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Xu C, Zhang X, Zhao L, Verkhivker GM, Bai F. Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies. JACS AU 2024; 4:1632-1645. [PMID: 38665669 PMCID: PMC11040708 DOI: 10.1021/jacsau.4c00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 04/28/2024]
Abstract
The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure-kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate constant (koff), enabling a direct assessment of the drug-target residence time. These models demonstrated good predictive performance, where hydrophobic and hydrogen bond interactions significantly influence the koff prediction. In subsequent applications, our models were used to assist in the discovery of new inhibitors for the N-terminal domain of HSP90α (N-HSP90α), demonstrating predictive capabilities on an experimental validation set with a new scaffold. In X-ray crystallography experiments, the loop-middle conformation of apo N-HSP90α was observed for the first time (previously, the loop-middle conformation had only been observed in holo-N-HSP90α structures). Interestingly, we observed different conformations of apo N-HSP90α simultaneously in an asymmetric unit, which was also observed in a holo-N-HSP90α structure, suggesting an equilibrium of conformations between different states in solution, which could be one of the determinants affecting the binding kinetics of the ligand. Different ligands can undergo conformational selection or alter the equilibrium of conformations, inducing conformational rearrangements and resulting in different effects on binding kinetics. We then used molecular dynamics simulations to describe conformational changes of apo N-HSP90α in different conformational states. In summary, the study of the binding kinetics and molecular mechanisms of N-HSP90α provides valuable information for the development of more targeted therapeutic approaches.
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Affiliation(s)
- Chao Xu
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Xianglei Zhang
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Lianghao Zhao
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Gennady M. Verkhivker
- Keck
Center for Science and Engineering, Graduate Program in Computational
and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department
of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
| | - Fang Bai
- Shanghai
Institute for Advanced Immunochemical Studies and School of Life Science
and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai 201210, China
- School
of Information Science and Technology, ShanghaiTech
University, 393 Middle Huaxia Road, Shanghai 201210, China
- Shanghai
Clinical Research and Trial Center, Shanghai 201210, China
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2
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Sohraby F, Nunes-Alves A. Advances in computational methods for ligand binding kinetics. Trends Biochem Sci 2022; 48:437-449. [PMID: 36566088 DOI: 10.1016/j.tibs.2022.11.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/16/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.
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Affiliation(s)
- Farzin Sohraby
- Institute of Chemistry, Technische Universität Berlin, 10623 Berlin, Germany
| | - Ariane Nunes-Alves
- Institute of Chemistry, Technische Universität Berlin, 10623 Berlin, Germany.
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3
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Ruzmetov T, Montes R, Sun J, Chen SH, Tang Z, Chang CEA. Binding Kinetics Toolkit for Analyzing Transient Molecular Conformations and Computing Free Energy Landscapes. J Phys Chem A 2022; 126:8761-8770. [DOI: 10.1021/acs.jpca.2c05499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Talant Ruzmetov
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Ruben Montes
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Jianan Sun
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Si-Han Chen
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Zhiye Tang
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Chia-en A. Chang
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
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4
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Akhunzada MJ, Yoon HJ, Deb I, Braka A, Wu S. Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors. Sci Rep 2022; 12:15972. [PMID: 36153364 PMCID: PMC9509322 DOI: 10.1038/s41598-022-20065-2] [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: 04/15/2022] [Accepted: 09/08/2022] [Indexed: 11/10/2022] Open
Abstract
AbstractRecently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding.
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5
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Eriksson O, Bhalla US, Blackwell KT, Crook SM, Keller D, Kramer A, Linne ML, Saudargienė A, Wade RC, Hellgren Kotaleski J. Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife 2022; 11:e69013. [PMID: 35792600 PMCID: PMC9259018 DOI: 10.7554/elife.69013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/13/2022] [Indexed: 12/22/2022] Open
Abstract
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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Affiliation(s)
- Olivia Eriksson
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
| | - Upinder Singh Bhalla
- National Center for Biological Sciences, Tata Institute of Fundamental ResearchBangaloreIndia
| | - Kim T Blackwell
- Department of Bioengineering, Volgenau School of Engineering, George Mason UniversityFairfaxUnited States
| | - Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State UniversityTempeUnited States
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrei Kramer
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere UniversityTampereFinland
| | - Ausra Saudargienė
- Neuroscience Institute, Lithuanian University of Health SciencesKaunasLithuania
- Department of Informatics, Vytautas Magnus UniversityKaunasLithuania
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
- Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of HeidelbergHeidelbergGermany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg UniversityHeidelbergGermany
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
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6
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Vavra O, Damborsky J, Bednar D. Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies. Biotechnol Adv 2022; 60:108009. [PMID: 35738509 DOI: 10.1016/j.biotechadv.2022.108009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/12/2022] [Accepted: 06/16/2022] [Indexed: 11/27/2022]
Abstract
Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments.
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Affiliation(s)
- Ondrej Vavra
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekařská 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekařská 53, 656 91 Brno, Czech Republic; Enantis, INBIT, Kamenice 34, 625 00 Brno, Czech Republic.
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekařská 53, 656 91 Brno, Czech Republic.
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7
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Cholko T, Kaushik S, Wu KY, Montes R, Chang CEA. GeomBD3: Brownian Dynamics Simulation Software for Biological and Engineered Systems. J Chem Inf Model 2022; 62:2257-2263. [PMID: 35549473 DOI: 10.1021/acs.jcim.1c01387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
GeomBD3 is a robust Brownian dynamics simulation package designed to easily handle natural or engineered systems in diverse environments and arrangements. The software package described herein allows users to design, execute, and analyze BD simulations. The simulations use all-atom, rigid molecular models that diffuse according to overdamped Langevin dynamics and interact through electrostatic, Lennard-Jones, and ligand desolvation potentials. The program automatically calculates molecular association rates, surface residence times, and association statistics for any number of user-defined association criteria. Users can also extract molecular association pathways, diffusion coefficients, intermolecular interaction energies, intermolecular contact probability maps, and more using the provided supplementary analysis scripts. We detail the use of the package from start to finish and apply it to a protein-ligand system and a large nucleic acid biosensor. GeomBD3 provides a versatile tool for researchers from various disciplines that can aid in rational design of engineered systems or play an explanatory role as a complement to experiments. GeomBD version 3 is available on our website at http://chemcha-gpu0.ucr.edu/geombd3/ and KBbox at https://kbbox.h-its.org/toolbox/methods/molecular-simulation/geombd/.
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Affiliation(s)
- Timothy Cholko
- Department of Chemistry, University of California, Riverside, Riverside, California 92521, United States
| | - Shivansh Kaushik
- Department of Chemistry, University of California, Riverside, Riverside, California 92521, United States
| | - Kingsley Y Wu
- Department of Chemistry, University of California, Riverside, Riverside, California 92521, United States
| | - Ruben Montes
- Department of Chemistry, University of California, Riverside, Riverside, California 92521, United States
| | - Chia-En A Chang
- Department of Chemistry, University of California, Riverside, Riverside, California 92521, United States
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8
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Briggs K, Bouhamidi MY, He L, Tabard-Cossa V. Efficient Simulation of Arbitrary Multicomponent First-Order Binding Kinetics for Improved Assay Design and Molecular Assembly. ACS MEASUREMENT SCIENCE AU 2022; 2:139-146. [PMID: 35479104 PMCID: PMC9026241 DOI: 10.1021/acsmeasuresciau.1c00037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
Traditional enzyme-linked immunosorbent assay (ELISA), long the workhorse for specific target protein detection using microplate wells, is nearing its fundamental limit of sensitivity. New opportunities in health care call for in vitro diagnostic tests with ultrahigh sensitivity. Magnetic bead-based sandwich immunoassay formats have been developed that can reach unprecedented sensitivities, orders of magnitude better than are allowed for by the rate constants for a single ligand-receptor interaction. However, these ultrahigh sensitivity assays are vulnerable to a host of confounding factors, including nonspecific binding from background molecules and loss of low-abundance target to tube walls and during wash steps. Moreover, the optimization of workflow is often time-consuming and expensive. In this work, we present a simulation tool that allows users to graphically define arbitrary binding assays, including fully reversible first-order binding kinetics, timed addition of extra components, and timed wash steps. The tool is freely available as a user-friendly webapp. The framework is lightweight and fast, allowing for inexpensive simulation and visualization of arbitrarily complex assay schemes, including but not limited to digital immunoassays, DNA hybridization, and enzyme kinetics, for validation and optimization of assay designs without requiring any programming knowledge from the user. We demonstrate some of these capabilities and provide practical guidance on assay simulation design.
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9
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Róg T, Girych M, Bunker A. Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design. Pharmaceuticals (Basel) 2021; 14:1062. [PMID: 34681286 PMCID: PMC8537670 DOI: 10.3390/ph14101062] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/14/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022] Open
Abstract
We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.
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Affiliation(s)
- Tomasz Róg
- Department of Physics, University of Helsinki, 00014 Helsinki, Finland;
| | - Mykhailo Girych
- Department of Physics, University of Helsinki, 00014 Helsinki, Finland;
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00014 Helsinki, Finland;
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10
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Bianciotto M, Gkeka P, Kokh DB, Wade RC, Minoux H. Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics. J Chem Theory Comput 2021; 17:6522-6535. [PMID: 34494849 DOI: 10.1021/acs.jctc.1c00453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.
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Affiliation(s)
- Marc Bianciotto
- Molecular Design Sciences, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | - Paraskevi Gkeka
- Molecular Design Sciences, Sanofi R&D, 91 385 Chilly-Mazarin, France
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, 69120 Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany
| | - Hervé Minoux
- Data and Data Science, Sanofi R&D, 91 385 Chilly-Mazarin, France
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11
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Ganotra GK, Nunes-Alves A, Wade RC. A Protocol to Use Comparative Binding Energy Analysis to Estimate Drug-Target Residence Time. Methods Mol Biol 2021; 2266:171-186. [PMID: 33759127 DOI: 10.1007/978-1-0716-1209-5_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Comparative Binding Energy (COMBINE) analysis is an approach for deriving a target-specific scoring function to compute binding free energy, drug-binding kinetics, or a related property by exploiting the information contained in the three-dimensional structures of receptor-ligand complexes. Here, we describe the process of setting up and running COMBINE analysis to derive a Quantitative Structure-Kinetics Relationship (QSKR) for the dissociation rate constants (koff) of inhibitors of a drug target. The derived QSKR model can be used to estimate residence times (τ, τ=1/koff) for similar inhibitors binding to the same target, and it can also help to identify key receptor-ligand interactions that distinguish inhibitors with short and long residence times. Herein, we demonstrate the protocol for the application of COMBINE analysis on a dataset of 70 inhibitors of heat shock protein 90 (HSP90) belonging to 11 different chemical classes. The procedure is generally applicable to any drug target with known structural information on its complexes with inhibitors.
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Affiliation(s)
- Gaurav K Ganotra
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Ariane Nunes-Alves
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany.
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
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12
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Abstract
Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.
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13
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Nunes-Alves A, Kokh DB, Wade RC. Recent progress in molecular simulation methods for drug binding kinetics. Curr Opin Struct Biol 2020; 64:126-133. [PMID: 32771530 DOI: 10.1016/j.sbi.2020.06.022] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/23/2020] [Accepted: 06/23/2020] [Indexed: 12/29/2022]
Abstract
Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.
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Affiliation(s)
- Ariane Nunes-Alves
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany.
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