1
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Sakaguchi K, Okiyama Y, Tanaka S. In Silico Search for Drug Candidates Targeting the PAX8-PPARγ Fusion Protein in Thyroid Cancer. Int J Mol Sci 2024; 25:5347. [PMID: 38791384 PMCID: PMC11121424 DOI: 10.3390/ijms25105347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/05/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024] Open
Abstract
The PAX8/PPARγ rearrangement, producing the PAX8-PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson-Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.
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Affiliation(s)
| | - Yoshio Okiyama
- Graduate School of System Informatics, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Shigenori Tanaka
- Graduate School of System Informatics, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
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2
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Kudo G, Yanagisawa K, Yoshino R, Hirokawa T. AAp-MSMD: Amino Acid Preference Mapping on Protein-Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics. J Chem Inf Model 2023; 63:7768-7777. [PMID: 38085669 PMCID: PMC10751795 DOI: 10.1021/acs.jcim.3c01677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/26/2023]
Abstract
Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.
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Affiliation(s)
- Genki Kudo
- Physics
Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8571, Ibaraki Japan
| | - Keisuke Yanagisawa
- Department
of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro 152-8550, Tokyo Japan
- Middle
Molecule IT-based Drug Discovery Laboratory, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro 152-8550, Tokyo Japan
| | - Ryunosuke Yoshino
- Faculty
of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki Japan
- Transborder
Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki Japan
| | - Takatsugu Hirokawa
- Faculty
of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8575, Ibaraki Japan
- Transborder
Medical Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki Japan
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3
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Liao J, Wang Q, Wu F, Huang Z. In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets. Molecules 2022; 27:7103. [PMID: 36296697 PMCID: PMC9609013 DOI: 10.3390/molecules27207103] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/25/2022] [Indexed: 07/30/2023] Open
Abstract
Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.
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Affiliation(s)
- Jianbo Liao
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
- The Second School of Clinical Medicine, Guangdong Medical University, Dongguan 523808, China
| | - Qinyu Wang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
| | - Fengxu Wu
- Hubei Key Laboratory of Wudang Local Chinese Medicine Research, School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan 442000, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory of Computer-Aided Drug Design of Dongguan City, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
- Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang 524023, China
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4
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Radoux CJ, Vianello F, McGreig J, Desai N, Bradley AR. The druggable genome: Twenty years later. FRONTIERS IN BIOINFORMATICS 2022; 2:958378. [PMID: 36304325 PMCID: PMC9580872 DOI: 10.3389/fbinf.2022.958378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.
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5
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Baltrukevich H, Podlewska S. From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output. Front Pharmacol 2022; 13:844293. [PMID: 35359865 PMCID: PMC8960308 DOI: 10.3389/fphar.2022.844293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/26/2022] [Indexed: 12/02/2022] Open
Abstract
An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.
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Affiliation(s)
- Hanna Baltrukevich
- Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
- Faculty of Pharmacy, Chair of Technology and Biotechnology of Medical Remedies, Jagiellonian University Medical College in Krakow, Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
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6
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Goel H, Hazel A, Yu W, Jo S, MacKerell AD. Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design. NEW J CHEM 2022; 46:919-932. [PMID: 35210743 PMCID: PMC8863107 DOI: 10.1039/d1nj04028f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Anthony Hazel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States
| | - Sunhwan Jo
- SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St. Baltimore, Maryland 21201, United States., SilcsBio LLC, 1100 Wicomico St. Suite 323, Baltimore, MD, 21230, United States.,, Tel: 410-706-7442, Fax: 410-706-5017
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7
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Sabanés Zariquiey F, Jacoby E, Vos A, van Vlijmen HWT, Tresadern G, Harvey J. Divide and Conquer. Pocket-Opening Mixed-Solvent Simulations in the Perspective of Docking Virtual Screening Applications for Drug Discovery. J Chem Inf Model 2022; 62:533-543. [PMID: 35041430 DOI: 10.1021/acs.jcim.1c01164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The existence of a druggable binding pocket is a prerequisite for computational drug-target interaction studies including virtual screening. Retrospective studies have shown that extended sampling methods like Markov State Modeling and mixed-solvent simulations can identify cryptic pockets relevant for drug discovery. Here, we apply a combination of mixed-solvent molecular dynamics (MD) and time-structure independent component analysis (TICA) to four retrospective case studies: NPC2, the CECR2 bromodomain, TEM-1, and MCL-1. We compare previous experimental and computational findings to our results. It is shown that the successful identification of cryptic pockets depends on the system and the cosolvent probes. We used alternative TICA internal features such as the unbiased backbone coordinates or backbone dihedrals versus biased interatomic distances. We found that in the case of NPC2, TEM-1, and MCL-1, the use of unbiased features is able to identify cryptic pockets, although in the case of the CECR2 bromodomain, more specific features are required to properly capture a pocket opening. In the perspective of virtual screening applications, it is shown how docking studies with the parent ligands depend critically on the conformational state of the targets.
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Affiliation(s)
| | - Edgar Jacoby
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Ann Vos
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Herman W T van Vlijmen
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Jeremy Harvey
- Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
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8
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Szabó PB, Sabanés Zariquiey F, Nogueira JJ. Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations. J Chem Inf Model 2021; 61:5508-5523. [PMID: 34730967 PMCID: PMC8659376 DOI: 10.1021/acs.jcim.1c00924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Indexed: 11/30/2022]
Abstract
The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.
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Affiliation(s)
- P. Bernát Szabó
- Department
of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
- Department
of Chemistry, Universidad Autónoma
de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
| | | | - Juan J. Nogueira
- Department
of Chemistry, Universidad Autónoma
de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
- IADCHEM,
Institute for Advanced Research in Chemistry, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 7, 28049 Madrid, Spain
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10
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Tanaka S, Tokutomi S, Hatada R, Okuwaki K, Akisawa K, Fukuzawa K, Komeiji Y, Okiyama Y, Mochizuki Y. Dynamic Cooperativity of Ligand-Residue Interactions Evaluated with the Fragment Molecular Orbital Method. J Phys Chem B 2021; 125:6501-6512. [PMID: 34124906 DOI: 10.1021/acs.jpcb.1c03043] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
By the splendid advance in computation power realized with the Fugaku supercomputer, it has become possible to perform ab initio fragment molecular orbital (FMO) calculations for thousands of dynamic structures of protein-ligand complexes in a parallel way. We thus carried out electron-correlated FMO calculations for a complex of the 3C-like (3CL) main protease (Mpro) of the new coronavirus (SARS-CoV-2) and its inhibitor N3 incorporating the structural fluctuations sampled by classical molecular dynamics (MD) simulation in hydrated conditions. Along with a statistical evaluation of the interfragment interaction energies (IFIEs) between the N3 ligand and the surrounding amino-acid residues for 1000 dynamic structure samples, in this study we applied a novel approach based on principal component analysis (PCA) and singular value decomposition (SVD) to the analysis of IFIE data in order to extract the dynamically cooperative interactions between the ligand and the residues. We found that the relative importance of each residue is modified via the structural fluctuations and that the ligand is bound in the pharmacophore in a dynamic manner through collective interactions formed by multiple residues, thus providing new insight into structure-based drug discovery.
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Affiliation(s)
- Shigenori Tanaka
- Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Shusuke Tokutomi
- Graduate School of System Informatics, Department of Computational Science, Kobe University, 1-1 Rokkodai, Nada-ku, Kobe 657-8501, Japan
| | - Ryo Hatada
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Koji Okuwaki
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Kazuki Akisawa
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
| | - Kaori Fukuzawa
- School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2-4-41 Ebara, Shinagawa-ku, Tokyo 142-8501, Japan.,Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, 6-6-11 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan.,Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Yuto Komeiji
- Biomedical Research Institute, AIST, Tsukuba Central 6, Tsukuba, Ibaraki 305-8566, Japan
| | - Yoshio Okiyama
- Division of Medicinal Safety Science, National Institute of Health Sciences, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 201-9501, Japan
| | - Yuji Mochizuki
- Department of Chemistry and Research Center for Smart Molecules, Faculty of Science, Rikkyo University, 3-34-1 Nishi-ikebukuro, Toshima-ku, Tokyo 171-8501, Japan.,Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
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11
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Smith RD, Carlson HA. Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics. J Chem Inf Model 2021; 61:1287-1299. [PMID: 33599485 DOI: 10.1021/acs.jcim.0c01002] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Protein dynamics play an important role in small molecule binding and can pose a significant challenge in the identification of potential binding sites. Cryptic binding sites have been defined as sites which require significant rearrangement of the protein structure to become physically accessible to a ligand. Mixed-solvent MD (MixMD) is a computational protocol which maps the surface of the protein using molecular dynamics (MD) of the unbound protein solvated in a 5% box of probe molecules with explicit water. This method has successfully identified known active and allosteric sites which did not require reorganization. In this study, we apply the MixMD protocol to identify known cryptic sites of 12 proteins characterized by a wide range of conformational changes. Of these 12 proteins, three require reorganization of side chains, five require loop movements, and four require movement of more significant structures such as whole helices. In five cases, we find that standard MixMD simulations are able to map the cryptic binding sites with at least one probe type. In two cases (guanylate kinase and TIE-2), accelerated MD, which increases sampling of torsional angles, was necessary to achieve mapping of portions of the cryptic binding site missed by standard MixMD. For more complex systems where movement of a helix or domain is necessary, MixMD was unable to map the binding site even with accelerated dynamics, possibly due to the limited timescale (100 ns for individual simulations). In general, similar conformational dynamics are observed in water-only simulations and those with probe molecules. This could imply that the probes are not driving opening events but rather take advantage of mapping sites that spontaneously open as part of their inherent conformational behavior. Finally, we show that docking to an ensemble of conformations from the standard MixMD simulations performs better than docking the apo crystal structure in nine cases and even better than half of the bound crystal structures. Poorer performance was seen in docking to ensembles of conformations from the accelerated MixMD simulations.
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Affiliation(s)
- Richard D Smith
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, Michigan 48109-1056, United States
| | - Heather A Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, Michigan 48109-1056, United States
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12
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Tan YS, Verma CS. Straightforward Incorporation of Multiple Ligand Types into Molecular Dynamics Simulations for Efficient Binding Site Detection and Characterization. J Chem Theory Comput 2020; 16:6633-6644. [PMID: 32810406 DOI: 10.1021/acs.jctc.0c00405] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Binding site identification and characterization is an important initial step in structure-based drug design. To account for the effects of protein flexibility and solvation, several cosolvent molecular dynamics (MD) simulation methods that incorporate small organic molecules into the protein's solvent box to probe for binding sites have been developed. However, most of these methods are highly inefficient, as they allow for the use of only one probe type at a time, which means that multiple sets of simulations have to be performed to map different types of binding sites. The high probe concentrations used in some of these methods also necessitate the use of artificial repulsive forces to prevent the probes from aggregating. Here, we present multiple-ligand-mapping MD (mLMMD), a method that incorporates multiple types of probes for simultaneous and efficient mapping of different types of binding sites without the need for introduction of artificial forces that may cause unintended mapping artifacts. We validate the method on a diverse set of 10 proteins and show that the mLMMD probes are able to reliably identify hydrophobic, hydrogen-bonding, charged, and cryptic binding sites in all of the test cases. Our results also highlight the potential utility of mLMMD for virtual screening and rational drug design.
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Affiliation(s)
- Yaw Sing Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - Chandra S Verma
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671.,Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543.,School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551
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13
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Modeling Epac1 interactions with the allosteric inhibitor AM-001 by co-solvent molecular dynamics. J Comput Aided Mol Des 2020; 34:1171-1179. [PMID: 32700175 PMCID: PMC7533256 DOI: 10.1007/s10822-020-00332-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 07/13/2020] [Indexed: 12/13/2022]
Abstract
The exchange proteins activated by cAMP (EPAC) are implicated in a large variety of physiological processes and they are considered as promising targets for a wide range of therapeutic applications. Several recent reports provided evidence for the therapeutic effectiveness of the inhibiting EPAC1 activity cardiac diseases. In that context, we recently characterized a selective EPAC1 antagonist named AM-001. This compound was featured by a non-competitive mechanism of action but the localization of its allosteric site to EPAC1 structure has yet to be investigated. Therefore, we performed cosolvent molecular dynamics with the aim to identify a suitable allosteric binding site. Then, the docking and molecular dynamics were used to determine the binding of the AM-001 to the regions highlighted by cosolvent molecular dynamics for EPAC1. These analyses led us to the identification of a suitable allosteric AM-001 binding pocket at EPAC1. As a model validation, we also evaluated the binding poses of the available AM-001 analogues, with a different biological potency. Finally, the complex EPAC1 with AM-001 bound at the putative allosteric site was further refined by molecular dynamics. The principal component analysis led us to identify the protein motion that resulted in an inactive like conformation upon the allosteric inhibitor binding.
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14
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Rallabandi HR, Ganesan P, Kim YJ. Targeting the C-Terminal Domain Small Phosphatase 1. Life (Basel) 2020; 10:life10050057. [PMID: 32397221 PMCID: PMC7281111 DOI: 10.3390/life10050057] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/05/2020] [Accepted: 05/07/2020] [Indexed: 12/15/2022] Open
Abstract
The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.
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15
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Goel H, Yu W, Ustach VD, Aytenfisu AH, Sun D, MacKerell AD. Impact of electronic polarizability on protein-functional group interactions. Phys Chem Chem Phys 2020; 22:6848-6860. [PMID: 32195493 PMCID: PMC7194236 DOI: 10.1039/d0cp00088d] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Interactions of proteins with functional groups are key to their biological functions, making it essential that they be accurately modeled. To investigate the impact of the inclusion of explicit treatment of electronic polarizability in force fields on protein-functional group interactions, the additive CHARMM and Drude polarizable force field are compared in the context of the Site-Identification by Ligand Competitive Saturation (SILCS) simulation methodology from which functional group interaction patterns with five proteins for which experimental binding affinities of multiple ligands are available, were obtained. The explicit treatment of polarizability produces significant differences in the functional group interactions in the ligand binding sites including overall enhanced binding of functional groups to the proteins. This is associated with variations of the dipole moments of solutes representative of functional groups in the binding sites relative to aqueous solution with higher dipole moments systematically occurring in the latter, though exceptions occur with positively charged methylammonium. Such variation indicates the complex, heterogeneous nature of the electronic environments of ligand binding sites and emphasizes the inherent limitation of fixed charged, additive force fields for modeling ligand-protein interactions. These effects yield more defined orientation of the functional groups in the binding pockets and a small, but systematic improvement in the ability of the SILCS method to predict the binding orientation and relative affinities of ligands to their target proteins. Overall, these results indicate that the physical model associated with the explicit treatment of polarizability along with the presence of lone pairs in a force field leads to changes in the nature of the interactions of functional groups with proteins versus that occurring with additive force fields, suggesting the utility of polarizable force fields in obtaining a more realistic understanding of protein-ligand interactions.
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Affiliation(s)
- Himanshu Goel
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St., Baltimore, Maryland 21201, USA.
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St., Baltimore, Maryland 21201, USA.
| | - Vincent D Ustach
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St., Baltimore, Maryland 21201, USA.
| | - Asaminew H Aytenfisu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St., Baltimore, Maryland 21201, USA.
| | - Delin Sun
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St., Baltimore, Maryland 21201, USA.
| | - Alexander D MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20, Penn St., Baltimore, Maryland 21201, USA.
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16
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Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity. Int J Mol Sci 2020; 21:ijms21020534. [PMID: 31947677 PMCID: PMC7014104 DOI: 10.3390/ijms21020534] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 12/29/2019] [Accepted: 01/10/2020] [Indexed: 02/07/2023] Open
Abstract
Nuclear receptors (NRs) are highly relevant drug targets in major indications such as oncologic, metabolic, reproductive, and immunologic diseases. However, currently, marketed drugs designed towards the orthosteric binding site of NRs often suffer from resistance mechanisms and poor selectivity. The identification of two superficial allosteric sites, activation function-2 (AF-2) and binding function-3 (BF-3), as novel drug targets sparked the development of inhibitors, while selectivity concerns due to a high conservation degree remained. To determine important pharmacophores and hydration sites among AF-2 and BF-3 of eight hormonal NRs, we systematically analyzed over 10 μ s of molecular dynamics simulations including simulations in explicit water and solvent mixtures. In addition, a library of over 300 allosteric inhibitors was evaluated by molecular docking. Based on our results, we suggest the BF-3 site to offer a higher potential for drug selectivity as opposed to the AF-2 site that is more conserved among the selected receptors. Detected similarities among the AF-2 sites of various NRs urge for a broader selectivity assessment in future studies. In combination with the Supplementary Material, this work provides a foundation to improve both selectivity and potency of allosteric inhibitors in a rational manner and increase the therapeutic applicability of this promising compound class.
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17
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Voloshin V, Smolin N, Geiger A, Winter R, Medvedev NN. Dynamics of TMAO and urea in the hydration shell of the protein SNase. Phys Chem Chem Phys 2019; 21:19469-19479. [PMID: 31461098 DOI: 10.1039/c9cp03184g] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Using all-atom molecular dynamics simulations of aqueous solutions of the globular protein SNase, the dynamic behavior of water molecules and cosolvents (trimethylamine-N-oxide (TMAO) and urea) in the hydration shell of the protein was studied for different solvent compositions. TMAO is a potent protein-stabilizing osmolyte, whereas urea is known to destabilize proteins. For molecules that are initially located in successive narrow layers at a given distance from the protein, the mean displacements and the distribution of displacements for short time intervals are calculated. For molecules that are initially located in solvation shells of a given thickness around the protein, the characteristic residence times in these shells are determined to characterize the dynamic behavior of the solvent molecules as a function of the distance to the protein. A combined consideration of these characteristics allows to reveal additional features of the dynamics of the cosolvents. It is shown that TMAO molecules leave the nearest vicinity of the protein faster than urea molecules, despite the fact that the mobility of TMAO molecules, measured by their mean displacements, is lower than that of urea. Moreover, we show that the rate of release of TMAO molecules from the hydration shell is lower in ternary (TMAO + urea + H2O) solvent mixtures than in the binary ones. This is consistent with a recent observation that the fraction of TMAO near the protein decreases in the presence of urea. From the analysis of the decay of the number of particles initially located in the region of the first peak of the distribution function of solvent molecules around the protein, we estimated that about 20 water molecules and 6-7 urea molecules stay near the protein for more than 1000 ps.
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Affiliation(s)
- Vladimir Voloshin
- Institute of Chemical Kinetics and Combustion, SB RAS, 630090 Novosibirsk, Russia.
| | - Nikolai Smolin
- Department of Cell and Molecular Physiology, Loyola University Chicago, Maywood, Illinois 60153, USA
| | - Alfons Geiger
- Physikalische Chemie, Fakultät für Chemie und Chemische Biologie, Technische Universität Dortmund, Otto-Hahn-Straße 4a, 44221 Dortmund, Germany.
| | - Roland Winter
- Physikalische Chemie, Fakultät für Chemie und Chemische Biologie, Technische Universität Dortmund, Otto-Hahn-Straße 4a, 44221 Dortmund, Germany.
| | - Nikolai N Medvedev
- Institute of Chemical Kinetics and Combustion, SB RAS, 630090 Novosibirsk, Russia. and Novosibirsk State University, 630090 Novosibirsk, Russia
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18
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Arcon JP, Defelipe LA, Lopez ED, Burastero O, Modenutti CP, Barril X, Marti MA, Turjanski AG. Cosolvent-Based Protein Pharmacophore for Ligand Enrichment in Virtual Screening. J Chem Inf Model 2019; 59:3572-3583. [DOI: 10.1021/acs.jcim.9b00371] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
| | | | | | | | | | - Xavier Barril
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
- Faculty of Pharmacy and Institute of Biomedicine (IBUB), University of Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
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19
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Basciu A, Malloci G, Pietrucci F, Bonvin AMJJ, Vargiu AV. Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape. J Chem Inf Model 2019; 59:1515-1528. [PMID: 30883122 DOI: 10.1021/acs.jcim.8b00730] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.
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Affiliation(s)
- Andrea Basciu
- Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy
| | - Giuliano Malloci
- Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy
| | - Fabio Pietrucci
- Sorbonne Université , Muséum National d'Histoire Naturelle, UMR CNRS 7590, IRD, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, IMPMC , F-75005 Paris , France
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Attilio V Vargiu
- Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy.,Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands
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20
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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21
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Araki M, Iwata H, Ma B, Fujita A, Terayama K, Sagae Y, Ono F, Tsuda K, Kamiya N, Okuno Y. Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach. J Comput Chem 2018; 39:2679-2689. [DOI: 10.1002/jcc.25715] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 09/19/2018] [Accepted: 09/25/2018] [Indexed: 11/09/2022]
Affiliation(s)
- Mitsugu Araki
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
- RIKEN Advanced Institute for Computational Sciences; 7-1-26 Minatojima-Minamimachi, Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Hiroaki Iwata
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe; Foundation for Biomedical Research and Innovation (FBRI); 6-3-5, Minatojima-Minamimachi Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Biao Ma
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe; Foundation for Biomedical Research and Innovation (FBRI); 6-3-5, Minatojima-Minamimachi Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Atsuto Fujita
- Research and Development Group for In Silico Drug Discovery, Pro-Cluster Kobe; Foundation for Biomedical Research and Innovation (FBRI); 6-3-5, Minatojima-Minamimachi Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Kei Terayama
- Department of Computational Biology and Medical Sciences; Graduate School of Frontier Sciences, The University of Tokyo; Chiba 277-8561 Japan
| | - Yukari Sagae
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
| | - Fumie Ono
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
| | - Koji Tsuda
- Department of Computational Biology and Medical Sciences; Graduate School of Frontier Sciences, The University of Tokyo; Chiba 277-8561 Japan
| | - Narutoshi Kamiya
- Graduate School of Simulation Studies; University of Hyogo; 7-1-28 Minatojima-Minamimachi, Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Yasushi Okuno
- Graduate School of Medicine; Kyoto University; 53 Shogoin-Kawaharacho, Sakyo-ku Kyoto 606-8507 Japan
- RIKEN Advanced Institute for Computational Sciences; 7-1-26 Minatojima-Minamimachi, Chuo-ku Kobe Hyogo 650-0047 Japan
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22
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Hou X, Rooklin D, Yang D, Liang X, Li K, Lu J, Wang C, Xiao P, Zhang Y, Sun JP, Fang H. Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors. J Chem Inf Model 2018; 58:2331-2342. [PMID: 30299094 DOI: 10.1021/acs.jcim.8b00548] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Accurate protein structure in the ligand-bound state is a prerequisite for successful structure-based virtual screening (SBVS). Therefore, applications of SBVS against targets for which only an apo structure is available may be severely limited. To address this constraint, we developed a computational strategy to explore the ligand-bound state of a target protein, by combined use of molecular dynamics simulation, MM/GBSA binding energy calculation, and fragment-centric topographical mapping. Our computational strategy is validated against low-molecular weight protein tyrosine phosphatase (LMW-PTP) and then successfully employed in the SBVS against protein tyrosine phosphatase receptor type O (PTPRO), a potential therapeutic target for various diseases. The most potent hit compound GP03 showed an IC50 value of 2.89 μM for PTPRO and possessed a certain degree of selectivity toward other protein phosphatases. Importantly, we also found that neglecting the ligand energy penalty upon binding partially accounts for the false positive SBVS hits. The preliminary structure-activity relationships of GP03 analogs are also reported.
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Affiliation(s)
- Xuben Hou
- Department of Medicinal Chemistry and Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmacy , Shandong University , Jinan , Shandong 250012 , China.,Department of Chemistry , New York University , New York , New York 10003 , United States
| | - David Rooklin
- Department of Chemistry , New York University , New York , New York 10003 , United States
| | - Duxiao Yang
- Key Laboratory of Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine , Shandong University , Jinan , Shandong 250012 , China
| | - Xiao Liang
- Department of Medicinal Chemistry and Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmacy , Shandong University , Jinan , Shandong 250012 , China
| | - Kangshuai Li
- Key Laboratory of Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine , Shandong University , Jinan , Shandong 250012 , China
| | - Jianing Lu
- Department of Chemistry , New York University , New York , New York 10003 , United States
| | - Cheng Wang
- Department of Chemistry , New York University , New York , New York 10003 , United States
| | - Peng Xiao
- Key Laboratory of Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine , Shandong University , Jinan , Shandong 250012 , China
| | - Yingkai Zhang
- Department of Chemistry , New York University , New York , New York 10003 , United States.,NYU-ECNU Center for Computational Chemistry , New York University-Shanghai , Shanghai 200122 , China
| | - Jin-Peng Sun
- Key Laboratory of Experimental Teratology of the Ministry of Education and Department of Biochemistry and Molecular Biology, School of Medicine , Shandong University , Jinan , Shandong 250012 , China
| | - Hao Fang
- Department of Medicinal Chemistry and Key Laboratory of Chemical Biology of Natural Products (MOE), School of Pharmacy , Shandong University , Jinan , Shandong 250012 , China
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23
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Takemura K, Sato C, Kitao A. ColDock: Concentrated Ligand Docking with All-Atom Molecular Dynamics Simulation. J Phys Chem B 2018; 122:7191-7200. [PMID: 29993242 DOI: 10.1021/acs.jpcb.8b02756] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
We propose a simple but efficient and accurate method to generate protein-ligand complex structures, called Concentrated ligand Docking (ColDock). This method consists of multiple independent molecular dynamics simulations in which ligands are initially distributed randomly around a protein at relatively high concentration (∼100 mM). This condition significantly increases the probability of the ligand exploring the protein surface, which induces spontaneous ligand binding to the correct binding sites within a 100 ns MD. After clustering of the protein-bound ligand poses, representatives of the populationally dominant clusters are considered as predicted ligand poses. We applied ColDock to four cases starting from holo protein structures and showed that ColDock can generate "correct" ligand poses very similar to the crystal complex structures. Correct ligand poses are also well reproduced in three out of four cases started from apo structures, with the exception being a case with an initially closed binding pocket. The results indicate that ColDock can be used as a protein-ligand docking as long as the ligand binding pocket is initially open. Plausible protein-ligand complex structures can be easily generated by conducting the ColDock procedure using standard MD simulation software.
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Affiliation(s)
| | | | - Akio Kitao
- School of Life Science and Technology , Tokyo Institute of Technology , 2 Chome-12-1 , Ookayama, Meguro, Tokyo 152-8550 , Japan
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24
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Allosteric Modulation of Intact γ-Secretase Structural Dynamics. Biophys J 2018; 113:2634-2649. [PMID: 29262358 DOI: 10.1016/j.bpj.2017.10.012] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/26/2017] [Accepted: 10/10/2017] [Indexed: 12/20/2022] Open
Abstract
As a protease complex involved in the cleavage of amyloid precursor proteins that lead to the formation of amyloid β fibrils implicated in Alzheimer's disease, γ-secretase is an important target for developing therapeutics against Alzheimer's disease. γ-secretase is composed of four subunits: nicastrin (NCT) in the extracellular (EC) domain, presenilin-1 (PS1), anterior pharynx defective 1, and presenilin enhancer 2 in the transmembrane (TM) domain. NCT and PS1 play important roles in binding amyloid β precursor proteins and modulating PS1 catalytic activity. Yet, the molecular mechanisms of coupling between substrate/modulator binding and catalytic activity remain to be elucidated. Recent determination of intact human γ-secretase cryo-electron microscopy structure has opened the way for a detailed investigation of the structural dynamics of this complex. Our analysis, based on a membrane-coupled anisotropic network model, reveals two types of NCT motions, bending and twisting, with respect to PS1. These underlie the fluctuations between the "open" and "closed" states of the lid-like NCT with respect to a hydrophilic loop 1 (HL1) on PS1, thus allowing or blocking access of the substrate peptide (EC portion) to HL1 and to the neighboring helix TM2. In addition to this alternating access mechanism, fluctuations in the volume of the PS1 central cavity facilitate the exposure of the catalytic site for substrate cleavage. Druggability simulations show that γ-secretase presents several hot spots for either orthosteric or allosteric inhibition of catalytic activity, consistent with experimental data. In particular, a hinge region at the interface between the EC and TM domains, near the interlobe groove of NCT, emerges as an allo-targeting site that would impact the coupling between HL1/TM2 and the catalytic pocket, opening, to our knowledge, new avenues for structure-based design of novel allosteric modulators of γ-secretase protease activity.
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