Laura Evans
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Lyman J Feron
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Alison J Foster
- Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Michael Grondine
- Oncology R&D, AstraZeneca, R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Vasudev Kantae
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Gillian M Lamont
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Scott Lamont
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - James T Lynch
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Sten Nilsson Lill
- Data Sciences & Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg 431 83, Sweden
| | - Graeme R Robb
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Jamal Saeh
- Oncology R&D, AstraZeneca, R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Marianne Schimpl
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - James S Scott
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - James Smith
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Bharath Srinivasan
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Sharon Tentarelli
- Oncology R&D, AstraZeneca, R&D Boston, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States
| | - Mercedes Vazquez-Chantada
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - David Wagner
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Jarrod J Walsh
- Discovery Sciences R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - David Watson
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
| | - Beth Williamson
- Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge Biomedical Campus, 1 Francis Crick Avenue, Cambridge CB2 0AA, U.K
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23
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Yamagishi M, Kuze Y, Kobayashi S, Nakashima M, Morishima S, Kawamata T, Makiyama J, Suzuki K, Seki M, Abe K, Imamura K, Watanabe E, Tsuchiya K, Yasumatsu I, Takayama G, Hizukuri Y, Ito K, Taira Y, Nannya Y, Tojo A, Watanabe T, Tsutsumi S, Suzuki Y, Uchimaru K. Mechanisms of action and resistance in histone methylation-targeted therapy. Nature 2024; 627:221-228. [PMID: 38383791 PMCID: PMC10917674 DOI: 10.1038/s41586-024-07103-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/23/2024] [Indexed: 02/23/2024]
Abstract
Epigenomes enable the rectification of disordered cancer gene expression, thereby providing new targets for pharmacological interventions. The clinical utility of targeting histone H3 lysine trimethylation (H3K27me3) as an epigenetic hallmark has been demonstrated1-7. However, in actual therapeutic settings, the mechanism by which H3K27me3-targeting therapies exert their effects and the response of tumour cells remain unclear. Here we show the potency and mechanisms of action and resistance of the EZH1-EZH2 dual inhibitor valemetostat in clinical trials of patients with adult T cell leukaemia/lymphoma. Administration of valemetostat reduced tumour size and demonstrated durable clinical response in aggressive lymphomas with multiple genetic mutations. Integrative single-cell analyses showed that valemetostat abolishes the highly condensed chromatin structure formed by the plastic H3K27me3 and neutralizes multiple gene loci, including tumour suppressor genes. Nevertheless, subsequent long-term treatment encounters the emergence of resistant clones with reconstructed aggregate chromatin that closely resemble the pre-dose state. Acquired mutations at the PRC2-compound interface result in the propagation of clones with increased H3K27me3 expression. In patients free of PRC2 mutations, TET2 mutation or elevated DNMT3A expression causes similar chromatin recondensation through de novo DNA methylation in the H3K27me3-associated regions. We identified subpopulations with distinct metabolic and gene translation characteristics implicated in primary susceptibility until the acquisition of the heritable (epi)mutations. Targeting epigenetic drivers and chromatin homeostasis may provide opportunities for further sustained epigenetic cancer therapies.
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Affiliation(s)
- Makoto Yamagishi
- Laboratory of Viral Oncology and Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
- Laboratory of Tumor Cell Biology, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
| | - Yuta Kuze
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Seiichiro Kobayashi
- Division of Hematopoietic Disease Control, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Hematology, Kanto Rosai Hospital, Kanagawa, Japan
| | - Makoto Nakashima
- Laboratory of Tumor Cell Biology, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Satoko Morishima
- Division of Endocrinology, Diabetes and Metabolism, Hematology and Rheumatology, Second Department of Internal Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
| | - Toyotaka Kawamata
- Department of Hematology/Oncology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Junya Makiyama
- Department of Hematology/Oncology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Hematology, Sasebo City General Hospital, Nagasaki, Japan
| | - Kako Suzuki
- Laboratory of Viral Oncology and Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory of Tumor Cell Biology, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Masahide Seki
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Kazumi Abe
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Kiyomi Imamura
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Eri Watanabe
- IMSUT Clinical Flow Cytometry Laboratory, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kazumi Tsuchiya
- IMSUT Clinical Flow Cytometry Laboratory, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Isao Yasumatsu
- Organic and Biomolecular Chemistry Department, Daiichi Sankyo RD Novare, Tokyo, Japan
| | | | | | - Kazumi Ito
- Translational Science I, Daiichi Sankyo, Tokyo, Japan
| | - Yukihiro Taira
- Laboratory of Viral Oncology and Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yasuhito Nannya
- Division of Hematopoietic Disease Control, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Hematology/Oncology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Arinobu Tojo
- Tokyo Medical and Dental University, Tokyo, Japan
| | - Toshiki Watanabe
- Department of Practical Management of Medical Information, Graduate School of Medicine, St Marianna University, Kanagawa, Japan
| | | | - Yutaka Suzuki
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
| | - Kaoru Uchimaru
- Laboratory of Tumor Cell Biology, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
- Department of Hematology/Oncology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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24
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Dehghani-Ghahnaviyeh S, Soylu C, Furet P, Velez-Vega C. Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα. J Phys Chem B 2024; 128:1819-1829. [PMID: 38373112 DOI: 10.1021/acs.jpcb.3c06766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.
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Affiliation(s)
- Sepehr Dehghani-Ghahnaviyeh
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Cihan Soylu
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Pascal Furet
- Novartis Institutes for BioMedical Research, CH4002 Basel, Switzerland
| | - Camilo Velez-Vega
- Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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25
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Kaufman B, Williams EC, Underkoffler C, Pederson R, Mardirossian N, Watson I, Parkhill J. COATI: Multimodal Contrastive Pretraining for Representing and Traversing Chemical Space. J Chem Inf Model 2024; 64:1145-1157. [PMID: 38316665 DOI: 10.1021/acs.jcim.3c01753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Creating a successful small molecule drug is a challenging multiparameter optimization problem in an effectively infinite space of possible molecules. Generative models have emerged as powerful tools for traversing data manifolds composed of images, sounds, and text and offer an opportunity to dramatically improve the drug discovery and design process. To create generative optimization methods that are more useful than brute-force molecular generation and filtering via virtual screening, we propose that four integrated features are necessary: large, quantitative data sets of molecular structure and activity, an invertible vector representation of realistic accessible molecules, smooth and differentiable regressors that quantify uncertainty, and algorithms to simultaneously optimize properties of interest. Over the course of 12 months, Terray Therapeutics has collected a data set of 2 billion quantitative binding measurements of small molecules to therapeutic targets, which directly motivates multiparameter generative optimization of molecules conditioned on these data. To this end, we present contrastive optimization for accelerated therapeutic inference (COATI), a pretrained, multimodal encoder-decoder model of druglike chemical space. COATI is constructed without any human biasing of features, using contrastive learning from text and 3D representations of molecules to allow for downstream use with structural models. We demonstrate that COATI possesses many of the desired properties of universal molecular embedding: fixed-dimension, invertibility, autoencoding, accurate regression, and low computation cost. Finally, we present a novel metadynamics algorithm for generative optimization using a small subset of our proprietary data collected for a model protein, carbonic anhydrase, designing molecules that satisfy the multiparameter optimization task of potency, solubility, and drug likeness. This work sets the stage for fully integrated generative molecular design and optimization for small molecules.
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Affiliation(s)
- Benjamin Kaufman
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Edward C Williams
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Carl Underkoffler
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Ryan Pederson
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Narbe Mardirossian
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - Ian Watson
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
| | - John Parkhill
- Terray Therapeutics, Inc., 800 Royal Oaks Dr, Monrovia, California 91016, United States
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26
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Dong J, Wang S, Cui W, Sun X, Guo H, Yan H, Vogel H, Wang Z, Yuan S. Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction. J Chem Theory Comput 2024. [PMID: 38394691 DOI: 10.1021/acs.jctc.3c01412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s-1) is in close agreement with surface plasmon resonance experiments (0.12 s-1), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains.
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Affiliation(s)
- Junlin Dong
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiyu Wang
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- AlphaMol Science Ltd, Shenzhen 518055, China
| | - Wenqiang Cui
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolin Sun
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Haojie Guo
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Hailu Yan
- School of Biological Sciences, College of Science and Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Horst Vogel
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhi Wang
- Artificial Intelligence Department, Zhejiang Financial College, Hangzhou 310018, China
| | - Shuguang Yuan
- Research Center for Computer-Aided Drug Discovery, Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- AlphaMol Science Ltd, Shenzhen 518055, China
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27
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Bansal N, Wang Y, Sciabola S. Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations. Molecules 2024; 29:830. [PMID: 38398581 PMCID: PMC10893267 DOI: 10.3390/molecules29040830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/24/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have gained traction for protein-ligand binding affinity predictions in early drug discovery programs. In this article, we perform retrospective binding free energy evaluations for 172 compounds from our internal collection spread over four different protein targets and five congeneric ligand series. We compared multiple state-of-the-art free energy methods ranging from physics-based methods with different levels of complexity and conformational sampling to state-of-the-art machine-learning-based methods that were available to us. Overall, we found that physics-based methods behaved particularly well when the ligand perturbations were made in the solvation region, and they did not perform as well when accounting for large conformational changes in protein active sites. On the other end, machine-learning-based methods offer a good cost-effective alternative for binding free energy calculations, but the accuracy of their predictions is highly dependent on the experimental data available for training the model.
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Affiliation(s)
- Nupur Bansal
- Biotherapeutic and Medicinal Sciences, Biogen, 225 Binney Street, Cambridge, MA 02142, USA; (Y.W.); (S.S.)
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28
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Khuttan S, Gallicchio E. What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling. J Chem Theory Comput 2024; 20:1489-1501. [PMID: 38252868 PMCID: PMC10867849 DOI: 10.1021/acs.jctc.3c01250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 01/24/2024]
Abstract
We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.
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Affiliation(s)
- Sheenam Khuttan
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
| | - Emilio Gallicchio
- Department
of Chemistry and Biochemistry, Brooklyn
College of the City University of New York, New York, New York 11210, United States
- Ph.D.
Program in Biochemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
- Ph.D.
Program in Chemistry, The Graduate Center
of the City University of New York, New York, New York 10016, United States
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29
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Hu R, Zhang J, Kang Y, Wang Z, Pan P, Deng Y, Hsieh CY, Hou T. Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization. J Chem Theory Comput 2024; 20:1465-1478. [PMID: 38300792 DOI: 10.1021/acs.jctc.3c01154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.
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Affiliation(s)
- Renling Hu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Polytechnic Institute, Zhejiang University, Hangzhou 310058, Zhejiang, China
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd., Hangzhou 310018, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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30
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Huang P, Åbacka H, Wilson CJ, Wind ML, Rűtzler M, Hagström-Andersson A, Gourdon P, de Groot BL, Venskutonytė R, Lindkvist-Petersson K. Molecular basis for human aquaporin inhibition. Proc Natl Acad Sci U S A 2024; 121:e2319682121. [PMID: 38319972 PMCID: PMC10873552 DOI: 10.1073/pnas.2319682121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/04/2024] [Indexed: 02/08/2024] Open
Abstract
Cancer invasion and metastasis are known to be potentiated by the expression of aquaporins (AQPs). Likewise, the expression levels of AQPs have been shown to be prognostic for survival in patients and have a role in tumor growth, edema, angiogenesis, and tumor cell migration. Thus, AQPs are key players in cancer biology and potential targets for drug development. Here, we present the single-particle cryo-EM structure of human AQP7 at 3.2-Å resolution in complex with the specific inhibitor compound Z433927330. The structure in combination with MD simulations shows that the inhibitor binds to the endofacial side of AQP7. In addition, cancer cells treated with Z433927330 show reduced proliferation. The data presented here serve as a framework for the development of AQP inhibitors.
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Affiliation(s)
- Peng Huang
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
| | - Hannah Åbacka
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
| | - Carter J. Wilson
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, 37077Gottingen, Germany
| | - Malene Lykke Wind
- Department of Biomedical Sciences, Copenhagen University, DK-2200Copenhagen N, Denmark
| | - Michael Rűtzler
- ApoGlyx, Lund22381, Sweden
- Division of Biochemistry and Structural Biology, Department of Chemistry, Lund University, Lund22100, Sweden
| | - Anna Hagström-Andersson
- Department of Laboratory Medicine, Division of Clinical Genetics, Lund University, Lund22184, Sweden
| | - Pontus Gourdon
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Department of Biomedical Sciences, Copenhagen University, DK-2200Copenhagen N, Denmark
| | - Bert L. de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, 37077Gottingen, Germany
| | - Raminta Venskutonytė
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Lund Institute of Advanced Neutron and X-Ray Science, Lund22370, Sweden
| | - Karin Lindkvist-Petersson
- Department of Experimental Medical Science, Lund University, Lund22184, Sweden
- Lund Institute of Advanced Neutron and X-Ray Science, Lund22370, Sweden
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31
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Gartan P, Khorsand F, Mizar P, Vahokovski JI, Cervantes LF, Haug BE, Brenk R, Brooks CL, Reuter N. Investigating Polypharmacology through Targeting Known Human Neutrophil Elastase Inhibitors to Proteinase 3. J Chem Inf Model 2024; 64:621-626. [PMID: 38276895 PMCID: PMC10865350 DOI: 10.1021/acs.jcim.3c01949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
Using a combination of multisite λ-dynamics (MSλD) together with in vitro IC50 assays, we evaluated the polypharmacological potential of a scaffold currently in clinical trials for inhibition of human neutrophil elastase (HNE), targeting cardiopulmonary disease, for efficacious inhibition of Proteinase 3 (PR3), a related neutrophil serine proteinase. The affinities we observe suggest that the dihydropyrimidinone scaffold can serve as a suitable starting point for the establishment of polypharmacologically targeting both enzymes and enhancing the potential for treatments addressing diseases like chronic obstructive pulmonary disease.
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Affiliation(s)
- Parveen Gartan
- Department
of Chemistry, University of Bergen, Bergen 5020, Norway
- Computational
Biology Unit, University of Bergen, Bergen 5020, Norway
| | - Fahimeh Khorsand
- Department
of Biomedicine, University of Bergen, Bergen 5020, Norway
| | - Pushpak Mizar
- Department
of Chemistry, University of Bergen, Bergen 5020, Norway
| | - Juha Ilmari Vahokovski
- Core
Facility for Biophysics, Structural Biology, and Screening, Department
of Biomedicine, University of Bergen, Bergen 5020, Norway
| | - Luis F. Cervantes
- Department
of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Bengt Erik Haug
- Department
of Chemistry, University of Bergen, Bergen 5020, Norway
- Centre for
Pharmacy, University of Bergen, Bergen 5020, Norway
| | - Ruth Brenk
- Department
of Biomedicine, University of Bergen, Bergen 5020, Norway
| | - Charles L. Brooks
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Biophysics
Program, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Nathalie Reuter
- Department
of Chemistry, University of Bergen, Bergen 5020, Norway
- Computational
Biology Unit, University of Bergen, Bergen 5020, Norway
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32
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Dawson JRD, Wadman GM, Zhang P, Tebben A, Carter PH, Gu S, Shroka T, Borrega-Roman L, Salanga CL, Handel TM, Kufareva I. Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.15.567150. [PMID: 38014122 PMCID: PMC10680698 DOI: 10.1101/2023.11.15.567150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.
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Affiliation(s)
- John R D Dawson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Grant M Wadman
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | | | | | - Percy H Carter
- Bristol Myers Squibb Company, Princeton, NJ, USA
- (current affiliation) Blueprint Medicines, Cambridge, MA, USA
| | - Siyi Gu
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- (current affiliation) Lycia Therapeutics, South San Francisco, CA
| | - Thomas Shroka
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- (current affiliation) Avidity Biosciences Inc., San Diego, CA
| | - Leire Borrega-Roman
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Catherina L Salanga
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Tracy M Handel
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Irina Kufareva
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
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33
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Georgiou K, Konstantinidi A, Hutterer J, Freudenberger K, Kolarov F, Lambrinidis G, Stylianakis I, Stampelou M, Gauglitz G, Kolocouris A. Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers. BIOCHIMICA ET BIOPHYSICA ACTA. BIOMEMBRANES 2024; 1866:184258. [PMID: 37995846 DOI: 10.1016/j.bbamem.2023.184258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/18/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Experimental binding free energies of 27 adamantyl amines against the influenza M2(22-46) WT tetramer, in its closed form at pH 8, were measured by ITC in DPC micelles. The measured Kd's range is ~44 while the antiviral potencies (IC50) range is ~750 with a good correlation between binding free energies computed with Kd and IC50 values (r = 0.76). We explored with MD simulations (ff19sb, CHARMM36m) the binding profile of complexes with strong, moderate and weak binders embedded in DMPC, DPPC, POPC or a viral mimetic membrane and using different experimental starting structures of M2. To predict accurately differences in binding free energy in response to subtle changes in the structure of the ligands, we performed 18 alchemical perturbative single topology FEP/MD NPT simulations (OPLS2005) using the BAR estimator (Desmond software) and 20 dual topology calculations TI/MD NVT simulations (ff19sb) using the MBAR estimator (Amber software) for adamantyl amines in complex with M2(22-46) WT in DMPC, DPPC, POPC. We observed that both methods with all lipids show a very good correlation between the experimental and calculated relative binding free energies (r = 0.77-0.87, mue = 0.36-0.92 kcal mol-1) with the highest performance achieved with TI/MBAR and lowest performance with FEP/BAR in DMPC bilayers. When antiviral potencies are used instead of the Kd values for computing the experimental binding free energies we obtained also good performance with both FEP/BAR (r = 0.83, mue = 0.75 kcal mol-1) and TI/MBAR (r = 0.69, mue = 0.77 kcal mol-1).
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Affiliation(s)
- Kyriakos Georgiou
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Athina Konstantinidi
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Johanna Hutterer
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany
| | - Kathrin Freudenberger
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany
| | - Felix Kolarov
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany; Roche, Penzberg, Bavaria, Germany
| | - George Lambrinidis
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Ioannis Stylianakis
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Margarita Stampelou
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Günter Gauglitz
- Institut für Physikalische und Theoretische Chemie, Eberhard-Karls-Universität, D-72076 Tübingen, Germany
| | - Antonios Kolocouris
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens (NKUA), Panepistimiopolis-Zografou, 15771 Athens, Greece.
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34
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Stampelou M, Ladds G, Kolocouris A. Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A 3 Receptor. J Phys Chem B 2024; 128:914-936. [PMID: 38236582 DOI: 10.1021/acs.jpcb.3c05986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.
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Affiliation(s)
- Margarita Stampelou
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
| | - Graham Ladds
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge CB2 1PD, U.K
| | - Antonios Kolocouris
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771 Athens, Greece
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35
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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36
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Brooks CL, MacKerell AD, Post CB, Nilsson L. Biomolecular dynamics in the 21st century. Biochim Biophys Acta Gen Subj 2024; 1868:130534. [PMID: 38065235 PMCID: PMC10842176 DOI: 10.1016/j.bbagen.2023.130534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/03/2024]
Abstract
The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.
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Affiliation(s)
- Charles L Brooks
- University of Michigan, Department of Chemistry, Ann Arbor, MI 48109, USA.
| | | | - Carol B Post
- Purdue University, Department of Medicinal Chemistry and Molecular Pharmacology, West Lafayette, IN 47907-2091, USA.
| | - Lennart Nilsson
- Karolinska Institutet, Department of Biosciences and Nutrition, SE-14183 Huddinge, Sweden.
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37
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Bothe U, Günther J, Nubbemeyer R, Siebeneicher H, Ring S, Bömer U, Peters M, Rausch A, Denner K, Himmel H, Sutter A, Terebesi I, Lange M, Wengner AM, Guimond N, Thaler T, Platzek J, Eberspächer U, Schäfer M, Steuber H, Zollner TM, Steinmeyer A, Schmidt N. Discovery of IRAK4 Inhibitors BAY1834845 (Zabedosertib) and BAY1830839. J Med Chem 2024; 67:1225-1242. [PMID: 38228402 PMCID: PMC10823478 DOI: 10.1021/acs.jmedchem.3c01714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 01/18/2024]
Abstract
Interleukin-1 receptor-associated kinase 4 (IRAK4) plays a critical role in innate inflammatory processes. Here, we describe the discovery of two clinical candidate IRAK4 inhibitors, BAY1834845 (zabedosertib) and BAY1830839, starting from a high-throughput screening hit derived from Bayer's compound library. By exploiting binding site features distinct to IRAK4 using an in-house docking model, liabilities of the original hit could surprisingly be overcome to confer both candidates with a unique combination of good potency and selectivity. Favorable DMPK profiles and activity in animal inflammation models led to the selection of these two compounds for clinical development in patients.
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Affiliation(s)
- Ulrich Bothe
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Judith Günther
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | | | | | - Sven Ring
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | | | - Michaele Peters
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | | | - Karsten Denner
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Herbert Himmel
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Andreas Sutter
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Ildiko Terebesi
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | | | - Antje M. Wengner
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Nicolas Guimond
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Tobias Thaler
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Johannes Platzek
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Uwe Eberspächer
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | | | | | - Thomas M. Zollner
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Andreas Steinmeyer
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
| | - Nicole Schmidt
- Bayer AG, Research &
Development, Pharmaceuticals, 13353 Berlin, Germany
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38
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Xue B, Yang Q, Zhang Q, Wan X, Fang D, Lin X, Sun G, Gobbo G, Cao F, Mathiowetz AM, Burke BJ, Kumpf RA, Rai BK, Wood GPF, Pickard FC, Wang J, Zhang P, Ma J, Jiang YA, Wen S, Hou X, Zou J, Yang M. Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation. J Chem Theory Comput 2024; 20:799-818. [PMID: 38157475 DOI: 10.1021/acs.jctc.3c00920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.
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Affiliation(s)
- Bai Xue
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Qingyi Yang
- Medicine Design, Pfizer Inc., 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Qiaochu Zhang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Xiao Wan
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Dong Fang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Xiaolu Lin
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Guangxu Sun
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Gianpaolo Gobbo
- XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
| | - Fenglei Cao
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Alan M Mathiowetz
- Medicine Design, Pfizer Inc., 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Benjamin J Burke
- Medicine Design, Pfizer Inc., 10777 Science Center Drive, San Diego, California 92121, United States
| | - Robert A Kumpf
- Medicine Design, Pfizer Inc., 10777 Science Center Drive, San Diego, California 92121, United States
| | - Brajesh K Rai
- Machine Learning and Computational Sciences, Pfizer Inc., 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Geoffrey P F Wood
- Pharmaceutical Science Small Molecule, Pfizer Inc., Eastern Point Road, Groton, Connecticut 06340, United States
| | - Frank C Pickard
- Pharmaceutical Science Small Molecule, Pfizer Inc., Eastern Point Road, Groton, Connecticut 06340, United States
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Peiyu Zhang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Jian Ma
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Yide Alan Jiang
- XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
| | - Shuhao Wen
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Xinjun Hou
- Medicine Design, Pfizer Inc., 1 Portland Street, Cambridge, Massachusetts 02139, United States
| | - Junjie Zou
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
| | - Mingjun Yang
- Shenzhen Jingtai Technology Co., Ltd. (XtalPi), Floor 3, Sf Industrial Plant, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen 518045, China
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39
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Smith M, Knight IS, Kormos RC, Pepe JG, Kunach P, Diamond MI, Shahmoradian SH, Irwin JJ, DeGrado WF, Shoichet BK. Docking for Molecules That Bind in a Symmetric Stack with SymDOCK. J Chem Inf Model 2024; 64:425-434. [PMID: 38191997 PMCID: PMC10806807 DOI: 10.1021/acs.jcim.3c01749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/10/2024]
Abstract
Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of great current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures, the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural-network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study, SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.
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Affiliation(s)
- Matthew
S. Smith
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Program
in Biophysics, University of California, UCSF Genentech Hall MC2240, 600
16th St Rm N474D,San Francisco, California 94143, United States
| | - Ian S. Knight
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
| | - Rian C. Kormos
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Program
in Biophysics, University of California, UCSF Genentech Hall MC2240, 600
16th St Rm N474D,San Francisco, California 94143, United States
| | - Joseph G. Pepe
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Program
in Biophysics, University of California, UCSF Genentech Hall MC2240, 600
16th St Rm N474D,San Francisco, California 94143, United States
| | - Peter Kunach
- McGill
Research Centre for Studies in Aging, McGill
University, 6875 Boulevard LaSalle, Montreal, Quebec H4H 1R3, Canada
- Department
of Neurology and Neurosurgery, McGill University, 1033 Pine Avenue West, Room 310, Montreal, Quebec H3A 1A1, Canada
- Center
for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell
Jr. Brain Institute, University of Texas
Southwestern Medical Center, 6124 Harry Hines Blvd. Suite NS03.200, Dallas, Texas 75390, United States
- Department
of Neurology, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., G2.222, Dallas, Texas 75390-9368, United States
- Department
of Neuroscience, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390-9111, United States
| | - Marc I. Diamond
- Center
for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell
Jr. Brain Institute, University of Texas
Southwestern Medical Center, 6124 Harry Hines Blvd. Suite NS03.200, Dallas, Texas 75390, United States
- Department
of Neurology, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., G2.222, Dallas, Texas 75390-9368, United States
- Department
of Neuroscience, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390-9111, United States
| | - Sarah H. Shahmoradian
- Center
for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell
Jr. Brain Institute, University of Texas
Southwestern Medical Center, 6124 Harry Hines Blvd. Suite NS03.200, Dallas, Texas 75390, United States
- Department
of Biophysics, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390-8816, United States
| | - John J. Irwin
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
| | - William F. DeGrado
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Cardiovascular
Research Institute, University of California, 555 Mission Bay Blvd South, PO Box 589001, San Francisco, California 94158-9001, United
States
| | - Brian K. Shoichet
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
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40
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Hayes RL, Nixon CF, Marqusee S, Brooks CL. Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design. Proc Natl Acad Sci U S A 2024; 121:e2312029121. [PMID: 38194446 PMCID: PMC10801872 DOI: 10.1073/pnas.2312029121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/22/2023] [Indexed: 01/11/2024] Open
Abstract
Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.
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Affiliation(s)
- Ryan L. Hayes
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA92697
- Department of Chemistry, University of Michigan, Ann Arbor, MI48109
| | - Charlotte F. Nixon
- Department of Molecular and Cell Biology, University of California, Berkeley, CA94720
| | - Susan Marqusee
- Department of Molecular and Cell Biology, University of California, Berkeley, CA94720
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA94720
- Department of Chemistry, University of California, Berkeley, CA94720
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, MI48109
- Biophysics Program, University of Michigan, Ann Arbor, MI48109
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41
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Kushwaha R, Rai R, Gawande V, Singh V, Yadav AK, Koch B, Dhar P, Banerjee S. Antibacterial Photodynamic Therapy by Zn(II)-Curcumin Complex: Synthesis, Characterization, DFT Calculation, Antibacterial Activity, and Molecular Docking. Chembiochem 2024; 25:e202300652. [PMID: 37921481 DOI: 10.1002/cbic.202300652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/28/2023] [Accepted: 11/02/2023] [Indexed: 11/04/2023]
Abstract
The increase in antibacterial drug resistance is threatening global health conditions. Recently, antibacterial photodynamic therapy (aPDT) has emerged as an effective antibacterial treatment with high cure gain. In this work, three Zn(II) complexes viz., [Zn(en)(acac)Cl] (1), [Zn(bpy)(acac)Cl] (2), [Zn(en)(cur)Cl] (3), where en=ethylenediamine (1 and 3), bpy=2,2'-bipyridine (2), acac=acetylacetonate (1 and 2), cur=curcumin monoanionic (3) were developed as aPDT agents. Complexes 1-3 were synthesized and fully characterized using NMR, HRMS, FTIR, UV-Vis. and fluorescence spectroscopy. The HOMO-LUMO energy gap (Eg), and adiabatic splittings (ΔS1-T1 and ΔS0-T1 ) obtained from DFT calculation indicated the photosensivity of the complexes. These complexes have not shown any potent antibacterial activity under dark conditions but the antibacterial activity of these complexes was significantly enhanced upon light exposure (MIC value up to 0.025 μg/mL) due to their light-mediated 1 O2 generation abilities. The molecular docking study suggested that complexes 1-3 interact efficiently with DNA gyrase B (PDB ID: 4uro). Importantly, 1-3 did not show any toxicity toward normal HEK-293 cells. Overall, in this work, we have demonstrated the promising potential of Zn(II) complexes as effective antibacterial agents under the influence of visible light.
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Affiliation(s)
- Rajesh Kushwaha
- Department of Chemistry, Indian Institute of Technology (BHU), 221005, Varanasi, Uttar Pradesh, India
| | - Rohit Rai
- School of Biochemical Engineering, Indian Institute of Technology (BHU), 221005, Varanasi, Uttar Pradesh, India
| | - Vedant Gawande
- Department of Chemistry, Indian Institute of Technology (BHU), 221005, Varanasi, Uttar Pradesh, India
| | - Virendra Singh
- Department of Zoology, Institution of Science, Banaras Hindu University, 221005, Varanasi, Uttar Pradesh, India
| | - Ashish Kumar Yadav
- Department of Chemistry, Indian Institute of Technology (BHU), 221005, Varanasi, Uttar Pradesh, India
| | - Biplob Koch
- Department of Zoology, Institution of Science, Banaras Hindu University, 221005, Varanasi, Uttar Pradesh, India
| | - Prodyut Dhar
- School of Biochemical Engineering, Indian Institute of Technology (BHU), 221005, Varanasi, Uttar Pradesh, India
| | - Samya Banerjee
- Department of Chemistry, Indian Institute of Technology (BHU), 221005, Varanasi, Uttar Pradesh, India
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42
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Feng D, Liu B, Chen Z, Xu J, Geng M, Duan W, Ai J, Zhang H. Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation. J Biomol Struct Dyn 2024:1-13. [PMID: 38198294 DOI: 10.1080/07391102.2024.2301754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/30/2023] [Indexed: 01/12/2024]
Abstract
Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a combination of machine learning (ML)-based virtual screening and free energy perturbation (FEP) calculations to identify novel HPK1 inhibitors. ML-based screening yielded 10 potent HPK1 inhibitors (IC50 < 1 μM). The FEP-guided modification of the in-house false-positive hit, DW21302, revealed that a single key atom change could trigger activity cliffs. The resulting DW21302-A was a potent HPK1 inhibitor (IC50 = 2.1 nM) and potently inhibited cellular HPK1 signaling and enhanced T-cell function. Molecular dynamics (MD) simulations and ADME predictions confirmed DW21302-A as candidate compound. This study provides new strategies and chemical scaffolds for HPK1 inhibitor development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Dazhi Feng
- Department of Medicinal Chemistry, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, China
| | - Bo Liu
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
| | - Zhiwei Chen
- Department of Medicinal Chemistry, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jinyi Xu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing, China
| | - Meiyu Geng
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Shandong Laboratory of Yantai Drug Discovery, Bohai Rim Advanced Research Institute for Drug Discovery, Yantai, Shandong, China
| | - Wenhu Duan
- Department of Medicinal Chemistry, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Shandong Laboratory of Yantai Drug Discovery, Bohai Rim Advanced Research Institute for Drug Discovery, Yantai, Shandong, China
| | - Jing Ai
- Division of Antitumor Pharmacology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hefeng Zhang
- Department of Medicinal Chemistry, Shanghai Institute of Materia Medica (SIMM), Chinese Academy of Sciences, Shanghai, China
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43
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Gelžinytė E, Öeren M, Segall MD, Csányi G. Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. J Chem Theory Comput 2024; 20:164-177. [PMID: 38108269 PMCID: PMC10782450 DOI: 10.1021/acs.jctc.3c00710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.
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Affiliation(s)
- Elena Gelžinytė
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
| | - Mario Öeren
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Matthew D. Segall
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
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44
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Coskun D, Lihan M, Rodrigues JPGLM, Vass M, Robinson D, Friesner RA, Miller EB. Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation. J Chem Theory Comput 2024; 20:477-489. [PMID: 38100422 DOI: 10.1021/acs.jctc.3c00839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Free energy perturbation (FEP) remains an indispensable method for computationally assaying prospective compounds in advance of synthesis. However, before FEP can be deployed prospectively, it must demonstrate retrospective recapitulation of known experimental data where the subtle details of the atomic ligand-receptor model are consequential. An open question is whether AlphaFold models can serve as useful initial models for FEP in the regime where there exists a congeneric series of known chemical matter but where no experimental structures are available either of the target or of close homologues. As AlphaFold structures are provided without a bound ligand, we employ induced fit docking to refine the AlphaFold models in the presence of one or more congeneric ligands. In this work, we first validate the performance of our latest induced fit docking technology, IFD-MD, on a retrospective set of public experimental GPCR structures with 95% of cross-docks producing a pose with a ligand RMSD ≤ 2.5 Å in the top two predictions. We then apply IFD-MD and FEP on AlphaFold models of the somatostatin receptor family of GPCRs. We use AlphaFold models produced prior to the availability of any experimental structure from this family. We arrive at FEP-validated models for SSTR2, SSTR4, and SSTR5, with RMSE around 1 kcal/mol, and explore the challenges of model validation under scenarios of limited ligand data, ample ligand data, and categorical data.
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Affiliation(s)
- Dilek Coskun
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Muyun Lihan
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | | | - Márton Vass
- Schrödinger Technologies Limited, Davidson House, First Floor, Reading RG1 3 EU, U.K
| | - Daniel Robinson
- Schrödinger Technologies Limited, Davidson House, First Floor, Reading RG1 3 EU, U.K
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York 10036, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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45
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Chen L, Wu Y, Wu C, Silveira A, Sherman W, Xu H, Gallicchio E. Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands. J Chem Inf Model 2024; 64:250-264. [PMID: 38147877 DOI: 10.1021/acs.jcim.3c01705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.
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Affiliation(s)
- Lieyang Chen
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Yujie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Chuanjie Wu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
| | - Ana Silveira
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States
| | - Huafeng Xu
- Roivant Sciences, 151 W 42nd Street, 15th Floor, New York, New York 10036, United States
- Atommap Corporation, New York, New York 10017, United States
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, New York, New York 11210, United States
- Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
- Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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46
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Yu S, Zhang Y, Yang J, Xu H, Lan S, Zhao B, Luo M, Ma X, Zhang H, Wang S, Shen H, Zhang Y, Xu Y, Li R. Discovery of (R)-4-(8-methoxy-2-methyl-1-(1-phenylethy)-1H-imidazo[4,5-c]quinnolin-7-yl)-3,5-dimethylisoxazole as a potent and selective BET inhibitor for treatment of acute myeloid leukemia (AML) guided by FEP calculation. Eur J Med Chem 2024; 263:115924. [PMID: 37992518 DOI: 10.1016/j.ejmech.2023.115924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/28/2023] [Accepted: 10/29/2023] [Indexed: 11/24/2023]
Abstract
The functions of the bromodomain and extra terminal (BET) family of proteins have been proved to be involved in various diseases, particularly the acute myeloid leukemia (AML). In this work, guided by free energy perturbation (FEP) calculation, a methyl group was selected to be attached to the 1H-imidazo[4,5-c]quinoline skeleton, and a series of congeneric compounds were synthesized. Among them, compound 10 demonstrated outstanding activity against BRD4 BD1 with an IC50 value of 1.9 nM and exhibited remarkable antiproliferative effects against MV4-11 cells. The X-ray cocrystal structure proved that 10 occupied the acetylated lysine (KAc) binding cavity and the WPF shelf of BRD4 BD1. Additionally, 10 displayed high selectivity towards BET family members, effectively inhibiting the growth of AML cells, promoting apoptosis, and arresting the cell cycle at the G0/G1 phase. Further mechanistic studies demonstrated that compound 10 could suppress the expression of c-Myc and CDK6 while enhancing the expression of P21, PARP, and cleaved PARP. Moreover, 10 exhibited remarkable pharmacokinetic properties and significant antitumor efficacy in vivo. Therefore, compound 10 may represent a new, potent and selective BET bromodomain inhibitor for the development of therapeutics to treat AML.
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Affiliation(s)
- Su Yu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yan Zhang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jie Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hongrui Xu
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China
| | - Suke Lan
- College of Chemistry & Environment Protection Engineering, Southwest Minzu University, Chengdu, 610041, China
| | - Binyan Zhao
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Meng Luo
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xinyu Ma
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hongjia Zhang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shirui Wang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hui Shen
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China
| | - Yan Zhang
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China
| | - Yong Xu
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China.
| | - Rui Li
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
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47
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AlRawashdeh S, Barakat KH. Applications of Molecular Dynamics Simulations in Drug Discovery. Methods Mol Biol 2024; 2714:127-141. [PMID: 37676596 DOI: 10.1007/978-1-0716-3441-7_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.
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Affiliation(s)
- Sara AlRawashdeh
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Khaled H Barakat
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
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48
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Singh K, Bhushan B, Singh B. Advances in Drug Discovery and Design using Computer-aided Molecular Modeling. Curr Comput Aided Drug Des 2024; 20:697-710. [PMID: 37711101 DOI: 10.2174/1573409920666230914123005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023]
Abstract
Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.
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Affiliation(s)
- Kuldeep Singh
- Department of Pharmacology, Rajiv Academy for Pharmacy, Mathura Uttar Pradesh, India
| | - Bharat Bhushan
- Department of Pharmacology, Institute of Pharmaceutical Research, GLA University, Mathura Uttar Pradesh, India
| | - Bhoopendra Singh
- Department of Pharmacy, B.S.A. College of Engineering & Technology, Mathura Uttar Pradesh India
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49
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Herz AM, Kellici T, Morao I, Michel J. Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities. Methods Mol Biol 2024; 2716:241-264. [PMID: 37702943 DOI: 10.1007/978-1-0716-3449-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.
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Affiliation(s)
- Anna M Herz
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK
| | - Tahsin Kellici
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
- Merck & Co., Inc., Modelling and Informatics, West Point, PA, USA
| | - Inaki Morao
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK.
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50
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Talevi A. Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects. Methods Mol Biol 2024; 2714:1-20. [PMID: 37676590 DOI: 10.1007/978-1-0716-3441-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.
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Affiliation(s)
- Alan Talevi
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), La Plata, Argentina.
- Argentinean National Council of Scientific and Technical Research (CONICET), La Plata, Argentina.
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