1
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Cleves AE, Tandon H, Jain AN. Structure-based pose prediction: Non-cognate docking extended to macrocyclic ligands. J Comput Aided Mol Des 2024; 38:33. [PMID: 39414633 DOI: 10.1007/s10822-024-00574-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Accepted: 09/27/2024] [Indexed: 10/18/2024]
Abstract
So-called "cross-docking" is the prediction of the bound configuration of small-molecule ligands that differ from the cognate ligand of a protein co-crystal structure. This is a much more challenging problem than re-docking the cognate ligand, particularly when the new ligand is structurally dissimilar from prior known ones. We have updated the previously introduced PINC ("PINC Is Not Cognate") benchmark which introduced the idea of temporal segregation to measure cross-docking performance. The temporal set encompasses 846 future ligands for ten targets based on information from the earliest 25% of X-ray co-crystal structures known for each target. Here, we extend the benchmark to include thirteen targets where the bound poses of 128 macrocyclic ligands are to be predicted based on knowledge from structures of bound non-macrocyclic ligands. Performance was roughly equivalent for both the temporally-split non-macrocyclic ligand set and the macrocycle prediction set. Using standard and fully automatic protocols for the Surflex-Dock and ForceGen methods, across the combined 974 non-macrocyclic and macrocyclic ligands, the top-scoring pose family was correct 68% of the time, with the top-two pose families achieving a 79% success rate. Correct poses among all those predicted were identified 92% of the time. These success rates far exceeded those observed for the alternative methods AutoDock Vina and Gnina on both sets.
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Affiliation(s)
- Ann E Cleves
- BioPharmics Division, Optibrium Limited, Cambridge, CB25 9PB, UK
| | - Himani Tandon
- Research Department, Optibrium Limited, Cambridge, CB25 9PB, UK
| | - Ajay N Jain
- BioPharmics Division, Optibrium Limited, Cambridge, CB25 9PB, UK.
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2
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Wahl J. PheSA: An Open-Source Tool for Pharmacophore-Enhanced Shape Alignment. J Chem Inf Model 2024; 64:5944-5953. [PMID: 39092495 DOI: 10.1021/acs.jcim.4c00516] [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: 08/04/2024]
Abstract
PheSA is an open-source pharmacophore- and shape-based screening and molecular alignment tool that is fully open-source as part of OpenChemLib. Supporting standard ligand-based screening, flexible refinement of alignments, and receptor-guided shape docking, PheSA is a very flexible tool and can be used for different use cases in structure-based drug design. We present the algorithm and different benchmark studies that investigate the screening performance and also the quality of the generated alignments and the pose prediction performance of the receptor-guided PheSA algorithm. An important finding is the effect of the type of similarity metric used for measuring screening enrichment (symmetric Tanimoto versus asymmetric Tversky), whereby we could observe improved enrichment rates by using Tversky. PheSA exhibits enrichments on the DUD-E that are on par with commercial methods.
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Affiliation(s)
- Joel Wahl
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd, Hegenheimermattweg 91, CH-4123 Allschwil, Switzerland
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3
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Ni B, Wang H, Khalaf HKS, Blay V, Houston DR. AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening. J Chem Inf Model 2024; 64:3779-3789. [PMID: 38624083 PMCID: PMC11094722 DOI: 10.1021/acs.jcim.4c00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024]
Abstract
Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.
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Affiliation(s)
- Boyang Ni
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
| | - Haoying Wang
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
| | - Huda Kadhim Salem Khalaf
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
| | - Vincent Blay
- Department
of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, California 95064, United States
| | - Douglas R. Houston
- Institute
for Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh EH9 3BF, U.K.
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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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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: 10] [Impact Index Per Article: 10.0] [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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6
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Hönig SMN, Lemmen C, Rarey M. Small molecule superposition: A comprehensive overview on pose scoring of the latest methods. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Sophia M. N. Hönig
- ZBH ‐ Center for Bioinformatics Universität Hamburg Hamburg Germany
- BioSolveIT Sankt Augustin Germany
| | | | - Matthias Rarey
- ZBH ‐ Center for Bioinformatics Universität Hamburg Hamburg Germany
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7
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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8
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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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9
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Mapping of Protein Binding Sites using clustering algorithms development of a pharmacophore based drug discovery tool. J Mol Graph Model 2022; 115:108228. [DOI: 10.1016/j.jmgm.2022.108228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/28/2022] [Accepted: 05/19/2022] [Indexed: 11/22/2022]
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10
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Bolcato G, Heid E, Boström J. On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. J Chem Inf Model 2022; 62:1388-1398. [PMID: 35271260 PMCID: PMC8965872 DOI: 10.1021/acs.jcim.1c01535] [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: 11/28/2022]
Abstract
![]()
Multiparameter optimization,
the heart of drug design, is still
an open challenge. Thus, improved methods for automated compound design
with multiple controlled properties are desired. Here, we present
a significant extension to our previously described fragment-based
reinforcement learning method (DeepFMPO) for the generation of novel
molecules with optimal properties. As before, the generative process
outputs optimized molecules similar to the input structures, now with
the improved feature of replacing parts of these molecules with fragments
of similar three-dimensional (3D) shape and electrostatics. We developed
and benchmarked a new python package, ESP-Sim, for the comparison
of the electrostatic potential and the molecular shape, allowing the
calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**)
obtained using the quantum chemistry program Psi4. By performing comparisons
of 3D fragments, we can simulate 3D properties while overcoming the
notoriously difficult step of accurately describing bioactive conformations.
The new improved generative (DeepFMPO v3D) method is demonstrated
with a scaffold-hopping exercise identifying CDK2 bioisosteres. The
code is open-source and freely available.
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Affiliation(s)
- Giovanni Bolcato
- Molecular Modeling Section, University of Padova, 35131 Padova, Italy
| | - Esther Heid
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, 02139 Massachusetts, United States
| | - Jonas Boström
- Medicinal Chemistry, Early CVRM, BioPharmaceuticals R&D, AstraZeneca, 431 50 Mölndal, Sweden
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11
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Suresh CH, Remya GS, Anjalikrishna PK. Molecular electrostatic potential analysis: A powerful tool to interpret and predict chemical reactivity. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1601] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Cherumuttathu H. Suresh
- Chemical Sciences and Technology Division CSIR‐National Institute for Interdisciplinary Science and Technology Thiruvananthapuram Kerala India
- Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India
| | - Geetha S. Remya
- Chemical Sciences and Technology Division CSIR‐National Institute for Interdisciplinary Science and Technology Thiruvananthapuram Kerala India
- Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India
| | - Puthannur K. Anjalikrishna
- Chemical Sciences and Technology Division CSIR‐National Institute for Interdisciplinary Science and Technology Thiruvananthapuram Kerala India
- Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India
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12
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Ajjarapu SM, Tiwari A, Ramteke PW, Singh DB, Kumar S. Ligand-based drug designing. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00018-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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13
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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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14
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Giangreco I, Mukhopadhyay A, Cole JC. Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods. J Chem Inf Model 2021; 61:5841-5852. [PMID: 34792345 DOI: 10.1021/acs.jcim.1c00866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.
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Affiliation(s)
- Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Abhik Mukhopadhyay
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U.K
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15
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Kroth H, Oden F, Serra AM, Molette J, Mueller A, Berndt M, Capotosti F, Gabellieri E, Schmitt-Willich H, Hickman D, Pfeifer A, Dinkelborg L, Stephens A. Structure-activity relationship around PI-2620 highlights the importance of the nitrogen atom position in the tricyclic core. Bioorg Med Chem 2021; 52:116528. [PMID: 34839158 DOI: 10.1016/j.bmc.2021.116528] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 10/19/2022]
Abstract
Tau aggregates represent a critical pathology in Alzheimer's disease (AD) and other forms of dementia. The extent of Tau neurofibrillary tangles across defined brain regions corresponds well to the observed level of cognitive decline in AD. Compound 1 (PI-2620) was recently identified as a promising Tau positron emission tomography tracer for AD and non-AD tauopathies. To evaluate the impact of the N-atom position with respect to Tau- and off-target binding, tricyclic core analogs of PI-2620 with nitrogen atoms at different positions were prepared. Affinity to aggregated Tau was evaluated using human AD brain homogenates, and their off-target binding was evaluated in a monoamine oxidase A (MAO-A) competition assay. The novel tricyclic core derivatives all displayed inferior Tau binding or MAO-A off-target selectivity, indicating PI-2620 to be the optimal design for high affinity binding to Tau and high MAO-A selectivity.
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Affiliation(s)
- Heiko Kroth
- AC Immune SA, EPFL Innovation Park, Building B, 1015 Lausanne, Switzerland.
| | - Felix Oden
- Life Molecular Imaging GmbH, Tegeler Strasse 6-7, 13353 Berlin, Germany
| | | | - Jerome Molette
- AC Immune SA, EPFL Innovation Park, Building B, 1015 Lausanne, Switzerland
| | - Andre Mueller
- Life Molecular Imaging GmbH, Tegeler Strasse 6-7, 13353 Berlin, Germany
| | - Mathias Berndt
- Life Molecular Imaging GmbH, Tegeler Strasse 6-7, 13353 Berlin, Germany
| | | | | | | | - David Hickman
- AC Immune SA, EPFL Innovation Park, Building B, 1015 Lausanne, Switzerland
| | - Andrea Pfeifer
- AC Immune SA, EPFL Innovation Park, Building B, 1015 Lausanne, Switzerland
| | - Ludger Dinkelborg
- Life Molecular Imaging GmbH, Tegeler Strasse 6-7, 13353 Berlin, Germany
| | - Andrew Stephens
- Life Molecular Imaging GmbH, Tegeler Strasse 6-7, 13353 Berlin, Germany
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16
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Abstract
Stochastic computing is an emerging scientific field pushed by the need for developing high-performance artificial intelligence systems in hardware to quickly solve complex data processing problems. This is the case of virtual screening, a computational task aimed at searching across huge molecular databases for new drug leads. In this work, we show a classification framework in which molecules are described by an energy-based vector. This vector is then processed by an ultra-fast artificial neural network implemented through FPGA by using stochastic computing techniques. Compared to other previously published virtual screening methods, this proposal provides similar or higher accuracy, while it improves processing speed by about two or three orders of magnitude.
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17
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Chen X, Zhao Z, Luo J, Wu T, Shen Y, Chang S, Wan S, Li Z, Zhang J, Pang J, Tian Y. Novel natural scaffold as hURAT1 inhibitor identified by 3D-shape-based, docking-based virtual screening approach and biological evaluation. Bioorg Chem 2021; 117:105444. [PMID: 34775203 DOI: 10.1016/j.bioorg.2021.105444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/13/2022]
Abstract
As a promising therapeutic target for gout, hURAT1 has attracted increasing attention. In this work, we identified a novel scaffold of hURAT1 inhibitors from a personal natural product database of verified herb-treated gout. First, we constructed more than 800 natural compounds from Chinese medicine that were verified to treat gout. Following the application of both shape-based and docking-based virtual screening (VS) methods, taking into account the shape similarity and flexibility of the target, we identified isopentenyl dihydroflavones that might inhibit hURAT1. Specifically, 9 compounds with commercial availability were tested with biochemical assays for the inhibition of 14C-uric acid uptake in high-expression hURAT1 cells (HEK293-hURAT1), and their structure-activity relationship was evaluated. As a result, 8-isopentenyl dihydroflavone was identified as a novel scaffold of hURAT1 inhibitors since isobavachin (DHF3) inhibited hURAT1 with an IC50 value of 0.39 ± 0.17 μM, which was comparable to verinurad with an IC50 value of 0.32 ± 0.23 μM. Remarkably, isobavachin also displayed an eminent effect in the decline of serum uric acid in vivo experiments. Taken together, isobavachin is a promising candidate for the treatment of hyperuricemia and gout.
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Affiliation(s)
- Xinhua Chen
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Zean Zhao
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jiajun Luo
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Ting Wu
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Yudong Shen
- College of Food Sciences, South China Agricultural University, Guangzhou 510642, People's Republic of China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information, Engineering, Jiangsu University of Technology, Changzhou 213001, People's Republic of China
| | - Shanhe Wan
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Zhonghuang Li
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China
| | - Jiajie Zhang
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China.
| | - Jianxin Pang
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China.
| | - Yuanxin Tian
- Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, People's Republic of China.
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18
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Current Status of Baricitinib as a Repurposed Therapy for COVID-19. Pharmaceuticals (Basel) 2021; 14:ph14070680. [PMID: 34358107 PMCID: PMC8308612 DOI: 10.3390/ph14070680] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/08/2021] [Accepted: 07/11/2021] [Indexed: 12/23/2022] Open
Abstract
The emergence of the COVID-19 pandemic has mandated the instant (re)search for potential drug candidates. In response to the unprecedented situation, it was recognized early that repurposing of available drugs in the market could timely save lives, by skipping the lengthy phases of preclinical and initial safety studies. BenevolentAI’s large knowledge graph repository of structured medical information suggested baricitinib, a Janus-associated kinase inhibitor, as a potential repurposed medicine with a dual mechanism; hindering SARS-CoV2 entry and combatting the cytokine storm; the leading cause of mortality in COVID-19. However, the recently-published Adaptive COVID-19 Treatment Trial-2 (ACTT-2) positioned baricitinib only in combination with remdesivir for treatment of a specific category of COVID-19 patients, whereas the drug is not recommended to be used alone except in clinical trials. The increased pace of data output in all life sciences fields has changed our understanding of data processing and manipulation. For the purpose of drug design, development, or repurposing, the integration of different disciplines of life sciences is highly recommended to achieve the ultimate benefit of using new technologies to mine BIG data, however, the final say remains to be concluded after the drug is used in clinical practice. This review demonstrates different bioinformatics, chemical, pharmacological, and clinical aspects of baricitinib to highlight the repurposing journey of the drug and evaluates its placement in the current guidelines for COVID-19 treatment.
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19
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Schindler O, Raček T, Maršavelski A, Koča J, Berka K, Svobodová R. Optimized SQE atomic charges for peptides accessible via a web application. J Cheminform 2021; 13:45. [PMID: 34193251 PMCID: PMC8243439 DOI: 10.1186/s13321-021-00528-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/18/2021] [Indexed: 12/03/2022] Open
Abstract
Background Partial atomic charges find many applications in computational chemistry, chemoinformatics, bioinformatics, and nanoscience. Currently, frequently used methods for charge calculation are the Electronegativity Equalization Method (EEM), Charge Equilibration method (QEq), and Extended QEq (EQeq). They all are fast, even for large molecules, but require empirical parameters. However, even these advanced methods have limitations—e.g., their application for peptides, proteins, and other macromolecules is problematic. An empirical charge calculation method that is promising for peptides and other macromolecular systems is the Split-charge Equilibration method (SQE) and its extension SQE+q0. Unfortunately, only one parameter set is available for these methods, and their implementation is not easily accessible. Results In this article, we present for the first time an optimized guided minimization method (optGM) for the fast parameterization of empirical charge calculation methods and compare it with the currently available guided minimization (GDMIN) method. Then, we introduce a further extension to SQE, SQE+qp, adapted for peptide datasets, and compare it with the common approaches EEM, QEq EQeq, SQE, and SQE+q0. Finally, we integrate SQE and SQE+qp into the web application Atomic Charge Calculator II (ACC II), including several parameter sets. Conclusion The main contribution of the article is that it makes SQE methods with their parameters accessible to the users via the ACC II web application (https://acc2.ncbr.muni.cz) and also via a command-line application. Furthermore, our improvement, SQE+qp, provides an excellent solution for peptide datasets. Additionally, optGM provides comparable parameters to GDMIN in a markedly shorter time. Therefore, optGM allows us to perform parameterizations for charge calculation methods with more parameters (e.g., SQE and its extensions) using large datasets. Graphic Abstract ![]()
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Affiliation(s)
- Ondřej Schindler
- CEITEC-Central European Institute of Technology, Masaryk University, Kamenice 5, 602 00, Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Tomáš Raček
- CEITEC-Central European Institute of Technology, Masaryk University, Kamenice 5, 602 00, Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.,Faculty of Informatics, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic
| | - Aleksandra Maršavelski
- Division of Biochemistry, Department of Chemistry, Faculty of Science, University of Zagreb, Horvatovac 102a, 10000, Zagreb, Croatia
| | - Jaroslav Koča
- CEITEC-Central European Institute of Technology, Masaryk University, Kamenice 5, 602 00, Brno, Czech Republic.,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic
| | - Karel Berka
- Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc, 17. listopadu 1192/12, 771 46, Olomouc, Czech Republic
| | - Radka Svobodová
- CEITEC-Central European Institute of Technology, Masaryk University, Kamenice 5, 602 00, Brno, Czech Republic. .,National Centre for Biomolecular Research, Faculty of Science, Masaryk University, Kamenice 5, 625 00, Brno, Czech Republic.
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20
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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21
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Molecular fingerprints based on Jacobi expansions of electron densities. Theor Chem Acc 2021. [DOI: 10.1007/s00214-020-02708-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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22
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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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23
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Singh N, Chaput L, Villoutreix BO. Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces. J Chem Inf Model 2020; 60:3910-3934. [PMID: 32786511 DOI: 10.1021/acs.jcim.0c00545] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.
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Affiliation(s)
- Natesh Singh
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France
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24
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Raschka S, Kaufman B. Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Methods 2020; 180:89-110. [PMID: 32645448 PMCID: PMC8457393 DOI: 10.1016/j.ymeth.2020.06.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 06/23/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023] Open
Abstract
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
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Affiliation(s)
- Sebastian Raschka
- University of Wisconsin-Madison, Department of Statistics, United States.
| | - Benjamin Kaufman
- University of Wisconsin-Madison, Department of Biostatistics and Medical Informatics, United States
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25
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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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