1
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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024; 128:7362-7375. [PMID: 39031121 PMCID: PMC11294009 DOI: 10.1021/acs.jpcb.4c03045] [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] [Indexed: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- 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
| | - 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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2
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Egbert M, Jones G, Collins MR, Kozakov D, Vajda S. FTMove: A Web Server for Detection and Analysis of Cryptic and Allosteric Binding Sites by Mapping Multiple Protein Structures. J Mol Biol 2022; 434:167587. [PMID: 35662465 PMCID: PMC9789685 DOI: 10.1016/j.jmb.2022.167587] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/25/2022] [Accepted: 04/07/2022] [Indexed: 12/27/2022]
Abstract
Protein mapping distributes many copies of different molecular probes on the surface of a target protein in order to determine binding hot spots, regions that are highly preferable for ligand binding. While mapping of X-ray structures by the FTMap server is inherently static, this limitation can be overcome by the simultaneous analysis of multiple structures of the protein. FTMove is an automated web server that implements this approach. From the input of a target protein, by PDB code, the server identifies all structures of the protein available in the PDB, runs mapping on them, and combines the results to form binding hot spots and binding sites. The user may also upload their own protein structures, bypassing the PDB search for similar structures. Output of the server consists of the consensus binding sites and the individual mapping results for each structure - including the number of probes located in each binding site, for each structure. This level of detail allows the users to investigate how the strength of a binding site relates to the protein conformation, other binding sites, and the presence of ligands or mutations. In addition, the structures are clustered on the basis of their binding properties. The use of FTMove is demonstrated by application to 22 proteins with known allosteric binding sites; the orthosteric and allosteric binding sites were identified in all but one case, and the sites were typically ranked among the top five. The FTMove server is publicly available at https://ftmove.bu.edu.
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Affiliation(s)
- Megan Egbert
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Matthew R Collins
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Department of Chemistry, Boston University, Boston, MA 02215, USA.
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3
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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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4
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Feng G, Zhang X, Li Y, Wang R. Analysis of the Binding Sites on BAX and the Mechanism of BAX Activators through Extensive Molecular Dynamics Simulations. J Chem Inf Model 2021; 62:5208-5222. [PMID: 34047559 DOI: 10.1021/acs.jcim.0c01420] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The BAX protein is a pro-apoptotic member of the Bcl-2 family, which triggers apoptosis by causing permeabilization of the mitochondrial outer membrane. However, the activation mechanism of BAX is far from being understood. Although a few small-molecule BAX activators have been reported in the literature, their crystal structures in complex with BAX have not been resolved. So far, their binding modes were modeled at most by simple molecular docking efforts. Lack of an in-depth understanding of the activation mechanism of BAX hinders the development of more effective BAX activators. In this work, we employed cosolvent molecular dynamics simulation to detect the potential binding sites on the surface of BAX and performed a long-time molecular dynamics simulation (50 μs in total) to derive the possible binding modes of three BAX activators (i.e., BAM7, BTC-8, and BTSA1) reported in the literature. Our results indicate that the trigger, S184, and vMIA sites are the three major binding sites on the full-length BAX structure. Moreover, the canonical hydrophobic groove is clearly detected on the α9-truncated BAX structure, which is consistent with the outcomes of relevant experimental studies. Interestingly, it is observed that solvent probes bind to the trigger bottom pocket more stably than the PPI trigger site. Each activator was subjected to unbiased molecular dynamics simulations started at the three major binding sites in five parallel jobs. Our MD results indicate that all three activators tend to stay at the trigger site with favorable MM-GB/SA binding energies. BAM7 and BTSA1 can enter the trigger bottom pocket and thereby enhance the movement of the α1-α2 loop, which may be a key factor at the early stage of BAX activation. Our molecular modeling results may provide useful guidance for designing smart biological experiments to further explore BAX activation and directing structure-based efforts toward discovering more effective BAX activators.
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Affiliation(s)
- Guoqin Feng
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, People's Republic of China.,State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China
| | - Xiangying Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, People's Republic of China
| | - Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, People's Republic of China
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, People's Republic of China.,State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, People's Republic of China.,Shanxi Key Laboratory of Innovative Drugs for the Treatment of Serious Diseases Based on Chronic Inflammation, College of Traditional Chinese Medicines, Shanxi University of Chinese Medicine, Taiyuan, Shanxi 030619, People's Republic of China
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5
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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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6
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Wakefield AE, Yueh C, Beglov D, Castilho MS, Kozakov D, Keserű GM, Whitty A, Vajda S. Benchmark Sets for Binding Hot Spot Identification in Fragment-Based Ligand Discovery. J Chem Inf Model 2020; 60:6612-6623. [PMID: 33291870 DOI: 10.1021/acs.jcim.0c00877] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Binding hot spots are regions of proteins that, due to their potentially high contribution to the binding free energy, have high propensity to bind small molecules. We present benchmark sets for testing computational methods for the identification of binding hot spots with emphasis on fragment-based ligand discovery. Each protein structure in the set binds a fragment, which is extended into larger ligands in other structures without substantial change in its binding mode. Structures of the same proteins without any bound ligand are also collected to form an unbound benchmark. We also discuss a set developed by Astex Pharmaceuticals for the validation of hot and warm spots for fragment binding. The set is based on the assumption that a fragment that occurs in diverse ligands in the same subpocket identifies a binding hot spot. Since this set includes only ligand-bound proteins, we added a set with unbound structures. All four sets were tested using FTMap, a computational analogue of fragment screening experiments to form a baseline for testing other prediction methods, and differences among the sets are discussed.
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Affiliation(s)
- Amanda E Wakefield
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States.,Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Christine Yueh
- Acpharis Inc., Holliston, Massachusetts 01746, United States
| | - Dmitri Beglov
- Acpharis Inc., Holliston, Massachusetts 01746, United States
| | - Marcelo S Castilho
- Faculdade de Farmácia da Universidade Federal da Bahia, Bahia, Salvador, BA 40170-115, Brazil
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Adrian Whitty
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States.,Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
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7
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Ustach VD, Lakkaraju SK, Jo S, Yu W, Jiang W, MacKerell AD. Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization. J Chem Inf Model 2019; 59:3018-3035. [PMID: 31034213 PMCID: PMC6597307 DOI: 10.1021/acs.jcim.9b00210] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.
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Affiliation(s)
- Vincent D. Ustach
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
| | | | - Sunhwan Jo
- SilcsBio, LLC, 8 Market Place, Suite 300, Baltimore, MD 21202
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
| | - Wenjuan Jiang
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201
- SilcsBio, LLC, 8 Market Place, Suite 300, Baltimore, MD 21202
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8
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Ghanakota P, DasGupta D, Carlson HA. Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations. J Chem Inf Model 2019; 59:2035-2045. [PMID: 31017411 DOI: 10.1021/acs.jcim.8b00925] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD) (Ghanakota, P.; Carlson, H. A. Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J. Phys. Chem. B 2016, 120, 8685-8695), we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets: Abl Kinase, Androgen Receptor, Pdk1 Kinase, Farnesyl Pyrophosphate Synthase, Chk1 Kinase, Glucokinase, and Protein Tyrosine Phosphatase 1B.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| | - Debarati DasGupta
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
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9
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Mahmoud AH, Yang Y, Lill MA. Improving Atom-Type Diversity and Sampling in Cosolvent Simulations Using λ-Dynamics. J Chem Theory Comput 2019; 15:3272-3287. [PMID: 30933496 DOI: 10.1021/acs.jctc.8b00940] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Amr H. Mahmoud
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47906, United States
| | - Ying Yang
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47906, United States
| | - Markus A. Lill
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47906, United States
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10
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O'Reilly M, Cleasby A, Davies TG, Hall RJ, Ludlow RF, Murray CW, Tisi D, Jhoti H. Crystallographic screening using ultra-low-molecular-weight ligands to guide drug design. Drug Discov Today 2019; 24:1081-1086. [PMID: 30878562 DOI: 10.1016/j.drudis.2019.03.009] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/06/2019] [Accepted: 03/08/2019] [Indexed: 02/06/2023]
Abstract
We present a novel crystallographic screening methodology (MiniFrags) that employs high-concentration aqueous soaks with a chemically diverse and ultra-low-molecular-weight library (heavy atom count 5-7) to identify ligand-binding hot and warm spots on proteins. We propose that MiniFrag screening represents a highly effective method for guiding optimisation of fragment-derived lead compounds or chemical tools and that the high screening hit rates reflect enhanced sampling of chemical space.
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Affiliation(s)
- Marc O'Reilly
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Anne Cleasby
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Thomas G Davies
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Richard J Hall
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - R Frederick Ludlow
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Christopher W Murray
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Dominic Tisi
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK
| | - Harren Jhoti
- Astex Pharmaceuticals,436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, UK.
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11
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Damm-Ganamet KL, Arora N, Becart S, Edwards JP, Lebsack AD, McAllister HM, Nelen MI, Rao NL, Westover L, Wiener JJM, Mirzadegan T. Accelerating Lead Identification by High Throughput Virtual Screening: Prospective Case Studies from the Pharmaceutical Industry. J Chem Inf Model 2019; 59:2046-2062. [DOI: 10.1021/acs.jcim.8b00941] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
| | | | | | | | | | | | - Marina I. Nelen
- Discovery Sciences, Janssen Research and Development, Welsh and McKean Roads, Spring House, Pennsylvania 19477, United States
| | | | - Lori Westover
- Discovery Sciences, Janssen Research and Development, Welsh and McKean Roads, Spring House, Pennsylvania 19477, United States
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12
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Nepravishta R, Walpole S, Tailford L, Juge N, Angulo J. Deriving Ligand Orientation in Weak Protein-Ligand Complexes by DEEP-STD NMR Spectroscopy in the Absence of Protein Chemical-Shift Assignment. Chembiochem 2018; 20:340-344. [PMID: 30379391 PMCID: PMC6468252 DOI: 10.1002/cbic.201800568] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Indexed: 01/07/2023]
Abstract
Differential epitope mapping saturation transfer difference (DEEP‐STD) NMR spectroscopy is a recently developed powerful approach for elucidating the structure and pharmacophore of weak protein–ligand interactions, as it reports key information on the orientation of the ligand and the architecture of the binding pocket.1 The method relies on selective saturation of protein residues in the binding site and the generation of a differential epitope map by observing the ligand, which depicts the nature of the protein residues making contact with the ligand in the bound state. Selective saturation requires knowledge of the chemical‐shift assignment of the protein residues, which can be obtained either experimentally by NMR spectroscopy or predicted from 3D structures. Herein, we propose a simple experimental procedure to expand the DEEP‐STD NMR methodology to protein–ligand cases in which the spectral assignment of the protein is not available. This is achieved by experimentally identifying the chemical shifts of the residues present in binding hot‐spots on the surface of the receptor protein by using 2D NMR experiments combined with a paramagnetic probe.
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Affiliation(s)
- Ridvan Nepravishta
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, Norfolk, NR4 7TJ, UK
| | - Samuel Walpole
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, Norfolk, NR4 7TJ, UK
| | - Louise Tailford
- The Gut Microbes and Health Institute Strategic Program, Quadram Institute of Bioscience, Norwich Research Park, Norwich, Norfolk, NR4 7UA, UK
| | - Nathalie Juge
- The Gut Microbes and Health Institute Strategic Program, Quadram Institute of Bioscience, Norwich Research Park, Norwich, Norfolk, NR4 7UA, UK
| | - Jesus Angulo
- School of Pharmacy, University of East Anglia, Norwich Research Park, Norwich, Norfolk, NR4 7TJ, UK
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13
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Ghanakota P, van Vlijmen H, Sherman W, Beuming T. Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein–Protein Interaction Interfaces. J Chem Inf Model 2018; 58:784-793. [PMID: 29617116 DOI: 10.1021/acs.jcim.7b00487] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Phani Ghanakota
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | | | - Woody Sherman
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Thijs Beuming
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
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14
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Graham SE, Smith RD, Carlson HA. Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics. J Chem Inf Model 2018; 58:305-314. [PMID: 29286658 PMCID: PMC6190669 DOI: 10.1021/acs.jcim.7b00268] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Water molecules are an important factor in protein-ligand binding. Upon binding of a ligand with a protein's surface, waters can either be displaced by the ligand or may be conserved and possibly bridge interactions between the protein and ligand. Depending on the specific interactions made by the ligand, displacing waters can yield a gain in binding affinity. The extent to which binding affinity may increase is difficult to predict, as the favorable displacement of a water molecule is dependent on the site-specific interactions made by the water and the potential ligand. Several methods have been developed to predict the location of water sites on a protein's surface, but the majority of methods are not able to take into account both protein dynamics and the interactions made by specific functional groups. Mixed-solvent molecular dynamics (MixMD) is a cosolvent simulation technique that explicitly accounts for the interaction of both water and small molecule probes with a protein's surface, allowing for their direct competition. This method has previously been shown to identify both active and allosteric sites on a protein's surface. Using a test set of eight systems, we have developed a method using MixMD to identify conserved and displaceable water sites. Conserved sites can be determined by an occupancy-based metric to identify sites which are consistently occupied by water even in the presence of probe molecules. Conversely, displaceable water sites can be found by considering the sites which preferentially bind probe molecules. Furthermore, the inclusion of six probe types allows the MixMD method to predict which functional groups are capable of displacing which water sites. The MixMD method consistently identifies sites which are likely to be nondisplaceable and predicts the favorable displacement of water sites that are known to be displaced upon ligand binding.
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Affiliation(s)
- Sarah E. Graham
- Department of Biophysics, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, Michigan, 48109-1065
| | - Richard D. Smith
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, Michigan, 48109-1065
| | - Heather A. Carlson
- Department of Biophysics, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, Michigan, 48109-1065
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, Michigan, 48109-1065
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15
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Kimura SR, Hu HP, Ruvinsky AM, Sherman W, Favia AD. Deciphering Cryptic Binding Sites on Proteins by Mixed-Solvent Molecular Dynamics. J Chem Inf Model 2017; 57:1388-1401. [PMID: 28537745 DOI: 10.1021/acs.jcim.6b00623] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In recent years, molecular dynamics simulations of proteins in explicit mixed solvents have been applied to various problems in protein biophysics and drug discovery, including protein folding, protein surface characterization, fragment screening, allostery, and druggability assessment. In this study, we perform a systematic study on how mixtures of organic solvent probes in water can reveal cryptic ligand binding pockets that are not evident in crystal structures of apo proteins. We examine a diverse set of eight PDB proteins that show pocket opening induced by ligand binding and investigate whether solvent MD simulations on the apo structures can induce the binding site observed in the holo structures. The cosolvent simulations were found to induce conformational changes on the protein surface, which were characterized and compared with the holo structures. Analyses of the biological systems, choice of probes and concentrations, druggability of the resulting induced pockets, and application to drug discovery are discussed here.
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Affiliation(s)
- S Roy Kimura
- Schrödinger KK , 17th Fl, Marunouchi Trust Tower North, 1-8-1 Marunouchi, Chiyoda-ku, Tokyo, Japan
| | - Hai Peng Hu
- Lilly China Research and Development Center (LCRDC), Eli Lilly and Company , Building 8, 338 Jia Li Lue Road, Shanghai 201203, PR China
| | - Anatoly M Ruvinsky
- Schrödinger LLC , 222 Third Street, Suite 2230, Cambridge, Massachusetts 02142, United States
| | - Woody Sherman
- Schrödinger LLC , 222 Third Street, Suite 2230, Cambridge, Massachusetts 02142, United States
| | - Angelo D Favia
- Lilly China Research and Development Center (LCRDC), Eli Lilly and Company , Building 8, 338 Jia Li Lue Road, Shanghai 201203, PR China
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16
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Sayyed-Ahmad A, Gorfe AA. Mixed-Probe Simulation and Probe-Derived Surface Topography Map Analysis for Ligand Binding Site Identification. J Chem Theory Comput 2017; 13:1851-1861. [PMID: 28252958 DOI: 10.1021/acs.jctc.7b00130] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Membrane proteins represent a considerable fraction of pharmaceutical drug targets. A computational technique to identify ligand binding pockets in these proteins is therefore of great importance. We recently reported such a technique called pMD-membrane that utilizes small molecule probes to detect ligand binding sites and surface hotspots on membrane proteins based on probe-based molecular dynamics simulation. The current work extends pMD-membrane to a diverse set of small organic molecular species that can be used as cosolvents during simulation of membrane proteins. We also describe a projection technique for globally quantifying probe densities on the protein surface and introduce a technique to construct surface topography maps directly from the probe-binding propensity of surface residues. The map reveals surface patterns and geometric features that aid in filtering out high probe density hotspots lacking pocketlike characteristics. We demonstrate the applicability of the extended pMD-membrane and the new analysis tool by exploring the druggability of full-length G12D, G12V, and G13D oncogenic K-Ras mutants bound to a negatively charged lipid bilayer. Using data from 30 pMD-membrane runs conducted in the presence of a 2.8 M cosolvent made up of an equal proportion of seven small organic molecules, we show that our approach robustly identifies known allosteric ligand binding sites and other reactive regions on K-Ras. Our results also show that accessibility of some pockets is modulated by differential membrane interactions.
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Affiliation(s)
- Abdallah Sayyed-Ahmad
- Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston , 6431 Fannin Street, Houston, Texas 77030, United States
| | - Alemayehu A Gorfe
- Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston , 6431 Fannin Street, Houston, Texas 77030, United States
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17
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Tan YS, Reeks J, Brown CJ, Thean D, Ferrer
Gago FJ, Yuen TY, Goh EL, Lee XEC, Jennings CE, Joseph TL, Lakshminarayanan R, Lane DP, Noble MEM, Verma CS. Benzene Probes in Molecular Dynamics Simulations Reveal Novel Binding Sites for Ligand Design. J Phys Chem Lett 2016; 7:3452-7. [PMID: 27532490 PMCID: PMC5515508 DOI: 10.1021/acs.jpclett.6b01525] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Protein flexibility poses a major challenge in binding site identification. Several computational pocket detection methods that utilize small-molecule probes in molecular dynamics (MD) simulations have been developed to address this issue. Although they have proven hugely successful at reproducing experimental structural data, their ability to predict new binding sites that are yet to be identified and characterized has not been demonstrated. Here, we report the use of benzenes as probe molecules in ligand-mapping MD (LMMD) simulations to predict the existence of two novel binding sites on the surface of the oncoprotein MDM2. One of them was serendipitously confirmed by biophysical assays and X-ray crystallography to be important for the binding of a new family of hydrocarbon stapled peptides that were specifically designed to target the other putative site. These results highlight the predictive power of LMMD and suggest that predictions derived from LMMD simulations can serve as a reliable basis for the identification of novel ligand binding sites in structure-based 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
| | - Judith Reeks
- Northern
Institute for Cancer Research, Newcastle
University, Framlington
Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Christopher J. Brown
- p53
Laboratory, A*STAR, 8A Biomedical Grove, #06-04/05 Neuros/Immunos, Singapore 138648
| | - Dawn Thean
- p53
Laboratory, A*STAR, 8A Biomedical Grove, #06-04/05 Neuros/Immunos, Singapore 138648
| | | | - Tsz Ying Yuen
- Institute
of Chemical & Engineering Sciences, A*STAR, 8 Biomedical
Grove, #07-01 Neuros, Singapore 138665
| | - Eunice
Tze Leng Goh
- Singapore
Eye Research Institute, 11 Third Hospital Avenue, Singapore 168751
| | - Xue Er Cheryl Lee
- p53
Laboratory, A*STAR, 8A Biomedical Grove, #06-04/05 Neuros/Immunos, Singapore 138648
| | - Claire E. Jennings
- Northern
Institute for Cancer Research, Newcastle
University, Framlington
Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Thomas L. Joseph
- Bioinformatics
Institute, Agency for Science, Technology
and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | | | - David P. Lane
- p53
Laboratory, A*STAR, 8A Biomedical Grove, #06-04/05 Neuros/Immunos, Singapore 138648
- E-mail:
| | - Martin E. M. Noble
- Northern
Institute for Cancer Research, Newcastle
University, Framlington
Place, Newcastle upon Tyne NE2 4HH, U.K.
- E-mail:
| | - 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
- E-mail:
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18
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Ghanakota P, Carlson HA. Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics. J Med Chem 2016; 59:10383-10399. [PMID: 27486927 DOI: 10.1021/acs.jmedchem.6b00399] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural aqueous environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
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19
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Ung PMU, Ghanakota P, Graham SE, Lexa KW, Carlson HA. Identifying binding hot spots on protein surfaces by mixed-solvent molecular dynamics: HIV-1 protease as a test case. Biopolymers 2016; 105:21-34. [PMID: 26385317 DOI: 10.1002/bip.22742] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 09/14/2015] [Accepted: 09/14/2015] [Indexed: 12/16/2022]
Abstract
Mixed-solvent molecular dynamics (MixMD) simulations use full protein flexibility and competition between water and small organic probes to achieve accurate hot-spot mapping on protein surfaces. In this study, we improved MixMD using human immunodeficiency virus type-1 protease (HIVp) as the test case. We used three probe-water solutions (acetonitrile-water, isopropanol-water, and pyrimidine-water), first at 50% w/w concentration and later at 5% v/v. Paradoxically, better mapping was achieved by using fewer probes; 5% simulations gave a superior signal-to-noise ratio and far fewer spurious hot spots than 50% MixMD. Furthermore, very intense and well-defined probe occupancies were observed in the catalytic site and potential allosteric sites that have been confirmed experimentally. The Eye site, an allosteric site underneath the flap of HIVp, has been confirmed by the presence of a 5-nitroindole fragment in a crystal structure. MixMD also mapped two additional hot spots: the Exo site (between the Gly16-Gly17 and Cys67-Gly68 loops) and the Face site (between Glu21-Ala22 and Val84-Ile85 loops). The Exo site was observed to overlap with crystallographic additives such as acetate and dimethyl sulfoxide that are present in different crystal forms of the protein. Analysis of crystal structures of HIVp in different symmetry groups has shown that some surface sites are common interfaces for crystal contacts, which means that they are surfaces that are relatively easy to desolvate and complement with organic molecules. MixMD should identify these sites; in fact, their occupancy values help establish a solid cut-off where "druggable" sites are required to have higher occupancies than the crystal-packing faces.
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Affiliation(s)
- Peter M U Ung
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065
| | - Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065
| | - Sarah E Graham
- Department of Biophysics, College of LSA, University of Michigan, 930 N. University St., Ann Arbor, MI, 48109-1055
| | - Katrina W Lexa
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church St., Ann Arbor, MI, 48109-1065.,Department of Biophysics, College of LSA, University of Michigan, 930 N. University St., Ann Arbor, MI, 48109-1055
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20
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Ghanakota P, Carlson HA. Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J Phys Chem B 2016; 120:8685-95. [PMID: 27258368 DOI: 10.1021/acs.jpcb.6b03515] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mixed-solvent molecular dynamics (MixMD) is a hotspot-mapping technique that relies on molecular dynamics simulations of proteins in binary solvent mixtures. Previous work on MixMD has established the technique's effectiveness in capturing binding sites of small organic compounds. In this work, we show that MixMD can identify both competitive and allosteric sites on proteins. The MixMD approach embraces full protein flexibility and allows competition between solvent probes and water. Sites preferentially mapped by probe molecules are more likely to be binding hotspots. There are two important requirements for the identification of ligand-binding hotspots: (1) hotspots must be mapped at very high signal-to-noise ratio and (2) the hotspots must be mapped by multiple probe types. We have developed our mapping protocol around acetonitrile, isopropanol, and pyrimidine as probe solvents because they allowed us to capture hydrophilic, hydrophobic, hydrogen-bonding, and aromatic interactions. Charged probes were needed for mapping one target, and we introduce them in this work. In order to demonstrate the robust nature and wide applicability of the technique, a combined total of 5 μs of MixMD was applied across several protein targets known to exhibit allosteric modulation. Most notably, all the protein crystal structures used to initiate our simulations had no allosteric ligands bound, so there was no preorganization of the sites to predispose the simulations to find the allosteric hotspots. The protein test cases were ABL Kinase, Androgen Receptor, CHK1 Kinase, Glucokinase, PDK1 Kinase, Farnesyl Pyrophosphate Synthase, and Protein-Tyrosine Phosphatase 1B. The success of the technique is demonstrated by the fact that the top-four sites solely map the competitive and allosteric sites. Lower-ranked sites consistently map other biologically relevant sites, multimerization interfaces, or crystal-packing interfaces. Lastly, we highlight the importance of including protein flexibility by demonstrating that MixMD can map allosteric sites that are not detected in half the systems using FTMap applied to the same crystal structures.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
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21
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Abstract
Over the past two decades, solvent mapping has emerged as a useful tool for identifying hot spots within binding sites on proteins for drug-like molecules and suggesting properties of potential binders. While the experimental technique requires solving multiple crystal structures of a protein in different solvents, computational solvent mapping allows for fast analysis of a protein for potential binding sites and their druggability. Recent advances in genomics, systems biology and interactomics provide a multitude of potential targets for drug development and solvent mapping can provide useful information to help prioritize targets for drug discovery projects. Here, we review various approaches to computational solvent mapping, highlight some key advances and provide our opinion on future directions in the field.
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22
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Hall DR, Kozakov D, Whitty A, Vajda S. Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery. Trends Pharmacol Sci 2015; 36:724-736. [PMID: 26538314 DOI: 10.1016/j.tips.2015.08.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 08/03/2015] [Accepted: 08/04/2015] [Indexed: 01/01/2023]
Abstract
Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery.
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Affiliation(s)
- David R Hall
- Acpharis Inc., 160 North Mill Street, Holliston, MA 01746, USA
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
| | - Adrian Whitty
- Department of Chemistry, Boston University, Boston, MA, 02215, USA.
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA; Department of Chemistry, Boston University, Boston, MA, 02215, USA.
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23
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Lin JH. Review structure- and dynamics-based computational design of anticancer drugs. Biopolymers 2015; 105:2-9. [DOI: 10.1002/bip.22744] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 09/16/2015] [Accepted: 09/16/2015] [Indexed: 01/13/2023]
Affiliation(s)
- Jung Hsin Lin
- Research Center for Applied Sciences, Academia Sinica; Taipei Taiwan
- Institute of Biomedical Sciences, Academia Sinica; Taipei Taiwan
- School of Pharmacy; National Taiwan University; Taipei Taiwan
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24
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Lama D, Brown CJ, Lane DP, Verma CS. Gating by Tryptophan 73 Exposes a Cryptic Pocket at the Protein-Binding Interface of the Oncogenic eIF4E Protein. Biochemistry 2015; 54:6535-44. [DOI: 10.1021/acs.biochem.5b00812] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Dilraj Lama
- Bioinformatics
Institute, A*STAR (Agency for Science, Technology and Research), 30 Biopolis
Street, #07-01 Matrix, Singapore 138671
| | - Christopher J. Brown
- p53
Laboratory, A*STAR (Agency for Science, Technology and Research), 8A Biomedical Grove, #06-04/05, Neuros/Immunos, Singapore 138648
| | - David P. Lane
- p53
Laboratory, A*STAR (Agency for Science, Technology and Research), 8A Biomedical Grove, #06-04/05, Neuros/Immunos, Singapore 138648
| | - Chandra S. Verma
- Bioinformatics
Institute, A*STAR (Agency for Science, Technology and Research), 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, 50 Nanyang
Drive, Singapore 637551
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25
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Tan YS, Spring DR, Abell C, Verma CS. The Application of Ligand-Mapping Molecular Dynamics Simulations to the Rational Design of Peptidic Modulators of Protein-Protein Interactions. J Chem Theory Comput 2015; 11:3199-210. [PMID: 26575757 DOI: 10.1021/ct5010577] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
A computational ligand-mapping approach to detect protein surface pockets that interact with hydrophobic moieties is presented. In this method, we incorporated benzene molecules into explicit solvent molecular dynamics simulations of various protein targets. The benzene molecules successfully identified the binding locations of hydrophobic hot-spot residues and all-hydrocarbon cross-links from known peptidic ligands. They also unveiled cryptic binding sites that are occluded by side chains and the protein backbone. Our results demonstrate that ligand-mapping molecular dynamics simulations hold immense promise to guide the rational design of peptidic modulators of protein-protein interactions, including that of stapled peptides, which show promise as an exciting new class of cell-penetrating therapeutic molecules.
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Affiliation(s)
- Yaw Sing Tan
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom.,Bioinformatics Institute (A*STAR) , 30 Biopolis Street, #07-01 Matrix, Singapore 138671
| | - David R Spring
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Chris Abell
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Chandra S Verma
- Bioinformatics Institute (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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26
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Laraia L, McKenzie G, Spring DR, Venkitaraman AR, Huggins DJ. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions. CHEMISTRY & BIOLOGY 2015; 22:689-703. [PMID: 26091166 PMCID: PMC4518475 DOI: 10.1016/j.chembiol.2015.04.019] [Citation(s) in RCA: 115] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 04/01/2015] [Accepted: 04/08/2015] [Indexed: 01/19/2023]
Abstract
Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.
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Affiliation(s)
- Luca Laraia
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - Grahame McKenzie
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - David R Spring
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Ashok R Venkitaraman
- Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK
| | - David J Huggins
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK; Medical Research Council Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK; Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0HE, UK.
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27
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The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc 2015; 10:733-55. [PMID: 25855957 DOI: 10.1038/nprot.2015.043] [Citation(s) in RCA: 411] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
FTMap is a computational mapping server that identifies binding hot spots of macromolecules-i.e., regions of the surface with major contributions to the ligand-binding free energy. To use FTMap, users submit a protein, DNA or RNA structure in PDB (Protein Data Bank) format. FTMap samples billions of positions of small organic molecules used as probes, and it scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots in good agreement with experimental data. FTMap serves as the basis for other servers, namely FTSite, which is used to predict ligand-binding sites, FTFlex, which is used to account for side chain flexibility, FTMap/param, used to parameterize additional probes and FTDyn, for mapping ensembles of protein structures. Applications include determining the druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures and providing input for fragment-based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and it is much faster than the more-recent approaches to protein mapping based on mixed molecular dynamics. By using 16 probe molecules, the FTMap server finds the hot spots of an average-size protein in <1 h. As FTFlex performs mapping for all low-energy conformers of side chains in the binding site, its completion time is proportionately longer.
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28
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Gerogiokas G, Southey MWY, Mazanetz MP, Hefeitz A, Bodkin M, Law RJ, Michel J. Evaluation of water displacement energetics in protein binding sites with grid cell theory. Phys Chem Chem Phys 2015; 17:8416-26. [DOI: 10.1039/c4cp05572a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The grid cell theory method was used to elucidate perturbations in water network energetics in a range of protein–ligand complexes.
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Affiliation(s)
| | | | | | | | | | | | - J. Michel
- EaStCHEM School of Chemistry
- Edinburgh
- UK
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29
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Alvarez-Garcia D, Barril X. Molecular Simulations with Solvent Competition Quantify Water Displaceability and Provide Accurate Interaction Maps of Protein Binding Sites. J Med Chem 2014; 57:8530-9. [DOI: 10.1021/jm5010418] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Daniel Alvarez-Garcia
- Departament
de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Gran Via de les Corts Catalanes,
585, 08007 Barcelona, Spain
| | - Xavier Barril
- Departament
de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Gran Via de les Corts Catalanes,
585, 08007 Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
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30
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Lexa KW, Goh GB, Carlson HA. Parameter choice matters: validating probe parameters for use in mixed-solvent simulations. J Chem Inf Model 2014; 54:2190-9. [PMID: 25058662 PMCID: PMC4144759 DOI: 10.1021/ci400741u] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Probe mapping is a common approach
for identifying potential binding
sites in structure-based drug design; however, it typically relies
on energy minimizations of probes in the gas phase and a static protein
structure. The mixed-solvent molecular dynamics (MixMD) approach was
recently developed to account for full protein flexibility and solvation
effects in hot-spot mapping. Our first study used only acetonitrile
as a probe, and here, we have augmented the set of functional group
probes through careful testing and parameter validation. A diverse
range of probes are needed in order to map complex binding interactions.
A small variation in probe parameters can adversely effect mixed-solvent
behavior, which we highlight with isopropanol. We tested 11 solvents
to identify six with appropriate behavior in TIP3P water to use as
organic probes in the MixMD method. In addition to acetonitrile and
isopropanol, we have identified acetone, N-methylacetamide,
imidazole, and pyrimidine. These probe solvents will enable MixMD
studies to recover hydrogen-bonding sites, hydrophobic pockets, protein–protein
interactions, and aromatic hotspots. Also, we show that ternary-solvent
systems can be incorporated within a single simulation. Importantly,
these binary and ternary solvents do not require artificial repulsion
terms like other methods. Within merely 5 ns, layered solvent boxes
become evenly mixed for soluble probes. We used radial distribution
functions to evaluate solvent behavior, determine adequate mixing,
and confirm the absence of phase separation. We recommend that radial
distribution functions should be used to assess adequate sampling
in all mixed-solvent techniques rather than the current practice of
examining the solvent ratios at the edges of the solvent box.
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Affiliation(s)
- Katrina W Lexa
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor , 428 Church St., Ann Arbor, Michigan 48109-1065, United States
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Huang D, Rossini E, Steiner S, Caflisch A. Structured water molecules in the binding site of bromodomains can be displaced by cosolvent. ChemMedChem 2013; 9:573-9. [PMID: 23804246 DOI: 10.1002/cmdc.201300156] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2013] [Indexed: 01/16/2023]
Abstract
Bromodomains are α-helical bundles of approximately 110 residues that recognize acetylated lysine side chains mainly on histone tails. Bromodomains are known to play an important role in cancer and inflammation, and as such, significant efforts are being made to identify small-molecule inhibitors of these epigenetic reader proteins. Here, explicit solvent molecular dynamics (MD) simulations of two bromodomains (BAZ2B and CREBBP) are used to analyze the water molecules that seem to be conserved at the bottom of the acetyl-lysine binding site in most crystal structures of bromodomains. The MD runs suggest that the occupancy of the structured water molecules is influenced by conformational transitions of the loop that connects helices Z and A. Additional simulations in the presence of 50 molecules of cosolvent (i.e., 440 mM of dimethylsulfoxide, methanol, or ethanol) indicate that some of the structured water molecules can be displaced transiently. The residence time in the acetyl-lysine binding site is calculated to be about 1 ns, 2-5 ns, and 10-30 ns for methanol, ethanol, and dimethylsulfoxide, respectively, while the affinity of the three cosolvents for BAZ2B and CREBBP is in the range of 50-500 mM. The results described have implications for ligand design, suggesting that only structured water molecules that do not exchange with cosolvent should be maintained in crystal structures used for docking campaigns, and that hydroxy substituents should be incorporated in the ligand so as to map the structured water molecules replaced by (m)ethanol.
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Affiliation(s)
- Danzhi Huang
- Department of Biochemistry, University of Zürich, Winterthurerstrasse 190, 8057 Zürich (Switzerland).
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