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Zha J, Li M, Kong R, Lu S, Zhang J. Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques. J Mol Biol 2022; 434:167481. [DOI: 10.1016/j.jmb.2022.167481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/31/2022] [Indexed: 12/17/2022]
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2
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Chen G, Seukep AJ, Guo M. Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs. Mar Drugs 2020; 18:md18110545. [PMID: 33143025 PMCID: PMC7692358 DOI: 10.3390/md18110545] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/28/2022] Open
Abstract
Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.
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Affiliation(s)
- Guilin Chen
- Key Laboratory of Plant Germplasm Enhancement & Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; (G.C.); (A.J.S.)
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- Innovation Academy for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
| | - Armel Jackson Seukep
- Key Laboratory of Plant Germplasm Enhancement & Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; (G.C.); (A.J.S.)
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- Innovation Academy for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Buea, P.O. Box 63 Buea, Cameroon
| | - Mingquan Guo
- Key Laboratory of Plant Germplasm Enhancement & Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; (G.C.); (A.J.S.)
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- Innovation Academy for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
- Correspondence: ; Tel.: +86-27-8770-0850
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3
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Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem 2018; 235:1-8. [DOI: 10.1016/j.bpc.2018.01.004] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 01/18/2018] [Accepted: 01/26/2018] [Indexed: 01/10/2023]
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Brylinski M. Aromatic interactions at the ligand-protein interface: Implications for the development of docking scoring functions. Chem Biol Drug Des 2017; 91:380-390. [PMID: 28816025 DOI: 10.1111/cbdd.13084] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 06/29/2017] [Accepted: 08/11/2017] [Indexed: 12/22/2022]
Abstract
The ability to design and fine-tune non-covalent interactions between organic ligands and proteins is indispensable to rational drug development. Aromatic stacking has long been recognized as one of the key constituents of ligand-protein interfaces. In this communication, we employ a two-parameter geometric model to conduct a large-scale statistical analysis of aromatic contacts in the experimental and computer-generated structures of ligand-protein complexes, considering various combinations of aromatic amino acid residues and ligand rings. The geometry of interfacial π-π stacking in crystal structures accords with experimental and theoretical data collected for simple systems, such as the benzene dimer. Many contemporary ligand docking programs implicitly treat aromatic stacking with van der Waals and Coulombic potentials. Although this approach generally provides a sufficient specificity to model aromatic interactions, the geometry of π-π contacts in high-scoring docking conformations could still be improved. The comprehensive analysis of aromatic geometries at ligand-protein interfaces lies the foundation for the development of type-specific statistical potentials to more accurately describe aromatic interactions in molecular docking. A Perl script to detect and calculate the geometric parameters of aromatic interactions in ligand-protein complexes is available at https://github.com/michal-brylinski/earomatic. The dataset comprising experimental complex structures and computer-generated models is available at https://osf.io/rztha/.
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Affiliation(s)
- Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA
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5
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Štular T, Lešnik S, Rožman K, Schink J, Zdouc M, Ghysels A, Liu F, Aldrich CC, Haupt VJ, Salentin S, Daminelli S, Schroeder M, Langer T, Gobec S, Janežič D, Konc J. Discovery of Mycobacterium tuberculosis InhA Inhibitors by Binding Sites Comparison and Ligands Prediction. J Med Chem 2016; 59:11069-11078. [PMID: 27936766 DOI: 10.1021/acs.jmedchem.6b01277] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.
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Affiliation(s)
- Tanja Štular
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Samo Lešnik
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Kaja Rožman
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Julia Schink
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Mitja Zdouc
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - An Ghysels
- Center for Molecular Modeling, Ghent University , Technologiepark 903, 9052 Zwijnaarde, Belgium
| | - Feng Liu
- AAT Bioquest, Inc. , 520 Mercury Drive, Sunnyvale, California 94085, United States
| | - Courtney C Aldrich
- Department of Medicinal Chemistry, University of Minnesota , 308 Harvard Street Southeast, Minneapolis, Minnesota 55455, United States
| | - V Joachim Haupt
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Sebastian Salentin
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Simone Daminelli
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), Technische Universität Dresden , 01307 Dresden, Germany
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstrasse 14, A-1090 Vienna, Austria
| | - Stanislav Gobec
- Faculty of Pharmacy, University of Ljubljana , Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Dušanka Janežič
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
| | - Janez Konc
- National Institute of Chemistry , Hajdrihova 19, SI-1000 Ljubljana, Slovenia.,Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska , Glagoljaška 8, SI-6000 Koper, Slovenia
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6
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Assessing the similarity of ligand binding conformations with the Contact Mode Score. Comput Biol Chem 2016; 64:403-413. [PMID: 27620381 DOI: 10.1016/j.compbiolchem.2016.08.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 08/17/2016] [Accepted: 08/25/2016] [Indexed: 11/22/2022]
Abstract
Structural and computational biologists often need to measure the similarity of ligand binding conformations. The commonly used root-mean-square deviation (RMSD) is not only ligand-size dependent, but also may fail to capture biologically meaningful binding features. To address these issues, we developed the Contact Mode Score (CMS), a new metric to assess the conformational similarity based on intermolecular protein-ligand contacts. The CMS is less dependent on the ligand size and has the ability to include flexible receptors. In order to effectively compare binding poses of non-identical ligands bound to different proteins, we further developed the eXtended Contact Mode Score (XCMS). We believe that CMS and XCMS provide a meaningful assessment of the similarity of ligand binding conformations. CMS and XCMS are freely available at http://brylinski.cct.lsu.edu/content/contact-mode-score and http://geaux-computational-bio.github.io/contact-mode-score/.
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Fang Y, Ding Y, Feinstein WP, Koppelman DM, Moreno J, Jarrell M, Ramanujam J, Brylinski M. GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing. PLoS One 2016; 11:e0158898. [PMID: 27420300 PMCID: PMC4946785 DOI: 10.1371/journal.pone.0158898] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 06/23/2016] [Indexed: 12/19/2022] Open
Abstract
Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249.
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Affiliation(s)
- Ye Fang
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Yun Ding
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Wei P. Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - David M. Koppelman
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Juana Moreno
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Mark Jarrell
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - J. Ramanujam
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America
- Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America
- * E-mail:
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Poli G, Martinelli A, Tuccinardi T. Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies. J Enzyme Inhib Med Chem 2016; 31:167-173. [PMID: 27311630 DOI: 10.1080/14756366.2016.1193736] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.
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Affiliation(s)
- Giulio Poli
- a Department of Pharmacy , University of Pisa , Pisa , Italy
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