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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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Zhang S, Giese TJ, Lee TS, York DM. Alchemical Enhanced Sampling with Optimized Phase Space Overlap. J Chem Theory Comput 2024; 20:3935-3953. [PMID: 38666430 DOI: 10.1021/acs.jctc.4c00251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2024]
Abstract
An alchemical enhanced sampling (ACES) method has recently been introduced to facilitate importance sampling in free energy simulations. The method achieves enhanced sampling from Hamiltonian replica exchange within a dual topology framework while utilizing new smoothstep softcore potentials. A common sampling problem encountered in lead optimization is the functionalization of aromatic rings that exhibit distinct conformational preferences when interacting with the protein. It is difficult to converge the distribution of ring conformations due to the long time scale of ring flipping events; however, the ACES method addresses this issue by modeling the syn and anti ring conformations within a dual topology. ACES thereby samples the conformer distributions by alchemically tunneling between states, as opposed to traversing a physical pathway with a high rotational barrier. We demonstrate the use of ACES to overcome conformational sampling issues involving ring flipping in ML300-derived noncovalent inhibitors of SARS-CoV-2 Main Protease (Mpro). The demonstrations explore how the use of replica exchange and the choice of softcore selection affects the convergence of the ring conformation distributions. Furthermore, we examine how the accuracy of the calculated free energies is affected by the degree of phase space overlap (PSO) between adjacent states (i.e., between neighboring λ-windows) and the Hamiltonian replica exchange acceptance ratios. Both of these factors are sensitive to the spacing between the intermediate states. We introduce a new method for choosing a schedule of λ values. The method analyzes short "burn-in" simulations to construct a 2D map of the nonlocal PSO. The schedule is obtained by optimizing an alchemical pathway on the 2D map that equalizes the PSO between the λ intervals. The optimized phase space overlap λ-spacing method (Opt-PSO) leads to more numerous end-to-end single passes and round trips due to the correlation between PSO and Hamiltonian replica exchange acceptance ratios. The improved exchange statistics enhance the efficiency of ACES method. The method has been implemented into the FE-ToolKit software package, which is freely available.
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Affiliation(s)
- Shi Zhang
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Timothy J Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States
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3
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Ferraz-Caetano J, Teixeira F, Cordeiro MNDS. Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy. J Chem Inf Model 2024; 64:2250-2262. [PMID: 37603608 PMCID: PMC11005042 DOI: 10.1021/acs.jcim.3c00544] [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: 04/11/2023] [Indexed: 08/23/2023]
Abstract
Many challenges persist in developing accurate computational models for predicting solvation free energy (ΔGsol). Despite recent developments in Machine Learning (ML) methodologies that outperformed traditional quantum mechanical models, several issues remain concerning explanatory insights for broad chemical predictions with an acceptable speed-accuracy trade-off. To overcome this, we present a novel supervised ML model to predict the ΔGsol for an array of solvent-solute pairs. Using two different ensemble regressor algorithms, we made fast and accurate property predictions using open-source chemical features, encoding complex electronic, structural, and surface area descriptors for every solvent and solute. By integrating molecular properties and chemical interaction features, we have analyzed individual descriptor importance and optimized our model though explanatory information form feature groups. On aqueous and organic solvent databases, ML models revealed the predictive relevance of solutes with increasing polar surface area and decreasing polarizability, yielding better results than state-of-the-art benchmark Neural Network methods (without complex quantum mechanical or molecular dynamic simulations). Both algorithms successfully outperformed previous ΔGsol predictions methods, with a maximum absolute error of 0.22 ± 0.02 kcal mol-1, further validated in an external benchmark database and with solvent hold-out tests. With these explanatory and statistical insights, they allow a thoughtful application of this method for predicting other thermodynamic properties, stressing the relevance of ML modeling for further complex computational chemistry problems.
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Affiliation(s)
- José Ferraz-Caetano
- Department
of Chemistry and Biochemistry − Faculty of Sciences, University of Porto - Rua do Campo Alegre, S/N, 4169-007 Porto, Portugal
| | - Filipe Teixeira
- Centre
of Chemistry, University of Minho, Campus
de Gualtar, 4710-057 Braga, Portugal
| | - M. Natália D. S. Cordeiro
- Department
of Chemistry and Biochemistry − Faculty of Sciences, University of Porto - Rua do Campo Alegre, S/N, 4169-007 Porto, Portugal
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4
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Draper MR, Waterman A, Dannatt JE, Patel P. Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge. Phys Chem Chem Phys 2024; 26:7907-7919. [PMID: 38376855 PMCID: PMC10938873 DOI: 10.1039/d3cp04140a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.
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Affiliation(s)
- Michael R Draper
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | - Asa Waterman
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
| | | | - Prajay Patel
- Chemistry Department, University of Dallas, Irving, Texas, 75062, USA.
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5
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Wittlinger F, Ogboo BC, Shevchenko E, Damghani T, Pham CD, Schaeffner IK, Oligny BT, Chitnis SP, Beyett TS, Rasch A, Buckley B, Urul DA, Shaurova T, May EW, Schaefer EM, Eck MJ, Hershberger PA, Poso A, Laufer SA, Heppner DE. Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors. Commun Chem 2024; 7:38. [PMID: 38378740 PMCID: PMC10879502 DOI: 10.1038/s42004-024-01108-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/16/2024] [Indexed: 02/22/2024] Open
Abstract
Bivalent molecules consisting of groups connected through bridging linkers often exhibit strong target binding and unique biological effects. However, developing bivalent inhibitors with the desired activity is challenging due to the dual motif architecture of these molecules and the variability that can be introduced through differing linker structures and geometries. We report a set of alternatively linked bivalent EGFR inhibitors that simultaneously occupy the ATP substrate and allosteric pockets. Crystal structures show that initial and redesigned linkers bridging a trisubstituted imidazole ATP-site inhibitor and dibenzodiazepinone allosteric-site inhibitor proved successful in spanning these sites. The re-engineered linker yielded a compound that exhibited significantly higher potency (~60 pM) against the drug-resistant EGFR L858R/T790M and L858R/T790M/C797S, which was superadditive as compared with the parent molecules. The enhanced potency is attributed to factors stemming from the linker connection to the allosteric-site group and informs strategies to engineer linkers in bivalent agent design.
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Affiliation(s)
- Florian Wittlinger
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tübingen, Germany
| | - Blessing C Ogboo
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Ekaterina Shevchenko
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies" Eberhard Karls Universität Tübingen, 72076, Tübingen, Germany
- Tübingen Center for Academic Drug Discovery & Development (TüCAD2), 72076, Tübingen, Germany
| | - Tahereh Damghani
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Calvin D Pham
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Ilse K Schaeffner
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Brandon T Oligny
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Surbhi P Chitnis
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Tyler S Beyett
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, 5119 Rollins Research Center, 1510 Clifton Rd, Atlanta, GA, 30322, USA
| | - Alexander Rasch
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tübingen, Germany
| | - Brian Buckley
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA
| | - Daniel A Urul
- AssayQuant Technologies, Inc., Marlboro, MA, 01752, USA
| | - Tatiana Shaurova
- Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA
| | - Earl W May
- AssayQuant Technologies, Inc., Marlboro, MA, 01752, USA
| | | | - Michael J Eck
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Pamela A Hershberger
- Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA
| | - Antti Poso
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies" Eberhard Karls Universität Tübingen, 72076, Tübingen, Germany
- Tübingen Center for Academic Drug Discovery & Development (TüCAD2), 72076, Tübingen, Germany
- School of Pharmacy, University of Eastern Finland, 70210, Kuopio, Finland
| | - Stefan A Laufer
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tübingen, Germany.
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies" Eberhard Karls Universität Tübingen, 72076, Tübingen, Germany.
- Tübingen Center for Academic Drug Discovery & Development (TüCAD2), 72076, Tübingen, Germany.
| | - David E Heppner
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA.
- Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14203, USA.
- Department of Structural Biology, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA.
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6
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Mori M, Cocorullo M, Tresoldi A, Cazzaniga G, Gelain A, Stelitano G, Chiarelli LR, Tomaiuolo M, Delre P, Mangiatordi GF, Garofalo M, Cassetta A, Covaceuszach S, Villa S, Meneghetti F. Structural basis for specific inhibition of salicylate synthase from Mycobacterium abscessus. Eur J Med Chem 2024; 265:116073. [PMID: 38169270 DOI: 10.1016/j.ejmech.2023.116073] [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: 11/16/2023] [Revised: 12/15/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024]
Abstract
Blocking iron uptake and metabolism has been emerging as a promising therapeutic strategy for the development of novel antimicrobial compounds. Like all mycobacteria, M. abscessus (Mab) has evolved several countermeasures to scavenge iron from host carrier proteins, including the production of siderophores, which play a crucial role in these processes. In this study, we solved, for the first time, the crystal structure of Mab-SaS, the first enzyme involved in the biosynthesis of siderophores. Moreover, we screened a small, focused library and identified a compound exhibiting a potent inhibitory effect against Mab-SaS (IC50 ≈ 2 μM). Its binding mode was investigated by means of Induced Fit Docking simulations, performed on the crystal structure presented herein. Furthermore, cytotoxicity data and pharmacokinetic predictions revealed the safety and drug-likeness of this class of compounds. Finally, the crystallographic data were used to optimize the model for future virtual screening campaigns. Taken together, the findings of our study pave the way for the identification of potent Mab-SaS inhibitors, based on both established and unexplored chemotypes.
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Affiliation(s)
- Matteo Mori
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Mario Cocorullo
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via A. Ferrata 9, 27100, Pavia, Italy
| | - Andrea Tresoldi
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Giulia Cazzaniga
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Arianna Gelain
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
| | - Giovanni Stelitano
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via A. Ferrata 9, 27100, Pavia, Italy
| | - Laurent R Chiarelli
- Department of Biology and Biotechnology "Lazzaro Spallanzani", University of Pavia, Via A. Ferrata 9, 27100, Pavia, Italy
| | - Martina Tomaiuolo
- Institute of Crystallography, National Research Council, Trieste Outstation, Area Science Park - Basovizza, S.S.14 - Km. 163.5, 34149, Trieste, Italy
| | - Pietro Delre
- Institute of Crystallography, National Research Council, Via G. Amendola 122/o, 70126, Bari, Italy
| | - Giuseppe F Mangiatordi
- Institute of Crystallography, National Research Council, Via G. Amendola 122/o, 70126, Bari, Italy
| | - Mariangela Garofalo
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via F. Marzolo 5, 35131, Padova, Italy
| | - Alberto Cassetta
- Institute of Crystallography, National Research Council, Trieste Outstation, Area Science Park - Basovizza, S.S.14 - Km. 163.5, 34149, Trieste, Italy
| | - Sonia Covaceuszach
- Institute of Crystallography, National Research Council, Trieste Outstation, Area Science Park - Basovizza, S.S.14 - Km. 163.5, 34149, Trieste, Italy.
| | - Stefania Villa
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy.
| | - Fiorella Meneghetti
- Department of Pharmaceutical Sciences, University of Milan, Via L. Mangiagalli 25, 20133, Milano, Italy
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7
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Choi K. The Structure-property Relationships of Clinically Approved Protease Inhibitors. Curr Med Chem 2024; 31:1441-1463. [PMID: 37031455 DOI: 10.2174/0929867330666230409232655] [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: 11/05/2022] [Revised: 01/17/2023] [Accepted: 02/24/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Proteases play important roles in the regulation of many physiological processes, and protease inhibitors have become one of the important drug classes. Especially because the development of protease inhibitors often starts from a substrate- based peptidomimetic strategy, many of the initial lead compounds suffer from pharmacokinetic liabilities. OBJECTIVE To reduce drug attrition rates, drug metabolism and pharmacokinetics studies are fully integrated into modern drug discovery research, and the structure-property relationship illustrates how the modification of the chemical structure influences the pharmacokinetic and toxicological properties of drug compounds. Understanding the structure- property relationships of clinically approved protease inhibitor drugs and their analogues could provide useful information on the lead-to-candidate optimization strategies. METHODS About 70 inhibitors against human or pathogenic viral proteases have been approved until the end of 2021. In this review, 17 inhibitors are chosen for the structure- property relationship analysis because detailed pharmacological and/or physicochemical data have been disclosed in the medicinal chemistry literature for these inhibitors and their close analogues. RESULTS The compiled data are analyzed primarily focusing on the pharmacokinetic or toxicological deficiencies found in lead compounds and the structural modification strategies used to generate candidate compounds. CONCLUSION The structure-property relationships hereby summarized how the overall druglike properties could be successfully improved by modifying the structure of protease inhibitors. These specific examples are expected to serve as useful references and guidance for developing new protease inhibitor drugs in the future.
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Affiliation(s)
- Kihang Choi
- Department of Chemistry, Korea University, Seoul, 02841, Korea (ROK)
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8
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York DM. Modern Alchemical Free Energy Methods for Drug Discovery Explained. ACS PHYSICAL CHEMISTRY AU 2023; 3:478-491. [PMID: 38034038 PMCID: PMC10683484 DOI: 10.1021/acsphyschemau.3c00033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 12/02/2023]
Abstract
This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.
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Affiliation(s)
- Darrin M. York
- Laboratory for Biomolecular
Simulation Research, Institute for Quantitative Biomedicine, and Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway, New Jersey 08854, United States
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9
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Zhao Y, Xiong W, Li C, Zhao R, Lu H, Song S, Zhou Y, Hu Y, Shi B, Ge J. Hypoxia-induced signaling in the cardiovascular system: pathogenesis and therapeutic targets. Signal Transduct Target Ther 2023; 8:431. [PMID: 37981648 PMCID: PMC10658171 DOI: 10.1038/s41392-023-01652-9] [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: 07/10/2023] [Revised: 09/10/2023] [Accepted: 09/13/2023] [Indexed: 11/21/2023] Open
Abstract
Hypoxia, characterized by reduced oxygen concentration, is a significant stressor that affects the survival of aerobic species and plays a prominent role in cardiovascular diseases. From the research history and milestone events related to hypoxia in cardiovascular development and diseases, The "hypoxia-inducible factors (HIFs) switch" can be observed from both temporal and spatial perspectives, encompassing the occurrence and progression of hypoxia (gradual decline in oxygen concentration), the acute and chronic manifestations of hypoxia, and the geographical characteristics of hypoxia (natural selection at high altitudes). Furthermore, hypoxia signaling pathways are associated with natural rhythms, such as diurnal and hibernation processes. In addition to innate factors and natural selection, it has been found that epigenetics, as a postnatal factor, profoundly influences the hypoxic response and progression within the cardiovascular system. Within this intricate process, interactions between different tissues and organs within the cardiovascular system and other systems in the context of hypoxia signaling pathways have been established. Thus, it is the time to summarize and to construct a multi-level regulatory framework of hypoxia signaling and mechanisms in cardiovascular diseases for developing more therapeutic targets and make reasonable advancements in clinical research, including FDA-approved drugs and ongoing clinical trials, to guide future clinical practice in the field of hypoxia signaling in cardiovascular diseases.
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Affiliation(s)
- Yongchao Zhao
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China
| | - Weidong Xiong
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China
- Key Laboratory of Viral Heart Diseases, National Health Commission, Shanghai, 200032, China
- Key Laboratory of Viral Heart Diseases, Chinese Academy of Medical Sciences, Shanghai, 200032, China
| | - Chaofu Li
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China
| | - Ranzun Zhao
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China
| | - Hao Lu
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Shanghai, 200032, China
- Shanghai Clinical Research Center for Interventional Medicine, Shanghai, 200032, China
| | - Shuai Song
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Shanghai, 200032, China
- Shanghai Clinical Research Center for Interventional Medicine, Shanghai, 200032, China
| | - You Zhou
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China
- National Clinical Research Center for Interventional Medicine, Shanghai, 200032, China
- Shanghai Clinical Research Center for Interventional Medicine, Shanghai, 200032, China
| | - Yiqing Hu
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China.
| | - Bei Shi
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China.
| | - Junbo Ge
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, China.
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, 200032, China.
- Key Laboratory of Viral Heart Diseases, National Health Commission, Shanghai, 200032, China.
- Key Laboratory of Viral Heart Diseases, Chinese Academy of Medical Sciences, Shanghai, 200032, China.
- National Clinical Research Center for Interventional Medicine, Shanghai, 200032, China.
- Shanghai Clinical Research Center for Interventional Medicine, Shanghai, 200032, China.
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China.
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10
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Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
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Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
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11
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Suleiman M, Almalki FA, Ben Hadda T, Kawsar SMA, Chander S, Murugesan S, Bhat AR, Bogoyavlenskiy A, Jamalis J. Recent Progress in Synthesis, POM Analyses and SAR of Coumarin-Hybrids as Potential Anti-HIV Agents-A Mini Review. Pharmaceuticals (Basel) 2023; 16:1538. [PMID: 38004404 PMCID: PMC10675815 DOI: 10.3390/ph16111538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/18/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
The human immunodeficiency virus (HIV) is the primary cause of acquired immune deficiency syndrome (AIDS), one of the deadliest pandemic diseases. Various mechanisms and procedures have been pursued to synthesise several anti-HIV agents, but due to the severe side effects and multidrug resistance spawning from the treatment of HIV/AIDS using highly active retroviral therapy (HAART), it has become imperative to design and synthesise novel anti-HIV agents. Literature has shown that natural sources, particularly the plant kingdom, can release important metabolites that have several biological, mechanistic and structural representations similar to chemically synthesised compounds. Certainly, compounds from natural and ethnomedicinal sources have proven to be effective in the management of HIV/AIDS with low toxicity, fewer side effects and affordability. From plants, fungi and bacteria, coumarin can be obtained, which is a secondary metabolite and is well known for its actions in different stages of the HIV replication cycle: protease, integrase and reverse transcriptase (RT) inhibition, cell membrane fusion and viral host attachment. These, among other reasons, are why coumarin moieties will be the basis of a good building block for the development of potent anti-HIV agents. This review aims to outline the synthetic pathways, structure-activity relationship (SAR) and POM analyses of coumarin hybrids with anti-HIV activity, detailing articles published between 2000 and 2023.
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Affiliation(s)
- Mustapha Suleiman
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia;
- Department of Chemistry, Sokoto State University, Birnin Kebbi Road, Sokoto 852101, Nigeria
| | - Faisal A. Almalki
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Umm Al-Qura University, Mecca 21955, Saudi Arabia; (F.A.A.); (T.B.H.)
| | - Taibi Ben Hadda
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Umm Al-Qura University, Mecca 21955, Saudi Arabia; (F.A.A.); (T.B.H.)
- Laboratory of Applied Chemistry & Environment, Faculty of Sciences, Mohammed Premier University, MB 524, Oujda 60000, Morocco
| | - Sarkar M. A. Kawsar
- Laboratory of Carbohydrate and Nucleoside Chemistry, Department of Chemistry, University of Chittagong, Chittagong 4331, Bangladesh;
| | - Subhash Chander
- Amity Institute of Phytochemistry & Phytomedicine, Amity University Uttar Pradesh, Noida 201313, India;
| | - Sankaranarayanan Murugesan
- Medicinal Chemistry Research Laboratory, Birla Institute of Technology & Science Pilani (BITS Pilani), Pilani Campus, Pilani 333031, India;
| | - Ajmal R. Bhat
- Department of Chemistry, R.T.M. Nagpur University, Nagpur 440033, India;
| | - Andrey Bogoyavlenskiy
- Research and Production Center for Microbiology and Virology, Almaty 050010, Kazakhstan
| | - Joazaizulfazli Jamalis
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia;
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12
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Zhao X, Li H, Zhang K, Huang SY. Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information. J Phys Chem B 2023; 127:9021-9034. [PMID: 37822259 DOI: 10.1021/acs.jpcb.3c04421] [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: 10/13/2023]
Abstract
Scoring functions for protein-ligand interactions play a critical role in structure-based drug design. Owing to the good balance between general applicability and computational efficiency, knowledge-based scoring functions have obtained significant advancements and achieved many successes. Nevertheless, knowledge-based scoring functions face a challenge in utilizing the experimental affinity data and thus may not perform well in binding affinity prediction. Addressing the challenge, we have proposed an improved version of the iterative knowledge-based scoring function ITScore by considering binding affinity information, which is referred to as ITScoreAff, based on a large training set of 6216 protein-ligand complexes with both structures and affinity data. ITScoreAff was extensively evaluated and compared with ITScore, 33 traditional, and 6 machine learning scoring functions in terms of docking power, ranking power, and screening power on the independent CASF-2016 benchmark. It was shown that ITScoreAff obtained an overall better performance than the other 40 scoring functions and gave an average success rate of 85.3% in docking power, a correlation coefficient of 0.723 in scoring power, and an average rank correlation coefficient of 0.668 in ranking power. In addition, ITScoreAff also achieved the overall best screening power when the top 10% of the ranked database were considered. These results demonstrated the robustness of ITScoreAff and its improvement over existing scoring functions.
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Affiliation(s)
- Xuejun Zhao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Keqiong Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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13
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Heppner D, Wittlinger F, Ogboo B, Shevchenko E, Damghani T, Pham C, Schaeffner I, Oligny B, Chitnis S, Beyett T, Rasch A, Buckley B, Urul D, Shaurova T, May E, Schaefer E, Eck M, Hershberger P, Poso A, Laufer S. Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors. RESEARCH SQUARE 2023:rs.3.rs-3286949. [PMID: 37790373 PMCID: PMC10543509 DOI: 10.21203/rs.3.rs-3286949/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Bivalent molecules consisting of groups connected through bridging linkers often exhibit strong target binding and unique biological effects. However, developing bivalent inhibitors with the desired activity is challenging due to the dual motif architecture of these molecules and the variability that can be introduced through differing linker structures and geometries. We report a set of alternatively linked bivalent EGFR inhibitors that simultaneously occupy the ATP substrate and allosteric pockets. Crystal structures show that initial and redesigned linkers bridging a trisubstituted imidazole ATP-site inhibitor and dibenzodiazepinone allosteric-site inhibitor proved successful in spanning these sites. The reengineered linker yielded a compound that exhibited significantly higher potency (~60 pM) against the drug-resistant EGFR L858R/T790M and L858R/T790M/C797S, which was superadditive as compared with the parent molecules. The enhanced potency is attributed to factors stemming from the linker connection to the allosteric-site group and informs strategies to engineer linkers in bivalent agent design.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Michael Eck
- Dana-Farber Cancer Institute & Department of Biological Chemistry and Molecular Pharmacology at Harvard Medical School
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14
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Gruber N, Fernández-Canigia L, Kilimciler NB, Stipa P, Bisceglia JA, García MB, Gonzalez Maglio DH, Paz ML, Orelli LR. Amidinoquinoxaline N-oxides: synthesis and activity against anaerobic bacteria. RSC Adv 2023; 13:27391-27402. [PMID: 37711381 PMCID: PMC10498151 DOI: 10.1039/d3ra01184d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/20/2023] [Indexed: 09/16/2023] Open
Abstract
We present herein an in-depth study on the activity of amidinoquinoxaline N-oxides 1 against Gram-positive and Gram-negative anaerobic bacteria. Based on 5-phenyl-2,3-dihydropyrimidoquinoxaline N-oxide 1a, the selected structural variations included in our study comprise the substituents α- to the N-oxide function, the benzofused ring, substitution and quaternization of the amidine moiety, and the amidine ring size. Compounds 1 showed good to excellent antianaerobic activity, evaluated as the corresponding CIM50 and CIM90 values, and an antimicrobial spectrum similar to metronidazole. Six out of 13 compounds 1 had CIM90 values significantly lower than the reference drug. Among them, imidazoline derivatives 1i-l were the most active structures. Such compounds were synthesized by base-promoted ring closure of the corresponding amidines. The N-oxides under study showed no significant cytotoxicity against RAW 264.7 cells, with high selectivity indexes. Their calculated ADME properties indicate that the compounds are potentially good oral drug candidates. The antianaerobic activity correlated satisfactorily with the electron affinity of the compounds, suggesting that they may undergo bioreductive activation before exerting their antibacterial activity.
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Affiliation(s)
- Nadia Gruber
- Universidad de Buenos Aires, CONICET, Química Orgánica II, Departamento de Ciencias Químicas, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
| | | | - Natalia B Kilimciler
- Universidad de Buenos Aires, CONICET, Química Orgánica II, Departamento de Ciencias Químicas, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
| | - Pierluigi Stipa
- SIMAU Departament - Chemistry Division, Università Politecnica delle Marche Via Brecce Bianche 12 Ancona (I-60131) Italy
| | - Juan A Bisceglia
- Universidad de Buenos Aires, CONICET, Química Orgánica II, Departamento de Ciencias Químicas, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
| | - María B García
- Universidad de Buenos Aires, CONICET, Química Orgánica II, Departamento de Ciencias Químicas, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
| | - Daniel H Gonzalez Maglio
- Universidad de Buenos Aires, Instituto de Estudios de la Inmunidad Humoral (IDEHU), Cátedra de Inmunología, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
| | - Mariela L Paz
- Universidad de Buenos Aires, Instituto de Estudios de la Inmunidad Humoral (IDEHU), Cátedra de Inmunología, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
| | - Liliana R Orelli
- Universidad de Buenos Aires, CONICET, Química Orgánica II, Departamento de Ciencias Químicas, Facultad de Farmacia y Bioquímica Junín 956 (1113) Buenos Aires Argentina
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15
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Ghahremanpour MM, Saar A, Tirado-Rives J, Jorgensen WL. Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease. J Chem Inf Model 2023; 63:5309-5318. [PMID: 37561001 DOI: 10.1021/acs.jcim.3c00874] [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: 08/11/2023]
Abstract
Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.
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Affiliation(s)
| | - Anastasia Saar
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Julian Tirado-Rives
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - William L Jorgensen
- Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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16
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Jin J, Wang D, Shi G, Bao J, Wang J, Zhang H, Pan P, Li D, Yao X, Liu H, Hou T, Kang Y. FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization. J Med Chem 2023; 66:10808-10823. [PMID: 37471134 DOI: 10.1021/acs.jmedchem.3c01009] [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: 07/21/2023]
Abstract
Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design. In a large-scale benchmark evaluation on ZINC, CASF, and PDBbind test sets, FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of the generated molecules and can recover over 92% of the original molecules in the PDBbind test set (with at least five atoms). FFLOM also exhibits excellent potential applicability in several practical scenarios encompassing fragment linking, PROTAC design, R-group growing, and R-group optimization. In all four cases, FFLOM can perfectly reconstruct the ground-truth compounds and generate over 74% of molecules with novel fragments, some of which have higher binding affinity than the ground truth.
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Affiliation(s)
- Jieyu Jin
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Guqin Shi
- Shanghai Qilu Pharmaceutical R&D Center, 576 Libing Road, Pudong New Area District, Shanghai 310115, China
| | - Jingxiao Bao
- Shanghai Qilu Pharmaceutical R&D Center, 576 Libing Road, Pudong New Area District, Shanghai 310115, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- 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
| | - Dan Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macau 999078, China
| | - Tingjun Hou
- 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
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17
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Shen C, Zhang X, Hsieh CY, Deng Y, Wang D, Xu L, Wu J, Li D, Kang Y, Hou T, Pan P. A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem Sci 2023; 14:8129-8146. [PMID: 37538816 PMCID: PMC10395315 DOI: 10.1039/d3sc02044d] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/05/2023] Open
Abstract
Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.
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Affiliation(s)
- Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 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
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology Changzhou 213001 China
| | - Jian Wu
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- 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
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, 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
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18
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Chen W, Cui D, Jerome SV, Michino M, Lenselink EB, Huggins DJ, Beautrait A, Vendome J, Abel R, Friesner RA, Wang L. Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations. J Chem Inf Model 2023; 63:3171-3185. [PMID: 37167486 DOI: 10.1021/acs.jcim.3c00013] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R2 = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.
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Affiliation(s)
- Wei Chen
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Di Cui
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Steven V Jerome
- Schrödinger, Inc., 10201 Wateridge Circle, Suite 220, San Diego, California 92121, United States
| | - Mayako Michino
- Tri-Institutional Therapeutics Discovery Institute, 413 E. 69th Street, New York, New York 10065, United States
| | | | - David J Huggins
- Tri-Institutional Therapeutics Discovery Institute, 413 E. 69th Street, New York, New York 10065, United States
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States
| | - Alexandre Beautrait
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Jeremie Vendome
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, New York, New York 10027, United States
| | - Lingle Wang
- Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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19
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Kumar AP, P P, Mandal S, Kumar BRP, Raju RM, Dhanabal S, Rajagopal K, G R, X PN, Justin A. Computational studies, synthesis, in-vitro binding and transcription analysis of novel imidazolidine-2,4-dione and 2-thioxo thiazolidine-4-one based glitazones for central PPAR-γ agonism. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2023.135503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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20
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Carter ZJ, Hollander K, Spasov KA, Anderson KS, Jorgensen WL. Design, synthesis, and biological testing of biphenylmethyloxazole inhibitors targeting HIV-1 reverse transcriptase. Bioorg Med Chem Lett 2023; 84:129216. [PMID: 36871704 PMCID: PMC10278203 DOI: 10.1016/j.bmcl.2023.129216] [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: 11/17/2022] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023]
Abstract
We report non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) using a biphenylmethyloxazole pharmacophore. A crystal structure of benzyloxazole 1 was obtained and suggested the potential viability of biphenyl analogues. In particular, 6a, 6b, and 7 turned out to be potent NNRTIs with low-nanomolar activity in enzyme inhibition and infected T-cell assays, and with low cytotoxicity. Though modeling further suggested that analogues with fluorosulfate and epoxide warheads might provide covalent modification of Tyr188, synthesis and testing did not find evidence for this outcome.
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Affiliation(s)
- Zachary J Carter
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA
| | - Klarissa Hollander
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520-8066, USA; Department of Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, CT 06520-8066, USA
| | - Krasimir A Spasov
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520-8066, USA
| | - Karen S Anderson
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520-8066, USA; Department of Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, CT 06520-8066, USA.
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21
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Kwon Y, Park S, Lee J, Kang J, Lee HJ, Kim W. BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data. J Chem Inf Model 2023; 63:1429-1437. [PMID: 36821004 DOI: 10.1021/acs.jcim.2c01300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., "assay repositioning". The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.
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Affiliation(s)
| | - Sera Park
- KaiPharm, Seoul 03760, Republic of Korea
| | - Jaeok Lee
- College of Pharmacy, Research Institute of Pharmaceutical Science, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Jiyeon Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Hwa Jeong Lee
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea
| | - Wankyu Kim
- KaiPharm, Seoul 03760, Republic of Korea.,Department of Life Sciences, College of Natural Science, Ewha Womans University, Seoul 03760, Republic of Korea
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22
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Potlitz F, Link A, Schulig L. Advances in the discovery of new chemotypes through ultra-large library docking. Expert Opin Drug Discov 2023; 18:303-313. [PMID: 36714919 DOI: 10.1080/17460441.2023.2171984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (Mpro). AREAS COVERED This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel Mpro inhibitors targeting the SARS-CoV-2 virus. EXPERT OPINION With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.
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Affiliation(s)
- Felix Potlitz
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Andreas Link
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
| | - Lukas Schulig
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Greifswald, Germany
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23
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Zeng J, Tao Y, Giese TJ, York DM. QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery. J Chem Theory Comput 2023; 19:1261-1275. [PMID: 36696673 PMCID: PMC9992268 DOI: 10.1021/acs.jctc.2c01172] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE). The model has the advantage that it is able to properly treat electrostatic interactions and handle changes in charge/protonation states. The model is trained against reference data computed at the ωB97X/6-31G* level (as in the ANI-1x data set) and compared to several other approximate semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is demonstrated to be accurate for a wide range of intra- and intermolecular interactions (despite its intended use as an internal energy model) and has shown to perform exceptionally well for relative protonation/deprotonation energies and tautomers. An example application to model reactions involved in RNA strand cleavage catalyzed by protein and nucleic acid enzymes illustrates QDπ has average errors less than 0.5 kcal/mol, whereas the other models compared have errors over an order of magnitude greater. Taken together, this makes QDπ highly attractive as a potential force field model for drug discovery.
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Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Yujun Tao
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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24
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Mangiatordi GF, Cavalluzzi MM, Delre P, Lamanna G, Lumuscio MC, Saviano M, Majoral JP, Mignani S, Duranti A, Lentini G. Endocannabinoid Degradation Enzyme Inhibitors as Potential Antipsychotics: A Medicinal Chemistry Perspective. Biomedicines 2023; 11:biomedicines11020469. [PMID: 36831006 PMCID: PMC9953700 DOI: 10.3390/biomedicines11020469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/08/2023] Open
Abstract
The endocannabinoid system (ECS) plays a very important role in numerous physiological and pharmacological processes, such as those related to the central nervous system (CNS), including learning, memory, emotional processing, as well pain control, inflammatory and immune response, and as a biomarker in certain psychiatric disorders. Unfortunately, the half-life of the natural ligands responsible for these effects is very short. This perspective describes the potential role of the inhibitors of the enzymes fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MGL), which are mainly responsible for the degradation of endogenous ligands in psychic disorders and related pathologies. The examination was carried out considering both the impact that the classical exogenous ligands such as Δ9-tetrahydrocannabinol (THC) and (-)-trans-cannabidiol (CBD) have on the ECS and through an analysis focused on the possibility of predicting the potential toxicity of the inhibitors before they are subjected to clinical studies. In particular, cardiotoxicity (hERG liability), probably the worst early adverse reaction studied during clinical studies focused on acute toxicity, was predicted, and some of the most used and robust metrics available were considered to select which of the analyzed compounds could be repositioned as possible oral antipsychotics.
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Affiliation(s)
| | - Maria Maddalena Cavalluzzi
- Department of Pharmacy—Pharmaceutical Sciences, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy
| | - Pietro Delre
- Institute of Crystallography, National Research Council of Italy, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Giuseppe Lamanna
- Institute of Crystallography, National Research Council of Italy, Via G. Amendola 122/O, 70126 Bari, Italy
- Department of Chemistry, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy
| | - Maria Cristina Lumuscio
- Institute of Crystallography, National Research Council of Italy, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Michele Saviano
- Institute of Crystallography, National Research Council of Italy, Via Vivaldi 43, 81100 Caserta, Italy
| | - Jean-Pierre Majoral
- Laboratoire de Chimie de Coordination du CNRS, 205 Route de Narbonne, CEDEX 4, 31077 Toulouse, France
- Université Toulouse, 118 Route de Narbonne, CEDEX 4, 31077 Toulouse, France
| | - Serge Mignani
- CERMN (Centre d’Etudes et de Recherche sur le Médicament de Normandie), Université de Caen, 14032 Caen, France
- CQM—Centro de Química da Madeira, MMRG (Molecular Materials Research Group), Campus da Penteada, Universidade da Madeira, 9020-105 Funchal, Portugal
| | - Andrea Duranti
- Department of Biomolecular Sciences, University of Urbino Carlo Bo, Piazza del Rinascimento 6, 61029 Urbino, Italy
- Correspondence: ; Tel.: +39-0722-303501
| | - Giovanni Lentini
- Department of Pharmacy—Pharmaceutical Sciences, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy
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25
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Lee TS, Tsai HC, Ganguly A, York DM. ACES: Optimized Alchemically Enhanced Sampling. J Chem Theory Comput 2023; 19:10.1021/acs.jctc.2c00697. [PMID: 36630672 PMCID: PMC10333454 DOI: 10.1021/acs.jctc.2c00697] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
We present an alchemical enhanced sampling (ACES) method implemented in the GPU-accelerated AMBER free energy MD engine. The methods hinges on the creation of an "enhanced sampling state" by reducing or eliminating selected potential energy terms and interactions that lead to kinetic traps and conformational barriers while maintaining those terms that curtail the need to otherwise sample large volumes of phase space. For example, the enhanced sampling state might involve transforming regions of a ligand and/or protein side chain into a noninteracting "dummy state" with internal electrostatics and torsion angle terms turned off. The enhanced sampling state is connected to a real-state end point through a Hamiltonian replica exchange (HREMD) framework that is facilitated by newly developed alchemical transformation pathways and smoothstep softcore potentials. This creates a counterdiffusion of real and enhanced-sampling states along the HREMD network. The effect of a differential response of the environment to the real and enhanced-sampling states is minimized by leveraging the dual topology framework in AMBER to construct a counterbalancing HREMD network in the opposite alchemical direction with the same (or similar) real and enhanced sampling states at inverted end points. The method has been demonstrated in a series of test cases of increasing complexity where traditional MD, and in several cases alternative REST2-like enhanced sampling methods, are shown to fail. The hydration free energy for acetic acid was shown to be independent of the starting conformation, and the values for four additional edge case molecules from the FreeSolv database were shown to have a significantly closer agreement with experiment using ACES. The method was further able to handle different rotamer states in a Cdk2 ligand identified as fractionally occupied in crystal structures. Finally, ACES was applied to T4-lysozyme and demonstrated that the side chain distribution of V111χ1 could be reliably reproduced for the apo state, bound to p-xylene, and in p-xylene→ benzene transformations. In these cases, the ACES method is shown to be highly robust and superior to a REST2-like enhanced sampling implementation alone.
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Affiliation(s)
- Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Hsu-Chun Tsai
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Abir Ganguly
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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26
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Tsai HC, Lee TS, Ganguly A, Giese TJ, Ebert MCCJC, Labute P, Merz KM, York DM. AMBER Free Energy Tools: A New Framework for the Design of Optimized Alchemical Transformation Pathways. J Chem Theory Comput 2023; 19:10.1021/acs.jctc.2c00725. [PMID: 36622640 PMCID: PMC10329732 DOI: 10.1021/acs.jctc.2c00725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
We develop a framework for the design of optimized alchemical transformation pathways in free energy simulations using nonlinear mixing and a new functional form for so-called "softcore" potentials. We describe the implementation and testing of this framework in the GPU-accelerated AMBER software suite. The new optimized alchemical transformation pathways integrate a number of important features, including (1) the use of smoothstep functions to stabilize behavior near the transformation end points, (2) consistent power scaling of Coulomb and Lennard-Jones (LJ) interactions with unitless control parameters to maintain balance of electrostatic attractions and exchange repulsions, (3) pairwise form based on the LJ contact radius for the effective interaction distance with separation-shifted scaling, and (4) rigorous smoothing of the potential at the nonbonded cutoff boundary. The new softcore potential form is combined with smoothly transforming nonlinear λ weights for mixing specific potential energy terms, along with flexible λ-scheduling features, to enable robust and stable alchemical transformation pathways. The resulting pathways are demonstrated and tested, and shown to be superior to the traditional methods in terms of numerical stability and minimal variance of the free energy estimates for all cases considered. The framework presented here can be used to design new alchemical enhanced sampling methods, and leveraged in robust free energy workflows for large ligand data sets.
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Affiliation(s)
- Hsu-Chun Tsai
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Abir Ganguly
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Maximilian CCJC Ebert
- Congruence Therapeutics, 7171 Rue Frederick Banting #117, Saint-Laurent, Quebec, Canada H4S 1Z9
| | - Paul Labute
- Chemical Computing Group ULC, 910-1010 Sherbrooke West, Montreal, Quebec, Canada H3A 2R7
| | - Kenneth M. Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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27
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Sun Y, Zhao B, Wang Y, Chen Z, Zhang H, Qu L, Zhao Y, Song J. Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy. Phys Chem Chem Phys 2022; 24:29940-29951. [PMID: 36468652 DOI: 10.1039/d2cp03681a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.
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Affiliation(s)
- Yujia Sun
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Bodi Zhao
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Yuqi Wang
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Zitong Chen
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Huaiyu Zhang
- Institute of Computational Quantum Chemistry, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang, Hebei, 050024, P. R. China
| | - Lingbo Qu
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
| | - Yuan Zhao
- The Key Laboratory of Natural Medicine and Immuno - Engineering, Henan University, Kaifeng, Henan, 475000, P. R. China
| | - Jinshuai Song
- Green Catalysis Center, and College of Chemistry, Zhengzhou University, No. 100 Science Avenue, Zhengzhou, Henan, 450001, P. R. China.
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28
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Ganguly A, Tsai HC, Fernández-Pendás M, Lee TS, Giese TJ, York DM. AMBER Drug Discovery Boost Tools: Automated Workflow for Production Free-Energy Simulation Setup and Analysis (ProFESSA). J Chem Inf Model 2022; 62:6069-6083. [PMID: 36450130 PMCID: PMC9881431 DOI: 10.1021/acs.jcim.2c00879] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
We report an automated workflow for production free-energy simulation setup and analysis (ProFESSA) using the GPU-accelerated AMBER free-energy engine with enhanced sampling features and analysis tools, part of the AMBER Drug Discovery Boost package that has been integrated into the AMBER22 release. The workflow establishes a flexible, end-to-end pipeline for performing alchemical free-energy simulations that brings to bear technologies, including new enhanced sampling features and analysis tools, to practical drug discovery problems. ProFESSA provides the user with top-level control of large sets of free-energy calculations and offers access to the following key functionalities: (1) automated setup of file infrastructure; (2) enhanced conformational and alchemical sampling with the ACES method; and (3) network-wide free-energy analysis with the optional imposition of cycle closure and experimental constraints. The workflow is applied to perform absolute and relative solvation free-energy and relative ligand-protein binding free-energy calculations using different atom-mapping procedures. Results demonstrate that the workflow is internally consistent and highly robust. Further, the application of a new network-wide Lagrange multiplier constraint analysis that imposes key experimental constraints substantially improves binding free-energy predictions.
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Affiliation(s)
- Abir Ganguly
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Hsu-Chun Tsai
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Mario Fernández-Pendás
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA,Donostia International Physics Center (DIPC), PK 1072, 20080 Donostia-San Sebastian, Spain
| | - Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA,
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29
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Henry SP, Liosi ME, Ippolito JA, Menges F, Newton AS, Schlessinger J, Jorgensen WL. Covalent Modification of the JH2 Domain of Janus Kinase 2. ACS Med Chem Lett 2022; 13:1819-1826. [PMID: 36385940 PMCID: PMC9661697 DOI: 10.1021/acsmedchemlett.2c00414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/18/2022] [Indexed: 11/28/2022] Open
Abstract
Probe molecules that covalently modify the JAK2 pseudokinase domain (JH2) are reported. Selective targeting of JH2 domains over the kinase (JH1) domains is a necessary feature for ligands intended to evaluate JH2 domains as therapeutic targets. The JH2 domains of three Janus kinases (JAK1, JAK2, and TYK2) possess a cysteine residue in the catalytic loop that does not occur in their JH1 domains. Starting from a non-selective kinase binding molecule, computer-aided design directed attachment of substituents terminating in acrylamide warheads to modify Cys675 of JAK2 JH2. Successful covalent attachment was demonstrated first through observation of enhanced binding with increasing incubation time in fluorescence polarization experiments. Covalent binding also increased selectivity to as much as ca. 30-fold for binding the JAK2 JH2 domain over the JH1 domain after a 20-h incubation. Covalency was confirmed through HPLC electrospray quadrupole time-of-flight HRMS experiments, which revealed the expected mass shifts.
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Affiliation(s)
- Sean P. Henry
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Maria-Elena Liosi
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Joseph A. Ippolito
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Fabian Menges
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Ana S. Newton
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Joseph Schlessinger
- Department
of Pharmacology, Yale University School
of Medicine, New Haven, Connecticut 06520-8066, United States
| | - William L. Jorgensen
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
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30
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Spiriti J, Noé F, Wong CF. Simulation of ligand dissociation kinetics from the protein kinase PYK2. J Comput Chem 2022; 43:1911-1922. [PMID: 36073605 PMCID: PMC9976590 DOI: 10.1002/jcc.26991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/11/2022] [Accepted: 08/03/2022] [Indexed: 11/09/2022]
Abstract
Early-stage drug discovery projects often focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. The kinetics of drug binding are ignored but can have significant influence on drug efficacy. Therefore, increasing attention has been paid on evaluating drug-binding kinetics early in a drug discovery process. Simulating drug-binding kinetics at the atomic level is challenging for the long time scale involved. Here, we used the transition-based reweighting analysis method (TRAM) with the Markov state model to study the dissociation of a ligand from the protein kinase PYK2. TRAM combines biased and unbiased simulations to reduce computational costs. This work used the umbrella sampling technique for the biased simulations. Although using the potential of mean force from umbrella sampling simulations with the transition-state theory over-estimated the dissociation rate by three orders of magnitude, TRAM gave a dissociation rate within an order of magnitude of the experimental value.
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Affiliation(s)
- Justin Spiriti
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany,Department of Physics, Freie Universität Berlin, Berlin, Germany
| | - Chung F. Wong
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA
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31
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Bieniek MK, Cree B, Pirie R, Horton JT, Tatum NJ, Cole DJ. An open-source molecular builder and free energy preparation workflow. Commun Chem 2022; 5:136. [PMID: 36320862 PMCID: PMC9607723 DOI: 10.1038/s42004-022-00754-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 10/11/2022] [Indexed: 01/27/2023] Open
Abstract
Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.
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Affiliation(s)
- Mateusz K. Bieniek
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Ben Cree
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Rachael Pirie
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Joshua T. Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Natalie J. Tatum
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
| | - Daniel J. Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
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32
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Mudedla SK, Braka A, Wu S. Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules. Front Mol Biosci 2022; 9:1002535. [PMID: 36304919 PMCID: PMC9592901 DOI: 10.3389/fmolb.2022.1002535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/15/2022] [Indexed: 11/28/2022] Open
Abstract
Force fields for drug-like small molecules play an essential role in molecular dynamics simulations and binding free energy calculations. In particular, the accurate generation of partial charges on small molecules is critical to understanding the interactions between proteins and drug-like molecules. However, it is a time-consuming process. Thus, we generated a force field for small molecules and employed a machine learning (ML) model to rapidly predict partial charges on molecules in less than a minute of time. We performed density functional theory (DFT) calculation for 31770 small molecules that covered the chemical space of drug-like molecules. The partial charges for the atoms in a molecule were predicted using an ML model trained on DFT-based atomic charges. The predicted values were comparable to the charges obtained from DFT calculations. The ML model showed high accuracy in the prediction of atomic charges for external test data sets. We also developed neural network (NN) models to assign atom types, phase angles and periodicities. All the models performed with high accuracy on test data sets. Our code calculated all the descriptors that were needed for the prediction of force field parameters and produced topologies for small molecules by combining results from ML and NN models. To assess the accuracy of the predicted force field parameters, we calculated solvation free energies for small molecules, and the results were in close agreement with experimental free energies. The AI-generated force field was effective in the fast and accurate generation of partial charges and other force field parameters for small drug-like molecules.
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Affiliation(s)
| | | | - Sangwook Wu
- R&D Center, PharmCADD, Busan, South Korea
- Department of Physics, Pukyong National University, Busan, South Korea
- *Correspondence: Sangwook Wu,
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33
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Kaplan AL, Confair DN, Kim K, Barros-Álvarez X, Rodriguiz RM, Yang Y, Kweon OS, Che T, McCorvy JD, Kamber DN, Phelan JP, Martins LC, Pogorelov VM, DiBerto JF, Slocum ST, Huang XP, Kumar JM, Robertson MJ, Panova O, Seven AB, Wetsel AQ, Wetsel WC, Irwin JJ, Skiniotis G, Shoichet BK, Roth BL, Ellman JA. Bespoke library docking for 5-HT 2A receptor agonists with antidepressant activity. Nature 2022; 610:582-591. [PMID: 36171289 PMCID: PMC9996387 DOI: 10.1038/s41586-022-05258-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 08/22/2022] [Indexed: 01/11/2023]
Abstract
There is considerable interest in screening ultralarge chemical libraries for ligand discovery, both empirically and computationally1-4. Efforts have focused on readily synthesizable molecules, inevitably leaving many chemotypes unexplored. Here we investigate structure-based docking of a bespoke virtual library of tetrahydropyridines-a scaffold that is poorly sampled by a general billion-molecule virtual library but is well suited to many aminergic G-protein-coupled receptors. Using three inputs, each with diverse available derivatives, a one pot C-H alkenylation, electrocyclization and reduction provides the tetrahydropyridine core with up to six sites of derivatization5-7. Docking a virtual library of 75 million tetrahydropyridines against a model of the serotonin 5-HT2A receptor (5-HT2AR) led to the synthesis and testing of 17 initial molecules. Four of these molecules had low-micromolar activities against either the 5-HT2A or the 5-HT2B receptors. Structure-based optimization led to the 5-HT2AR agonists (R)-69 and (R)-70, with half-maximal effective concentration values of 41 nM and 110 nM, respectively, and unusual signalling kinetics that differ from psychedelic 5-HT2AR agonists. Cryo-electron microscopy structural analysis confirmed the predicted binding mode to 5-HT2AR. The favourable physical properties of these new agonists conferred high brain permeability, enabling mouse behavioural assays. Notably, neither had psychedelic activity, in contrast to classic 5-HT2AR agonists, whereas both had potent antidepressant activity in mouse models and had the same efficacy as antidepressants such as fluoxetine at as low as 1/40th of the dose. Prospects for using bespoke virtual libraries to sample pharmacologically relevant chemical space will be considered.
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Affiliation(s)
- Anat Levit Kaplan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | | | - Kuglae Kim
- Department of Pharmacology, University of North Carolina, Chapel Hill School of Medicine, Chapel Hill, NC, USA
- Department of Pharmacy, Yonsei University, Incheon, Republic of Korea
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ramona M Rodriguiz
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC, USA
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
| | - Oh Sang Kweon
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - Tao Che
- Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St Louis, MO, USA
| | - John D McCorvy
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA
| | - David N Kamber
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - James P Phelan
- Department of Chemistry, Yale University, New Haven, CT, USA
| | - Luan Carvalho Martins
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA
- Biochemistry Department, Institute for Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Vladimir M Pogorelov
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Jeffrey F DiBerto
- Department of Pharmacology, University of North Carolina, Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Samuel T Slocum
- Department of Pharmacology, University of North Carolina, Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Xi-Ping Huang
- National Institute of Mental Health Psychoactive Drug Screening Program, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Jain Manish Kumar
- Department of Pharmacology, University of North Carolina, Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Michael J Robertson
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ouliana Panova
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alpay B Seven
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Autumn Q Wetsel
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - William C Wetsel
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA.
- Mouse Behavioral and Neuroendocrine Analysis Core Facility, Duke University Medical Center, Durham, NC, USA.
- Department of Cell Biology, Duke University Medical Center, Durham, NC, USA.
- Department of Neurobiology, Duke University Medical Center, Durham, NC, USA.
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA.
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA.
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina, Chapel Hill School of Medicine, Chapel Hill, NC, USA.
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina Chapel Hill, Chapel Hill, NC, USA.
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34
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Shen C, Zhang X, Deng Y, Gao J, Wang D, Xu L, Pan P, Hou T, Kang Y. Boosting Protein-Ligand Binding Pose Prediction and Virtual Screening Based on Residue-Atom Distance Likelihood Potential and Graph Transformer. J Med Chem 2022; 65:10691-10706. [PMID: 35917397 DOI: 10.1021/acs.jmedchem.2c00991] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The past few years have witnessed enormous progress toward applying machine learning approaches to the development of protein-ligand scoring functions. However, the robust performance and wide applicability of scoring functions remain a big challenge for increasing the success rate of docking-based virtual screening. Herein, a novel scoring function named RTMScore was developed by introducing a tailored residue-based graph representation strategy and several graph transformer layers for the learning of protein and ligand representations, followed by a mixture density network to obtain residue-atom distance likelihood potential. Our approach was resolutely validated on the CASF-2016 benchmark, and the results indicate that RTMScore can outperform almost all of the other state-of-the-art methods in terms of both the docking and screening powers. Further evaluation confirms the robustness of our approach that can not only retain its docking power on cross-docked poses but also achieve improved performance as a rescoring tool in larger-scale virtual screening.
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Affiliation(s)
- Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China.,CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang 310018, China
| | - Junbo Gao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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35
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Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
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36
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Balasubramani SG, Schwartz SD. Transition Path Sampling Based Calculations of Free Energies for Enzymatic Reactions: The Case of Human Methionine Adenosyl Transferase and Plasmodium vivax Adenosine Deaminase. J Phys Chem B 2022; 126:5413-5420. [PMID: 35830574 PMCID: PMC9444332 DOI: 10.1021/acs.jpcb.2c03251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Transition path sampling (TPS) is widely used for the calculations of reaction rates, transition state structures, and reaction coordinates of condensed phase systems. Here we discuss a scheme for the calculation of free energies using the ensemble of TPS reactive trajectories in combination with a window-based sampling technique for enzyme-catalyzed reactions. We calculate the free energy profiles of the reactions catalyzed by the human methionine S-adenosyltransferase (MAT2A) enzyme and the Plasmodium vivax adenosine deaminase (pvADA) enzyme to assess the accuracy of this method. MAT2A catalyzes the formation of S-adenosine-l-methionine following a SN2 mechanism, and using our method, we estimate the free energy barrier for this reaction to be 16 kcal mol-1, which is in excellent agreement with the experimentally measured activation energy of 17.27 kcal mol-1. Furthermore, for the pvADA enzyme-catalyzed reaction we estimate a free energy barrier of 21 kcal mol-1, and the calculated free energy profile is similar to that predicted from experimental observations. Calculating free energies by employing our simple method within TPS provides significant advantages over methods such as umbrella sampling because it is free from any applied external bias, is accurate compared to experimental measurements, and has a reasonable computational cost.
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Affiliation(s)
- Sree Ganesh Balasubramani
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721, United States
| | - Steven D Schwartz
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721, United States
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37
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La Serra MA, Vidossich P, Acquistapace I, Ganesan AK, De Vivo M. Alchemical Free Energy Calculations to Investigate Protein-Protein Interactions: the Case of the CDC42/PAK1 Complex. J Chem Inf Model 2022; 62:3023-3033. [PMID: 35679463 PMCID: PMC9241073 DOI: 10.1021/acs.jcim.2c00348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
![]()
Here, we show that
alchemical free energy calculations can quantitatively
compute the effect of mutations at the protein–protein interface.
As a test case, we have used the protein complex formed by the small
Rho-GTPase CDC42 and its downstream effector PAK1, a serine/threonine
kinase. Notably, the CDC42/PAK1 complex offers a wealth of structural,
mutagenesis, and binding affinity data because of its central role
in cellular signaling and cancer progression. In this context, we
have considered 16 mutations in the CDC42/PAK1 complex and obtained
excellent agreement between computed and experimental data on binding
affinity. Importantly, we also show that a careful analysis of the
side-chain conformations in the mutated amino acids can considerably
improve the computed estimates, solving issues related to sampling
limitations. Overall, this study demonstrates that alchemical free
energy calculations can conveniently be integrated into the design
of experimental mutagenesis studies.
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Affiliation(s)
- Maria Antonietta La Serra
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
| | - Pietro Vidossich
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
| | - Isabella Acquistapace
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
| | - Anand K Ganesan
- Department of Dermatology, University of California, Irvine, Irvine, California 92697, United States.,Department of Biological Chemistry, University of California, Irvine, Irvine, California 92697, United States
| | - Marco De Vivo
- Laboratory of Molecular Modeling and Drug Discovery, Istituto Italiano di Tecnologia, via Morego 30, Genoa 16163, Italy
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38
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Liosi ME, Ippolito JA, Henry SP, Krimmer SG, Newton AS, Cutrona KJ, Olivarez RA, Mohanty J, Schlessinger J, Jorgensen WL. Insights on JAK2 Modulation by Potent, Selective, and Cell-Permeable Pseudokinase-Domain Ligands. J Med Chem 2022; 65:8380-8400. [PMID: 35653642 PMCID: PMC9939005 DOI: 10.1021/acs.jmedchem.2c00283] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
JAK2 is a non-receptor tyrosine kinase that regulates hematopoiesis through the JAK-STAT pathway. The pseudokinase domain (JH2) is an important regulator of the activity of the kinase domain (JH1). V617F mutation in JH2 has been associated with the pathogenesis of various myeloproliferative neoplasms, but JAK2 JH2 has been poorly explored as a pharmacological target. In light of this, we aimed to develop JAK2 JH2 binders that could selectively target JH2 over JH1 and test their capacity to modulate JAK2 activity in cells. Toward this goal, we optimized a diaminotriazole lead compound into potent, selective, and cell-permeable JH2 binders leveraging computational design, synthesis, binding affinity measurements for the JH1, JH2 WT, and JH2 V617F domains, permeability measurements, crystallography, and cell assays. Optimized diaminotriazoles are capable of inhibiting STAT5 phosphorylation in both WT and V617F JAK2 in cells.
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Affiliation(s)
- Maria-Elena Liosi
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA
| | | | - Sean P. Henry
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA
| | - Stefan G. Krimmer
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA,Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520-8066, USA
| | - Ana S. Newton
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA
| | - Kara J. Cutrona
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA
| | - Rene A. Olivarez
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA
| | - Jyotidarsini Mohanty
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520-8066, USA
| | - Joseph Schlessinger
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520-8066, USA
| | - William L. Jorgensen
- Department of Chemistry, Yale University, New Haven, CT 06520-8107, USA,Corresponding author. William L. Jorgensen.
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39
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Oshima H, Sugita Y. Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions. J Chem Inf Model 2022; 62:2846-2856. [PMID: 35639709 DOI: 10.1021/acs.jcim.1c01532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.
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Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.,Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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40
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Zhu W, Luo J, White AD. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. PATTERNS (NEW YORK, N.Y.) 2022; 3:100521. [PMID: 35755872 PMCID: PMC9214329 DOI: 10.1016/j.patter.2022.100521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/15/2022] [Accepted: 05/06/2022] [Indexed: 12/04/2022]
Abstract
Chemistry research has both high material and computational costs to conduct experiments. Intuitions are interested in differing classes of molecules, creating heterogeneous data that cannot be easily joined by conventional methods. This work introduces federated heterogeneous molecular learning. Federated learning allows end users to build a global model collaboratively while keeping their training data isolated. We first simulate a heterogeneous federated-learning benchmark (FedChem) by jointly performing scaffold splitting and latent Dirichlet allocation on existing datasets. Our results on FedChem show that significant learning challenges arise when working with heterogeneous molecules across clients. We then propose a method to alleviate the problem: Federated Learning by Instance reweighTing (FLIT(+)). FLIT(+) can align local training across clients. Experiments conducted on FedChem validate the advantages of this method. This work should enable a new type of collaboration for improving artificial intelligence (AI) in chemistry that mitigates concerns about sharing valuable chemical data.
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Affiliation(s)
- Wei Zhu
- Department of Computer Science, University of Rochester, Rochester, NY, USA
| | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, USA
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
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41
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Yang Z, Xu J, Du L, Yin J, Wang Z, Yi F, Duan L, Li Z, Wang B, Shu K, Tan W. Design, Synthesis, and Action Mechanism of 1,3-Benzodioxole Derivatives as Potent Auxin Receptor Agonists and Root Growth Promoters. FRONTIERS IN PLANT SCIENCE 2022; 13:902902. [PMID: 35755644 PMCID: PMC9226723 DOI: 10.3389/fpls.2022.902902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 05/12/2022] [Indexed: 06/15/2023]
Abstract
Deeper and longer roots allow crops to survive and flourish, but our understanding of the plant growth regulators promoting root system establishment is limited. Here, we report that, a novel auxin receptor agonist, named K-10, had a remarkable promotive effect on root growth in both Arabidopsis thaliana and Oryza sativa through the enhancement of root-related signaling responses. Using computer-aided drug discovery approaches, we developed potent lead compound by screening artificial chemicals on the basis of the auxin receptor TIR1 (Transport Inhibitor Response 1), and a series of N-(benzo[d] [1,3] dioxol-5-yl)-2-(one-benzylthio) acetamides, K-1 to K-22, were designed and synthesized. The results of bioassay showed that K-10 exhibited an excellent root growth-promoting activity far exceeding that of NAA (1-naphthylacetic acid). A further morphological investigation of the auxin related mutants (yucQ, tir1) revealed that K-10 had auxin-like physiological functions and was recognized by TIR1, and K-10 significantly enhanced auxin response reporter's (DR5:GUS) transcriptional activity. Consistently, transcriptome analysis showed that K-10 induced a common transcriptional response with auxin and down-regulated the expression of root growth-inhibiting genes. Further molecular docking analysis revealed that K-10 had a stronger binding ability with TIR1 than NAA. These results indicated that this class of derivatives could be a promising scaffold for the discovery and development of novel auxin receptor agonists, and the employment of K-10 may be effective for enhancing root growth and crop production.
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Affiliation(s)
- Zhikun Yang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- College of Pharmaceutical Science & Green Pharmaceutical Collaborative Innovation Center of Yangtze River Del-ta Region, Zhejiang University of Technology, Hangzhou, China
| | - Jiahui Xu
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- School of Ecology and Environment, Northwestern Polytechnical University, Xi’an, China
| | - Lin Du
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Jiaming Yin
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Zhao Wang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Fei Yi
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Liusheng Duan
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Zhaohu Li
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Baomin Wang
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Kai Shu
- School of Ecology and Environment, Northwestern Polytechnical University, Xi’an, China
| | - Weiming Tan
- College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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42
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Liang L, Liu H, Xing G, Deng C, Hua Y, Gu R, Lu T, Chen Y, Zhang Y. Accurate calculation of absolute free energy of binding for SHP2 allosteric inhibitors using free energy perturbation. Phys Chem Chem Phys 2022; 24:9904-9920. [PMID: 35416820 DOI: 10.1039/d2cp00405d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Accurate prediction of binding affinity is a primary objective in structure-based drug discovery. A free energy perturbation (FEP) method based on molecular dynamics simulation shows great promise for protein-ligand binding affinity predictions. However, accurate calculation of binding affinity for allosteric inhibitors remains unknown and elusive, which hampers the discovery of allosteric inhibitors. Allosteric inhibitors exhibit several significant advantages over orthosteric inhibitors including higher specificity and lower side effects. Allosteric inhibitors against SHP2 are thought to be beneficial not only for diseases related to metabolism, but also for cancer, which make SHP2 a potential drug target. However, high structural sensitivity makes structural optimization of SHP2 allosteric inhibitors face challenges. Herein, we calculated the absolute binding free energy of SHP2 allosteric inhibitors using the FEP method by employing different λ-windows/simulation time sampling strategies. A simulation run with 32 λ-windows/64 ps sampling strategy delivered an excellent correlation (r = 0.96) and an unprecedented low mean absolute error of 0.5 kcal mol-1 between predicted binding free energies and experimental ones, outperforming the MM/PBSA method. Our study demonstrates the possibility to accurately calculate the absolute binding free energy of allosteric inhibitors using FEP, which offers exciting prospects for the discovery of more effective allosteric inhibitors.
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Affiliation(s)
- Li Liang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Guomeng Xing
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Chenglong Deng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Yi Hua
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Rui Gu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Tao Lu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China. .,State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
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43
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Bai Q, Liu S, Tian Y, Xu T, Banegas‐Luna AJ, Pérez‐Sánchez H, Huang J, Liu H, Yao X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1581] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China
| | - Shuo Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Yanan Tian
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Tingyang Xu
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Antonio Jesús Banegas‐Luna
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Horacio Pérez‐Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Junzhou Huang
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Huanxiang Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China
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44
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Henry SP, Liosi ME, Ippolito JA, Cutrona KJ, Krimmer SG, Newton AS, Schlessinger J, Jorgensen WL. Conversion of a False Virtual Screen Hit into Selective JAK2 JH2 Domain Binders Using Convergent Design Strategies. ACS Med Chem Lett 2022; 13:819-826. [PMID: 35586418 PMCID: PMC9109162 DOI: 10.1021/acsmedchemlett.2c00051] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/19/2022] [Indexed: 12/12/2022] Open
Abstract
The Janus kinase 2 (JAK2) pseudokinase domain (JH2) is an ATP-binding domain that regulates the activity of the catalytic tyrosine kinase domain (JH1). Dysregulation of JAK2 JH1 signaling caused by the V617F mutation in JH2 is implicated in various myeloproliferative neoplasms. To explore if JAK2 activity can be modulated by a small molecule binding to the ATP site in JH2, we have developed several ligand series aimed at selectively targeting the JAK2 JH2 domain. We report here the evolution of a false virtual screen hit into a new JAK2 JH2 series. Optimization guided by computational modeling has yielded analogues with nanomolar affinity for the JAK2 JH2 domain and >100-fold selectivity for the JH2 domain over the JH1 domain. A crystal structure for one of the potent compounds bound to JAK2 JH2 clarifies the origins of the strong binding and selectivity. The compounds expand the platform for seeking molecules to regulate JAK2 signaling, including V617F JAK2 hyperactivation.
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Affiliation(s)
- Sean P. Henry
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Maria-Elena Liosi
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Joseph A. Ippolito
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Kara J. Cutrona
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Stefan G. Krimmer
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States,Department
of Pharmacology, Yale University School
of Medicine, New Haven, Connecticut 06520-8066, United States
| | - Ana S. Newton
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States
| | - Joseph Schlessinger
- Department
of Pharmacology, Yale University School
of Medicine, New Haven, Connecticut 06520-8066, United States
| | - William L. Jorgensen
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States,
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45
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Parameterization and Application of the General Amber Force Field to Model Fluoro Substituted Furanose Moieties and Nucleosides. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092616. [PMID: 35565967 PMCID: PMC9101125 DOI: 10.3390/molecules27092616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/07/2022] [Accepted: 04/14/2022] [Indexed: 11/16/2022]
Abstract
Molecular mechanics force field calculations have historically shown significant limitations in modeling the energetic and conformational interconversions of highly substituted furanose rings. This is primarily due to the gauche effect that is not easily captured using pairwise energy potentials. In this study, we present a refinement to the set of torsional parameters in the General Amber Force Field (gaff) used to calculate the potential energy of mono, di-, and gem-fluorinated nucleosides. The parameters were optimized to reproduce the pseudorotation phase angle and relative energies of a diverse set of mono- and difluoro substituted furanose ring systems using quantum mechanics umbrella sampling techniques available in the IpolQ engine in the Amber suite of programs. The parameters were developed to be internally consistent with the gaff force field and the TIP3P water model. The new set of angle and dihedral parameters and partial charges were validated by comparing the calculated phase angle probability to those obtained from experimental nuclear magnetic resonance experiments.
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46
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Gallagher T, Niwetmarin W. Oxidative Fragmentation of Cytisine as an Entry to the Bis(piperidine) Scaffold of Virgidivarine. HETEROCYCLES 2022. [DOI: 10.3987/com-22-s(r)21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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47
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Azimi S, Wu JZ, Khuttan S, Kurtzman T, Deng N, Gallicchio E. Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge. J Comput Aided Mol Des 2022; 36:63-76. [PMID: 35059940 PMCID: PMC8982563 DOI: 10.1007/s10822-021-00437-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/01/2021] [Indexed: 01/23/2023]
Abstract
We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with R values of 0.89 (ATM) and 0.83 (PMF). These results were ranked among the best of the SAMPL8 GDCC challenge and second only to those obtained with the more accurate AMOEBA force field. Interestingly, the two host molecules included in the challenge (TEMOA and TEETOA) displayed distinct binding mechanisms, with TEMOA undergoing a dehydration transition whereas guest binding to TEETOA resulted in the opening of the binding cavity that remains essentially dry during the process. The coupled reorganization and hydration equilibria observed in these systems is a useful prototype for the study of these phenomena often observed in the formation of protein-ligand complexes. Given that the two free energy methods employed here are based on entirely different thermodynamic pathways, the close agreement between the two and their general agreement with the experimental binding free energies are a testament to the high quality and precision achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and paves the way to further extensions of the method to more complex systems.
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Affiliation(s)
- Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Joe Z. Wu
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Tom Kurtzman
- Department of Chemistry, Lehman College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York,PhD Program in Chemistry, Graduate Center of the City University of New York,PhD Program in Biochemistry, Graduate Center of the City University of New York
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48
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Verma S, Pathak RK. Discovery and optimization of lead molecules in drug designing. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00004-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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49
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Santos JLS, Bezerra KS, Barbosa ED, Pereira ACL, Meurer YSR, Oliveira JIN, Gavioli EC, Fulco UL. In silico analysis of energy interactions between nociceptin/orfanin FQ receptor and two antagonists with potential antidepressive action. NEW J CHEM 2022. [DOI: 10.1039/d2nj00916a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This study addresses the binding energies of NOPR-ligand complexes and presents the main amino acid residues involved in the interaction between these complexes.
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Affiliation(s)
- J. L. S. Santos
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
| | - K. S. Bezerra
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
| | - E. D. Barbosa
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
| | - A. C. L. Pereira
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
| | - Y. S. R. Meurer
- Departamento de Psicologia, Universidade Federal da Paraíba, 58051-900, João Pessoa-PB, Brazil
| | - J. I. N. Oliveira
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
| | - E. C. Gavioli
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
| | - U. L. Fulco
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande do Norte, 59072-970, Natal-RN, Brazil
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50
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Andrianov GV, Ong WJG, Serebriiskii I, Karanicolas J. Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging. J Chem Inf Model 2021; 61:5967-5987. [PMID: 34762402 PMCID: PMC8865965 DOI: 10.1021/acs.jcim.1c00630] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In early-stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly identified active compound and improving its potency or other properties. Traditionally, this process relies on synthesizing and evaluating a series of analogues to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogues with improved potency. Our protocol begins from an inhibitor of the target kinase and generalizes the synthetic route used to access it. By searching for commercially available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions─or billions─of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into single new compounds and which are not. Further, the use of pairwise approximation allows interaction energies to be assigned to each compound in the library without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFRT790M, and ACK1. In each example, the enumerated library includes additional analogues reported by the original study to have activity, and these analogues are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.
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Affiliation(s)
- Grigorii V. Andrianov
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia, 420008
| | - Wern Juin Gabriel Ong
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,Bowdoin College, Brunswick, ME 04011
| | - Ilya Serebriiskii
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia, 420008
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,To whom correspondence should be addressed. , 215-728-7067
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