1
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Cleves AE, Jain AN, Demeter DA, Buchan ZA, Wilmot J, Hancock EN. From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product. J Comput Aided Mol Des 2024; 38:19. [PMID: 38630341 DOI: 10.1007/s10822-024-00555-3] [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: 01/02/2024] [Accepted: 02/26/2024] [Indexed: 04/19/2024]
Abstract
Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most informative based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.
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Affiliation(s)
- Ann E Cleves
- BioPharmics Division, Optibrium Limited, Cambridge, CB25 9GL, UK.
| | - Ajay N Jain
- BioPharmics Division, Optibrium Limited, Cambridge, CB25 9GL, UK
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2
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Dal Poggetto G, DiCaprio A, Reibarkh M, Cohen RD. Ultra-clean pure shift NMR with optimal water suppression for analysis of aqueous pharmaceutical samples. Analyst 2024; 149:2227-2231. [PMID: 38517550 DOI: 10.1039/d3an02150e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Pure shift NMR experiments greatly enhance spectral resolution by collapsing multiplet structures into singlets and, with water suppression, can be used for aqueous samples. Here, we combine ultra-clean pure-shift NMR (SAPPHIRE) with two different internally encoded water suppression schemes to achieve optimal performance for small molecule and macrocyclic peptide pharmaceuticals in water and acetonitrile-water mixtures.
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Affiliation(s)
| | - Adam DiCaprio
- Merck & Co., Inc., 770 Sumneytown Pike, 19846, West Point, PA, USA
| | - Mikhail Reibarkh
- Merck & Co., Inc., 126 East Lincoln Avenue, 07065, Rahway, NJ, USA.
| | - Ryan D Cohen
- Merck & Co., Inc., 126 East Lincoln Avenue, 07065, Rahway, NJ, USA.
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3
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Robin X, Studer G, Durairaj J, Eberhardt J, Schwede T, Walters WP. Assessment of protein-ligand complexes in CASP15. Proteins 2023; 91:1811-1821. [PMID: 37795762 DOI: 10.1002/prot.26601] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.
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Affiliation(s)
- Xavier Robin
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gabriel Studer
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jerome Eberhardt
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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4
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Jain AN, Brueckner AC, Jorge C, Cleves AE, Khandelwal P, Cortes JC, Mueller L. Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design. J Comput Aided Mol Des 2023; 37:519-535. [PMID: 37535171 PMCID: PMC10505130 DOI: 10.1007/s10822-023-00524-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 08/04/2023]
Abstract
Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.
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Affiliation(s)
- Ajay N Jain
- Research and Development, BioPharmics LLC, Sonoma County, CA, USA.
| | | | | | - Ann E Cleves
- Research and Development, BioPharmics LLC, Sonoma County, CA, USA
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5
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Seidel T, Permann C, Wieder O, Kohlbacher SM, Langer T. High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment. J Chem Inf Model 2023; 63:5549-5570. [PMID: 37624145 PMCID: PMC10498443 DOI: 10.1021/acs.jcim.3c00563] [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: 04/13/2023] [Indexed: 08/26/2023]
Abstract
Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.
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Affiliation(s)
- Thomas Seidel
- Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Christian Permann
- NeGeMac
Research Platform, Department of Pharmaceutical Sciences, Division
of Pharmaceutical Chemistry, University
of Vienna, Josef-Holaubek-Platz
2, 1090 Vienna, Austria
| | - Oliver Wieder
- Christian
Doppler Laboratory for Molecular Informatics in the Biosciences, Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Stefan M. Kohlbacher
- Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Thierry Langer
- Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
- NeGeMac
Research Platform, Department of Pharmaceutical Sciences, Division
of Pharmaceutical Chemistry, University
of Vienna, Josef-Holaubek-Platz
2, 1090 Vienna, Austria
- Christian
Doppler Laboratory for Molecular Informatics in the Biosciences, Department
of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
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6
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Ramelot TA, Palmer J, Montelione GT, Bhardwaj G. Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design. Curr Opin Struct Biol 2023; 80:102603. [PMID: 37178478 PMCID: PMC10923192 DOI: 10.1016/j.sbi.2023.102603] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/05/2023] [Indexed: 05/15/2023]
Abstract
Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.
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Affiliation(s)
- Theresa A Ramelot
- Department of Chemistry and Chemical Biology and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Jonathan Palmer
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Gaurav Bhardwaj
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA; Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA.
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7
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Jain AN, Brueckner AC, Cleves AE, Reibarkh M, Sherer EC. A Distributional Model of Bound Ligand Conformational Strain: From Small Molecules up to Large Peptidic Macrocycles. J Med Chem 2023; 66:1955-1971. [PMID: 36701387 PMCID: PMC9923749 DOI: 10.1021/acs.jmedchem.2c01744] [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] [Indexed: 01/27/2023]
Abstract
The internal conformational strain incurred by ligands upon binding a target site has a critical impact on binding affinity, and expectations about the magnitude of ligand strain guide conformational search protocols. Estimates for bound ligand strain begin with modeled ligand atomic coordinates from X-ray co-crystal structures. By deriving low-energy conformational ensembles to fit X-ray diffraction data, calculated strain energies are substantially reduced compared with prior approaches. We show that the distribution of expected global strain energy values is dependent on molecular size in a superlinear manner. The distribution of strain energy follows a rectified normal distribution whose mean and variance are related to conformational complexity. The modeled strain distribution closely matches calculated strain values from experimental data comprising over 3000 protein-ligand complexes. The distributional model has direct implications for conformational search protocols as well as for directions in molecular design.
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Affiliation(s)
- Ajay N. Jain
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States,
| | - Alexander C. Brueckner
- Molecular
Structure & Design, Bristol Myers Squibb, Princeton, New Jersey08543, United States
| | - Ann E. Cleves
- Research
& Development, BioPharmics LLC, Sonoma County, California95404, United States
| | - Mikhail Reibarkh
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States
| | - Edward C. Sherer
- Analytical
Research and Development, Merck & Co.
Inc., Rahway, New Jersey07065, United States,
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8
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Eastwood JRB, Jiang L, Bonneau R, Kirshenbaum K, Renfrew PD. Evaluating the Conformations and Dynamics of Peptoid Macrocycles. J Phys Chem B 2022; 126:5161-5174. [PMID: 35820178 DOI: 10.1021/acs.jpcb.2c01669] [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
Peptoid macrocycles are versatile and chemically diverse peptidomimetic oligomers. However, the conformations and dynamics of these macrocycles have not been evaluated comprehensively and require extensive further investigation. Recent studies indicate that two degrees of freedom, and four distinct conformations, adequately describe the behavior of each monomer backbone unit in most peptoid oligomers. On the basis of this insight, we conducted molecular dynamics simulations of model macrocycles using an exhaustive set of idealized possible starting conformations. Simulations of various sizes of peptoid macrocycles yielded a limited set of populated conformations. In addition to reproducing all relevant experimentally determined conformations, the simulations accurately predicted a cyclo-octamer conformation for which we now present the first experimental observation. Sets of three adjacent dihedral angles (ϕi, ψi, ωi+1) exhibited correlated crankshaft motions over the course of simulation for peptoid macrocycles of six residues and larger. These correlated motions may occur in the form of an inversion of one amide bond and the concerted rotation of the preceding ϕ and ψ angles to their mirror-image conformation, a variation on "crankshaft flip" motions studied in polymers and peptides. The energy landscape of these peptoid macrocycles can be described as a network of conformations interconnected by transformations of individual crankshaft flips. For macrocycles of up to eight residues, our mapping of the landscape is essentially complete.
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Affiliation(s)
- James R B Eastwood
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Linhai Jiang
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Richard Bonneau
- Center for Data Science, New York University, New York, New York 10011, United States.,Center for Computational Biology, Flatiron Institute, New York, New York 10010 United States
| | - Kent Kirshenbaum
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, New York, New York 10010 United States
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9
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Gao Q, Cleves AE, Wang X, Liu Y, Bowen S, Williamson RT, Jain AN, Sherer E, Reibarkh M. Solution cis-Proline Conformation of IPCs Inhibitor Aureobasidin A Elucidated via NMR-Based Conformational Analysis. JOURNAL OF NATURAL PRODUCTS 2022; 85:1449-1458. [PMID: 35622967 DOI: 10.1021/acs.jnatprod.1c01071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Aureobasidin A (abA) is a natural depsipeptide that inhibits inositol phosphorylceramide (IPC) synthases with significant broad-spectrum antifungal activity. abA is known to have two distinct conformations in solution corresponding to trans- and cis-proline (Pro) amide bond rotamers. While the trans-Pro conformation has been studied extensively, cis-Pro conformers have remained elusive. Conformational properties of cyclic peptides are known to strongly affect both potency and cell permeability, making a comprehensive characterization of abA conformation highly desirable. Here, we report a high-resolution 3D structure of the cis-Pro conformer of aureobasidin A elucidated for the first time using a recently developed NMR-driven computational approach. This approach utilizes ForceGen's advanced conformational sampling of cyclic peptides augmented by sparse distance and torsion angle constraints derived from NMR data. The obtained 3D conformational structure of cis-Pro abA has been validated using anisotropic residual dipolar coupling measurements. Support for the biological relevance of both the cis-Pro and trans-Pro abA configurations was obtained through molecular similarity experiments, which showed a significant 3D similarity between NMR-restrained abA conformational ensembles and another IPC synthase inhibitor, pleofungin A. Such ligand-based comparisons can further our understanding of the important steric and electrostatic characteristics of abA and can be utilized in the design of future therapeutics.
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Affiliation(s)
- Qi Gao
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Ann E Cleves
- Applied Science, BioPharmics LLC, Santa Rosa, California 95404, United States
| | - Xiao Wang
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Yizhou Liu
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Sean Bowen
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Robert Thomas Williamson
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Ajay N Jain
- Applied Science, BioPharmics LLC, Santa Rosa, California 95404, United States
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143, United States
| | - Edward Sherer
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Mikhail Reibarkh
- Analytical Research and Development, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
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10
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Wang S, Krummenacher K, Landrum GA, Sellers BD, Di Lello P, Robinson SJ, Martin B, Holden JK, Tom JYK, Murthy AC, Popovych N, Riniker S. Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry. J Chem Inf Model 2022; 62:472-485. [PMID: 35029985 DOI: 10.1021/acs.jcim.1c01165] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Nuclear magnetic resonance (NMR) data from NOESY (nuclear Overhauser enhancement spectroscopy) and ROESY (rotating frame Overhauser enhancement spectroscopy) experiments can easily be combined with distance geometry (DG) based conformer generators by modifying the molecular distance bounds matrix. In this work, we extend the modern DG based conformer generator ETKDG, which has been shown to reproduce experimental crystal structures from small molecules to large macrocycles well, to include NOE-derived interproton distances. In noeETKDG, the experimentally derived interproton distances are incorporated into the distance bounds matrix as loose upper (or lower) bounds to generate large conformer sets. Various subselection techniques can subsequently be applied to yield a conformer bundle that best reproduces the NOE data. The approach is benchmarked using a set of 24 (mostly) cyclic peptides for which NOE-derived distances as well as reference solution structures obtained by other software are available. With respect to other packages currently available, the advantages of noeETKDG are its speed and that no prior force-field parametrization is required, which is especially useful for peptides with unnatural amino acids. The resulting conformer bundles can be further processed with the use of structural refinement techniques to improve the modeling of the intramolecular nonbonded interactions. The noeETKDG code is released as a fully open-source software package available at www.github.com/rinikerlab/customETKDG.
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Affiliation(s)
- Shuzhe Wang
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Kajo Krummenacher
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Gregory A Landrum
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Benjamin D Sellers
- Department of Discovery Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Paola Di Lello
- Department of Structural Biology, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Sarah J Robinson
- Department of Discovery Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Bryan Martin
- Department of Structural Biology, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Jeffrey K Holden
- Department of Early Discovery Biochemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Jeffrey Y K Tom
- Department of Early Discovery Biochemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Anastasia C Murthy
- Department of Early Discovery Biochemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Nataliya Popovych
- Department of Early Discovery Biochemistry, Genentech, Inc., South San Francisco, California 94080, United States
| | - Sereina Riniker
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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11
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Cleves AE, Johnson SR, Jain AN. Synergy and Complementarity between Focused Machine Learning and Physics-Based Simulation in Affinity Prediction. J Chem Inf Model 2021; 61:5948-5966. [PMID: 34890185 DOI: 10.1021/acs.jcim.1c01382] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present results on the extent to which physics-based simulation (exemplified by FEP+) and focused machine learning (exemplified by QuanSA) are complementary for ligand affinity prediction. For both methods, predictions of activity for LFA-1 inhibitors from a medicinal chemistry lead optimization project were accurate within the applicable domain of each approach. A hybrid model that combined predictions by both approaches by simple averaging performed better than either method, with respect to both ranking and absolute pKi values. Two publicly available FEP+ benchmarks, covering 16 diverse biological targets, were used to test the generality of the synergy. By identifying training data specifically focused on relevant ligands, accurate QuanSA models were derived using ligand activity data known at the time of the original series publications. Results across the 16 benchmark targets demonstrated significant improvements both for ranking and for absolute pKi values using hybrid predictions that combined the FEP+ and QuanSA predicted affinity values. The results argue for a combined approach for affinity prediction that makes use of physics-driven methods as well as those driven by machine learning, each applied carefully on appropriate compounds, with hybrid prediction strategies being employed where possible.
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Affiliation(s)
- Ann E Cleves
- Applied Science, BioPharmics LLC, Santa Rosa, California 95404, United States
| | - Stephen R Johnson
- Computer-Assisted Drug-Design, Bristol-Myers Squibb Company, Princeton, New Jersey 08648, United States
| | - Ajay N Jain
- Research and Development, BioPharmics LLC, Santa Rosa, California 95404, United States
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12
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Adpressa DA, Reibarkh M, Jiang Y, Saurí J, Makarov AA. Interrogation of solution conformation of complex macrocyclic peptides utilizing a combined SEC-HDX-MS, circular dichroism, and NMR workflow. Analyst 2021; 147:325-332. [PMID: 34927633 DOI: 10.1039/d1an01619a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Recent technological and synthetic advances have led to a resurgence in the exploration of peptides as potential therapeutics. Understanding peptide conformation in both free and protein-bound states remains one of the most critical areas for successful development of peptide drugs. In this study it was demonstrated that the combination of Size-Exclusion Chromatography with Hydrogen-Deuterium Exchange Mass Spectrometry (SEC-HDX-MS) and Circular Dichroism Spectroscopy (CD) can be used to guide the selection of peptides for further NMR analysis. Moreover, the insights from this workflow guide the choice of the best biologically relevant conditions for NMR conformational studies of peptide ligands in a free state in solution. Combined information about solution conformation character and stability across temperatures and co-solvent compositions greatly expedites selection of optimal conditions for NMR analysis. In total, the combination of SEC-HDX-MS, CD, and NMR into a single complementary workflow greatly accelerates conformational analysis of peptides in the drug discovery lead optimization process.
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Affiliation(s)
- Donovon A Adpressa
- Analytical Research & Development, Merck & Co. Inc., Boston, Massachusetts 02115, USA.
| | - Mikhail Reibarkh
- Analytical Research & Development, Merck & Co. Inc., Rahway, New Jersey 07065, USA.
| | - Yuan Jiang
- Analytical Research & Development, Merck & Co. Inc., Boston, Massachusetts 02115, USA.
| | - Josep Saurí
- Analytical Research & Development, Merck & Co. Inc., Boston, Massachusetts 02115, USA.
| | - Alexey A Makarov
- Analytical Research & Development, Merck & Co. Inc., Boston, Massachusetts 02115, USA.
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13
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Zhu C, Xu B, Adpressa DA, Rudolf JD, Loesgen S. Discovery and Biosynthesis of a Structurally Dynamic Antibacterial Diterpenoid. Angew Chem Int Ed Engl 2021; 60:14163-14170. [PMID: 33780586 PMCID: PMC9247737 DOI: 10.1002/anie.202102453] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/07/2021] [Indexed: 01/07/2023]
Abstract
A new bicyclic diterpenoid, benditerpenoic acid, was isolated from soil-dwelling Streptomyces sp. (CL12-4). We sequenced the bacterial genome, identified the responsible biosynthetic gene cluster, verified the function of the terpene synthase, and heterologously produced the core diterpene. Comparative bioinformatics indicated this Streptomyces strain is phylogenetically unique and possesses nine terpene synthases. The absolute configurations of the new trans-fused bicyclo[8.4.0]tetradecanes were achieved by extensive spectroscopic analyses, including Mosher's analysis, J-based coupling analysis, and computations based on sparse NMR-derived experimental restraints. Interestingly, benditerpenoic acid exists in two distinct ring-flipped bicyclic conformations with a rotational barrier of ≈16 kcal mol-1 in solution. The diterpenes exhibit moderate antibacterial activity against Gram-positive bacteria including methicillin and multi-drug resistant Staphylococcus aureus. This is a rare example of an eunicellane-type diterpenoid from bacteria and the first identification of a diterpene synthase and biosynthetic gene cluster responsible for the construction of the eunicellane scaffold.
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Affiliation(s)
- Chenxi Zhu
- Department of Chemistry, Oregon State University, Corvallis, Oregon 97331, USA
- Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, Florida 32080, USA
| | - Baofu Xu
- Department of Chemistry, University of Florida, Gainesville, Florida, 32611, USA
| | - Donovon A. Adpressa
- Department of Analytical Research and Development, Merck & Co., Inc. Boston, MA 02115, USA
| | - Jeffrey D. Rudolf
- Department of Chemistry, University of Florida, Gainesville, Florida, 32611, USA
| | - Sandra Loesgen
- Department of Chemistry, Oregon State University, Corvallis, Oregon 97331, USA
- Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, Florida 32080, USA
- Department of Chemistry, University of Florida, Gainesville, Florida, 32611, USA
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14
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Entdeckung und Biosynthese eines strukturdynamischen antibakteriellen Diterpenoids. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202102453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Ru X, Lin Z. Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions. J Phys Chem B 2021; 125:3824-3829. [PMID: 33830761 DOI: 10.1021/acs.jpcb.1c01255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The computational determination of peptide conformations is a challenging task of finding minima in a high dimensional space. By combining the sampling efficiency of the genetic algorithm (GA) and the dimensionality reduction resulted from the backbone dihedral angle correlations, named as the path matrix (PM) method, a new searching algorithm, parallel microgenetic algorithm (PMGA), is proposed. Meanwhile, PMGA employs the density functional theory based energy as the fitness function and performs local geometry optimizations to enhance the reliability of its GA encoding strategy. Tests on peptides with up to eight amino-acid residues show PMGA is quite efficient for providing high-quality conformational coverages. The computational cost of the PMGA search increases slowly with the number of amino-acid residues in a peptide, with no sign of deterioration on the searching results for the increased length of the peptide. The PMGA method should therefore be useful for determining the conformations of oligopeptide, studying the protein-ligand interactions, and designing the peptide-based drugs.
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Affiliation(s)
- Xiao Ru
- Hefei National Research Center for Physical Sciences at Microscales & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei 230026, China
| | - Zijing Lin
- Hefei National Research Center for Physical Sciences at Microscales & CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei 230026, China
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16
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Brueckner AC, Deng Q, Cleves AE, Lesburg CA, Alvarez JC, Reibarkh MY, Sherer EC, Jain AN. Conformational Strain of Macrocyclic Peptides in Ligand-Receptor Complexes Based on Advanced Refinement of Bound-State Conformers. J Med Chem 2021; 64:3282-3298. [PMID: 33724820 DOI: 10.1021/acs.jmedchem.0c02159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Macrocyclic peptides are an important modality in drug discovery, but molecular design is limited due to the complexity of their conformational landscape. To better understand conformational propensities, global strain energies were estimated for 156 protein-macrocyclic peptide cocrystal structures. Unexpectedly large strain energies were observed when the bound-state conformations were modeled with positional restraints. Instead, low-energy conformer ensembles were generated using xGen that fit experimental X-ray electron density maps and gave reasonable strain energy estimates. The ensembles featured significant conformational adjustments while still fitting the electron density as well or better than the original coordinates. Strain estimates suggest the interaction energy in protein-ligand complexes can offset a greater amount of strain for macrocyclic peptides than for small molecules and non-peptidic macrocycles. Across all molecular classes, the approximate upper bound on global strain energies had the same relationship with molecular size, and bound-state ensembles from xGen yielded favorable binding energy estimates.
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Affiliation(s)
- Alexander C Brueckner
- Computational & Structural Chemistry, Merck & Co Inc, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Qiaolin Deng
- Computational & Structural Chemistry, Merck & Co Inc, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Ann E Cleves
- Bioengineering and Therapeutic Sciences, University of California San Francisco, Box 0128, San Francisco, California 94158, United States
| | - Charles A Lesburg
- Computational and Structural Chemistry, Merck and Co Inc, 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
| | - Juan C Alvarez
- Computational and Structural Chemistry, Merck and Co Inc, 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
| | - Mikhail Y Reibarkh
- Analytical Research and Development, Merck & Co Inc, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Edward C Sherer
- Analytical Research and Development, Merck & Co Inc, 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Ajay N Jain
- Bioengineering and Therapeutic Sciences, University of California San Francisco, Box 0128, San Francisco, California 94158, United States
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17
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Kelly CN, Townsend CE, Jain AN, Naylor MR, Pye CR, Schwochert J, Lokey RS. Geometrically Diverse Lariat Peptide Scaffolds Reveal an Untapped Chemical Space of High Membrane Permeability. J Am Chem Soc 2021; 143:705-714. [PMID: 33381960 PMCID: PMC8514148 DOI: 10.1021/jacs.0c06115] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Constrained, membrane-permeable peptides offer the possibility of engaging challenging intracellular targets. Structure-permeability relationships have been extensively studied in cyclic peptides whose backbones are cyclized from head to tail, like the membrane permeable and orally bioavailable natural product cyclosporine A. In contrast, the physicochemical properties of lariat peptides, which are cyclized from one of the termini onto a side chain, have received little attention. Many lariat peptide natural products exhibit interesting biological activities, and some, such as griselimycin and didemnin B, are membrane permeable and have intracellular targets. To investigate the structure-permeability relationships in the chemical space exemplified by these natural products, we generated a library of scaffolds using stable isotopes to encode stereochemistry and determined the passive membrane permeability of over 1000 novel lariat peptide scaffolds with molecular weights around 1000. Many lariats were surprisingly permeable, comparable to many known orally bioavailable drugs. Passive permeability was strongly dependent on N-methylation, stereochemistry, and ring topology. A variety of structure-permeability trends were observed including a relationship between alternating stereochemistry and high permeability, as well as a set of highly permeable consensus sequences. For the first time, robust structure-permeability relationships are established in synthetic lariat peptides exceeding 1000 compounds.
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Affiliation(s)
- Colin N. Kelly
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, USA
| | - Chad E. Townsend
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, USA
| | - Ajay N. Jain
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA
| | - Matthew R. Naylor
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, USA
| | | | | | - R. Scott Lokey
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, USA
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18
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Chen Y, Kirchmair J. Cheminformatics in Natural Product-based Drug Discovery. Mol Inform 2020; 39:e2000171. [PMID: 32725781 PMCID: PMC7757247 DOI: 10.1002/minf.202000171] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 07/28/2020] [Indexed: 12/20/2022]
Abstract
This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.
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Affiliation(s)
- Ya Chen
- Center for Bioinformatics (ZBH)Department of Computer ScienceFaculty of MathematicsInformatics and Natural SciencesUniversität Hamburg20146HamburgGermany
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH)Department of Computer ScienceFaculty of MathematicsInformatics and Natural SciencesUniversität Hamburg20146HamburgGermany
- Department of Pharmaceutical ChemistryFaculty of Life SciencesUniversity of Vienna1090ViennaAustria
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19
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Dubey A, Takeuchi K, Reibarkh M, Arthanari H. The role of NMR in leveraging dynamics and entropy in drug design. JOURNAL OF BIOMOLECULAR NMR 2020; 74:479-498. [PMID: 32720098 PMCID: PMC7686249 DOI: 10.1007/s10858-020-00335-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/11/2020] [Indexed: 05/03/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy has contributed to structure-based drug development (SBDD) in a unique way compared to the other biophysical methods. The potency of a ligand binding to a protein is dictated by the binding free energy, which is an intricate interplay between entropy and enthalpy. In addition to providing the atomic resolution structural information, NMR can help to identify protein-ligand interactions that potentially contribute to the enthalpic component of the free energy. NMR can also illuminate dynamic aspects of the interaction, which correspond to the entropic term of the free energy. The ability of NMR to access both terms in the free energy equation stems from the suite of experiments developed to shed light on various aspects that contribute to both entropy and enthalpy, deepening our understanding of the biological function of macromolecules and assisting to target them in physiological conditions. Here we provide a brief account of the contribution of NMR to SBDD, highlighting hallmark examples and discussing the challenges that demand further method development. In the era of integrated biology, the unique ability of NMR to directly ascertain structural and dynamical aspects of macromolecule and monitor changes in these properties upon engaging a ligand can be combined with computational and other structural and biophysical methods to provide a more complete picture of the energetics of drug engagement with the target. Such efforts can be used to engineer better drugs.
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Affiliation(s)
- Abhinav Dubey
- 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
| | - Koh Takeuchi
- Cellular and Molecular Biotechnology Research Institute & Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, 135-0064, Japan.
| | - Mikhail Reibarkh
- Analytical Research and Development, Merck & Co., Inc., Rahway, NJ, 07065, USA
| | - Haribabu Arthanari
- 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.
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20
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Jain AN, Cleves AE, Brueckner AC, Lesburg CA, Deng Q, Sherer EC, Reibarkh MY. XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X-ray Electron Density Maps. J Med Chem 2020; 63:10509-10528. [DOI: 10.1021/acs.jmedchem.0c01373] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ajay N. Jain
- Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143 United States
| | - Ann E. Cleves
- BioPharmics LLC, Santa Rosa, California 95404 United States
| | | | | | - Qiaolin Deng
- Merck and Co., Inc., Kenilworth, New Jersey 07033 United States
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21
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Singh N, Decroly E, Khatib AM, Villoutreix BO. Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages. Eur J Pharm Sci 2020; 153:105495. [PMID: 32730844 PMCID: PMC7384984 DOI: 10.1016/j.ejps.2020.105495] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 07/16/2020] [Accepted: 07/27/2020] [Indexed: 12/28/2022]
Abstract
In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177, F-59000 Lille, France
| | | | - Abdel-Majid Khatib
- Univ. Bordeaux, Allée Geoffroy St Hilaire, 33615 Pessac, France
- INSERM, LAMC, UMR 1029, Allée Geoffroy St Hilaire, 33615 Pessac, France
- Corresponding authors.
| | - Bruno O. Villoutreix
- Univ. Lille, INSERM, Institut Pasteur de Lille, U1177, F-59000 Lille, France
- Corresponding authors.
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22
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Hawkins PCD, Wlodek S. Decisions with Confidence: Application to the Conformation Sampling of Molecules in the Solid State. J Chem Inf Model 2020; 60:3518-3533. [DOI: 10.1021/acs.jcim.0c00358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Paul C. D. Hawkins
- OpenEye Scientific, 9 Bisbee Court, Suite D, Santa Fe, New Mexico 87508, United States
| | - Stanislaw Wlodek
- OpenEye Scientific, 9 Bisbee Court, Suite D, Santa Fe, New Mexico 87508, United States
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23
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Kamenik AS, Kraml J, Hofer F, Waibl F, Quoika PK, Kahler U, Schauperl M, Liedl KR. Macrocycle Cell Permeability Measured by Solvation Free Energies in Polar and Apolar Environments. J Chem Inf Model 2020; 60:3508-3517. [PMID: 32551643 PMCID: PMC7388155 DOI: 10.1021/acs.jcim.0c00280] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The relation of surface polarity and conformational preferences is decisive for cell permeability and thus bioavailability of macrocyclic drugs. Here, we employ grid inhomogeneous solvation theory (GIST) to calculate solvation free energies for a series of six macrocycles in water and chloroform as a measure of passive membrane permeability. We perform accelerated molecular dynamics simulations to capture a diverse structural ensemble in water and chloroform, allowing for a direct profiling of solvent-dependent conformational preferences. Subsequent GIST calculations facilitate a quantitative measure of solvent preference in the form of a transfer free energy, calculated from the ensemble-averaged solvation free energies in water and chloroform. Hence, the proposed method considers how the conformational diversity of macrocycles in polar and apolar solvents translates into transfer free energies. Following this strategy, we find a striking correlation of 0.92 between experimentally determined cell permeabilities and calculated transfer free energies. For the studied model systems, we find that the transfer free energy exceeds the purely water-based solvation free energies as a reliable estimate of cell permeability and that conformational sampling is imperative for a physically meaningful model. We thus recommend this purely physics-based approach as a computational tool to assess cell permeabilities of macrocyclic drug candidates.
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Affiliation(s)
- Anna S Kamenik
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Johannes Kraml
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Florian Hofer
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Franz Waibl
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Patrick K Quoika
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Ursula Kahler
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Michael Schauperl
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
| | - Klaus R Liedl
- Institute of General, Inorganic and Theoretical Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, Innsbruck A-6020 Austria
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24
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Cleves AE, Jain AN. Structure- and Ligand-Based Virtual Screening on DUD-E +: Performance Dependence on Approximations to the Binding Pocket. J Chem Inf Model 2020; 60:4296-4310. [PMID: 32271577 DOI: 10.1021/acs.jcim.0c00115] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Using the DUD-E+ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof. For both structure-based and ligand-based approaches, the precise characterization of the binding site in question had a significant impact on screening performance. Using the single original DUD-E protein, Surflex-Dock yielded mean ROC area of 0.81 ± 0.11. Using the cognate ligand instead, with the eSim method for screening, yielded 0.77 ± 0.14. Moving to ensembles of five protein pocket variants increased docking performance to 0.84 ± 0.09. Results for the analogous ligand-based approach (using the five crystallographically aligned cognate ligands) was 0.83 ± 0.11. Using the same ligands, but making use of an automatically generated mutual alignment, yielded mean AUC nearly as good as from single-structure docking: 0.80 ± 0.12. Detailed results and statistical analyses show that structure- and ligand-based methods are complementary and can be fruitfully combined to enhance screening efficiency. A hybrid approach combining ensemble docking with eSim-based screening produced the best and most consistent performance (mean ROC area of 0.89 ± 0.08 and 1% early enrichment of 46-fold). Based on results from both the docking and ligand-similarity approaches, it is clearly unwise to make use of a single arbitrarily chosen protein structure for docking or single ligand query for similarity-based screening.
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Affiliation(s)
- Ann E Cleves
- Applied Science, BioPharmics LLC, Santa Rosa, California 95404, United States
| | - Ajay N Jain
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143, United States
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25
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Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:149-162. [DOI: 10.1007/s10822-019-00244-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 10/30/2019] [Indexed: 12/20/2022]
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26
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Adebomi V, Cohen RD, Wills R, Chavers HAH, Martin GE, Raj M. CyClick Chemistry for the Synthesis of Cyclic Peptides. Angew Chem Int Ed Engl 2019; 58:19073-19080. [DOI: 10.1002/anie.201911900] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Indexed: 01/07/2023]
Affiliation(s)
- Victor Adebomi
- Department of Chemistry and Biochemistry Auburn University Auburn AL 36830 USA
| | - Ryan D. Cohen
- Analytical Research and Development Merck & Co. Inc. Rahway NJ 07065 USA
- Department of Chemistry & Biochemistry Seton Hall University South Orange NJ 07079 USA
| | - Rachel Wills
- Department of Chemistry and Biochemistry Auburn University Auburn AL 36830 USA
| | | | - Gary E. Martin
- Analytical Research and Development Merck & Co. Inc. Rahway NJ 07065 USA
- Department of Chemistry & Biochemistry Seton Hall University South Orange NJ 07079 USA
| | - Monika Raj
- Department of Chemistry and Biochemistry Auburn University Auburn AL 36830 USA
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27
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Adebomi V, Cohen RD, Wills R, Chavers HAH, Martin GE, Raj M. CyClick Chemistry for the Synthesis of Cyclic Peptides. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201911900] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Victor Adebomi
- Department of Chemistry and Biochemistry Auburn University Auburn AL 36830 USA
| | - Ryan D. Cohen
- Analytical Research and Development Merck & Co. Inc. Rahway NJ 07065 USA
- Department of Chemistry & Biochemistry Seton Hall University South Orange NJ 07079 USA
| | - Rachel Wills
- Department of Chemistry and Biochemistry Auburn University Auburn AL 36830 USA
| | | | - Gary E. Martin
- Analytical Research and Development Merck & Co. Inc. Rahway NJ 07065 USA
- Department of Chemistry & Biochemistry Seton Hall University South Orange NJ 07079 USA
| | - Monika Raj
- Department of Chemistry and Biochemistry Auburn University Auburn AL 36830 USA
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28
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Cleves AE, Johnson SR, Jain AN. Electrostatic-field and surface-shape similarity for virtual screening and pose prediction. J Comput Aided Mol Des 2019; 33:865-886. [PMID: 31650386 PMCID: PMC6856045 DOI: 10.1007/s10822-019-00236-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 10/11/2019] [Indexed: 02/04/2023]
Abstract
We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called “eSim”). Rather than employing heuristic “colors” or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p \documentclass[12pt]{minimal}
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\begin{document}$$< 10^{-9}$$\end{document}<10-9, by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD \documentclass[12pt]{minimal}
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\begin{document}$$\le 2.0$$\end{document}≤2.0 Å among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons.
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Affiliation(s)
- Ann E Cleves
- Applied Science, BioPharmics LLC, Santa Rosa, CA, USA
| | - Stephen R Johnson
- Computer-Assisted Drug-Design, Bristol-Myers Squibb, Co., Princeton, NJ, USA
| | - Ajay N Jain
- Dept. of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA.
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