1
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Mandal N, Surpeta B, Brezovsky J. Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics. J Chem Inf Model 2024; 64:6623-6635. [PMID: 39143923 DOI: 10.1021/acs.jcim.4c00966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.
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Affiliation(s)
- Nishita Mandal
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
| | - Bartlomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
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2
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Ray D, Parrinello M. Kinetics from Metadynamics: Principles, Applications, and Outlook. J Chem Theory Comput 2023; 19:5649-5670. [PMID: 37585703 DOI: 10.1021/acs.jctc.3c00660] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.
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Affiliation(s)
- Dhiman Ray
- Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
| | - Michele Parrinello
- Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genova, Italy
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3
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Wong CF. 15 Years of molecular simulation of drug-binding kinetics. Expert Opin Drug Discov 2023; 18:1333-1348. [PMID: 37789731 PMCID: PMC10926948 DOI: 10.1080/17460441.2023.2264770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 09/26/2023] [Indexed: 10/05/2023]
Abstract
INTRODUCTION Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made. AREAS COVERED This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years. EXPERT OPINION The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.
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Affiliation(s)
- Chung F Wong
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, MO, USA
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4
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Joshi DC, Gosse C, Huang SY, Lin JH. A Curvilinear-Path Umbrella Sampling Approach to Characterizing the Interactions Between Rapamycin and Three FKBP12 Variants. Front Mol Biosci 2022; 9:879000. [PMID: 35874613 PMCID: PMC9304761 DOI: 10.3389/fmolb.2022.879000] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Rapamycin is an immunosuppressant macrolide that exhibits anti-proliferative properties through inhibiting the mTOR kinase. In fact, the drug first associates with the FKBP12 enzyme before interacting with the FRB domain of its target. Despite the availability of structural and thermodynamic information on the interaction of FKBP12 with rapamycin, the energetic and mechanistic understanding of this process is still incomplete. We recently reported a multiple-walker umbrella sampling simulation approach to characterizing the protein–protein interaction energetics along curvilinear paths. In the present paper, we extend our investigations to a protein-small molecule duo, the FKBP12•rapamycin complex. We estimate the binding free energies of rapamycin with wild-type FKBP12 and two mutants in which a hydrogen bond has been removed, D37V and Y82F. Furthermore, the underlying mechanistic details are analyzed. The calculated standard free energies of binding agree well with the experimental data, and the roles of the hydrogen bonds are shown to be quite different for each of these two mutated residues. On one hand, removing the carboxylate group of D37 strongly destabilizes the association; on the other hand, the hydroxyl group of Y82 is nearly unnecessary for the stability of the complex because some nonconventional, cryptic, indirect interaction mechanisms seem to be at work.
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Affiliation(s)
| | - Charlie Gosse
- Institut de Biologie de l’Ecole Normale Supérieure, ENS, CNRS, INSERM, PSL Research University, Paris, France
| | - Shu-Yu Huang
- Research Center for Applied Sciences, Academia Sinica, Taipei, Taiwan
| | - Jung-Hsin Lin
- Research Center for Applied Sciences, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
- Biomedical Translation Research Center, National Biotechnology Research Park, Academia Sinica, Taipei, Taiwan
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- College of Engineering Sciences, Chang Gung University, Taoyuan, Taiwan
- *Correspondence: Jung-Hsin Lin,
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5
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Ray D, Stone SE, Andricioaei I. Markovian Weighted Ensemble Milestoning (M-WEM): Long-Time Kinetics from Short Trajectories. J Chem Theory Comput 2021; 18:79-95. [PMID: 34910499 DOI: 10.1021/acs.jctc.1c00803] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We introduce a rare-event sampling scheme, named Markovian Weighted Ensemble Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian milestoning theory to efficiently calculate thermodynamic and kinetic properties of long-time-scale biomolecular processes from short atomistic molecular dynamics simulations. M-WEM is tested on the Müller-Brown potential model, the conformational switching in alanine dipeptide, and the millisecond time-scale protein-ligand unbinding in a trypsin-benzamidine complex. Not only can M-WEM predict the kinetics of these processes with quantitative accuracy but it also allows for a scheme to reconstruct a multidimensional free-energy landscape along additional degrees of freedom, which are not part of the milestoning progress coordinate. For the ligand-receptor system, the experimental residence time, association and dissociation kinetics, and binding free energy could be reproduced using M-WEM within a simulation time of a few hundreds of nanoseconds, which is a fraction of the computational cost of other currently available methods, and close to 4 orders of magnitude less than the experimental residence time. Due to the high accuracy and low computational cost, the M-WEM approach can find potential applications in kinetics and free-energy-based computational drug design.
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Affiliation(s)
- Dhiman Ray
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
| | - Sharon Emily Stone
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, Irvine, California 92697, United States.,Department of Physics and Astronomy, University of California Irvine, Irvine, California 92697, United States
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6
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Zhang Q, Zhao N, Meng X, Yu F, Yao X, Liu H. The prediction of protein-ligand unbinding for modern drug discovery. Expert Opin Drug Discov 2021; 17:191-205. [PMID: 34731059 DOI: 10.1080/17460441.2022.2002298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods. AREAS COVERED In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed. EXPERT OPINION Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.
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Affiliation(s)
| | - Nannan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Xiaoxiao Meng
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Fansen Yu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China.,Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou, China
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7
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Sohraby F, Javaheri Moghadam M, Aliyar M, Aryapour H. A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: probing the binding pathway of dasatinib to Src-kinase. Bioinformatics 2021; 36:4714-4720. [PMID: 32525544 DOI: 10.1093/bioinformatics/btaa565] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Small molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased MD simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased MD simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) MD simulations, in this case a protein-ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding's process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nanosecond time scales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction. AVAILABILITY AND IMPLEMENTATION The links of the tutorial and technical documents are accessible in the article. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Farzin Sohraby
- Department of Biology, Faculty of Science, Golestan University, Gorgan 4913815759, Iran
| | | | - Masoud Aliyar
- Department of Biology, Faculty of Science, Golestan University, Gorgan 4913815759, Iran
| | - Hassan Aryapour
- Department of Biology, Faculty of Science, Golestan University, Gorgan 4913815759, Iran
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8
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Lotz S, Dickson A. Wepy: A Flexible Software Framework for Simulating Rare Events with Weighted Ensemble Resampling. ACS OMEGA 2020; 5:31608-31623. [PMID: 33344813 PMCID: PMC7745226 DOI: 10.1021/acsomega.0c03892] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/15/2020] [Indexed: 05/03/2023]
Abstract
Here, we introduce the open-source software framework wepy (https://github.com/ADicksonLab/wepy) which is a toolkit for running and analyzing weighted ensemble (WE) simulations. The wepy toolkit is in pure Python and as such is highly portable and extensible, making it an excellent platform to develop and use new WE resampling algorithms such as WExplore, REVO, and others while leveraging the entire Python ecosystem. In addition, wepy simplifies WE-specific analyses by defining out-of-core tree-like data structures using the cross-platform HDF5 file format. In this paper, we discuss the motivations and challenges for simulating rare events in biomolecular systems. As has previously been shown, high-dimensional WE resampling algorithms such as WExplore and REVO have been successful at these tasks, especially for rare events that are difficult to describe by one or two collective variables. We explain in detail how wepy facilitates implementation of these algorithms, as well as aids in analyzing the unique structure of WE simulation results. To explain how wepy and WE work in general, we describe the mathematical formalism of WE, an overview of the architecture of wepy, and provide code examples of how to construct, run, and analyze simulation results for a protein-ligand system (T4 Lysozyme in an implicit solvent). This paper is written with a variety of readers in mind, including (1) those curious about how to leverage WE rare-event simulations for their domain, (2) current WE users who want to begin using new high-dimensional resamplers such as WExplore and REVO, and (3) expert users who would like to prototype or implement their own algorithms that can be easily adopted by others.
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Affiliation(s)
- Samuel
D. Lotz
- Department
of Biochemistry & Molecular Biology, Michigan State University, East Lansing 48824, Michigan, United States
| | - Alex Dickson
- Department
of Biochemistry & Molecular Biology, Michigan State University, East Lansing 48824, Michigan, United States
- Department
of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing 48824, Michigan, United States
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9
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 127] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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10
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Ahn SH, Jagger BR, Amaro RE. Ranking of Ligand Binding Kinetics Using a Weighted Ensemble Approach and Comparison with a Multiscale Milestoning Approach. J Chem Inf Model 2020; 60:5340-5352. [PMID: 32315175 DOI: 10.1021/acs.jcim.9b00968] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
To improve lead optimization efforts in finding the right ligand, pharmaceutical industries need to know the ligand's binding kinetics, such as binding and unbinding rate constants, which often correlate with the ligand's efficacy in vivo. To predict binding kinetics efficiently, enhanced sampling methods, such as milestoning and the weighted ensemble (WE) method, have been used in molecular dynamics (MD) simulations of these systems. However, a comparison of these enhanced sampling methods in ranking ligands has not been done. Hence, a WE approach called the concurrent adaptive sampling (CAS) algorithm that uses MD simulations was used to rank seven ligands for β-cyclodextrin, a system in which a multiscale milestoning approach called simulation enabled estimation of kinetic rates (SEEKR) was also used, which uses both MD and Brownian dynamics simulations. Overall, the CAS algorithm can successfully rank ligands using the unbinding rate constant koff values and binding free energy ΔG values, as SEEKR did, with reduced computational cost that is about the same as SEEKR. We compare the CAS algorithm simulations with different parameters and discuss the impact of parameters in ranking ligands and obtaining rate constant and binding free energy estimates. We also discuss similarities and differences and advantages and disadvantages of SEEKR and the CAS algorithm for future use.
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Affiliation(s)
- Surl-Hee Ahn
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Benjamin R Jagger
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
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11
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Ray D, Gokey T, Mobley DL, Andricioaei I. Kinetics and free energy of ligand dissociation using weighted ensemble milestoning. J Chem Phys 2020; 153:154117. [PMID: 33092382 DOI: 10.1063/5.0021953] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We consider the recently developed weighted ensemble milestoning (WEM) scheme [D. Ray and I. Andricioaei, J. Chem. Phys. 152, 234114 (2020)] and test its capability of simulating ligand-receptor dissociation dynamics. We performed WEM simulations on the following host-guest systems: Na+/Cl- ion pair and 4-hydroxy-2-butanone ligand with FK506 binding protein. As a proof of principle, we show that the WEM formalism reproduces the Na+/Cl- ion pair dissociation timescale and the free energy profile obtained from long conventional MD simulation. To increase the accuracy of WEM calculations applied to kinetics and thermodynamics in protein-ligand binding, we introduced a modified WEM scheme called weighted ensemble milestoning with restraint release (WEM-RR), which can increase the number of starting points per milestone without adding additional computational cost. WEM-RR calculations obtained a ligand residence time and binding free energy in agreement with experimental and previous computational results. Moreover, using the milestoning framework, the binding time and rate constants, dissociation constants, and committor probabilities could also be calculated at a low computational cost. We also present an analytical approach for estimating the association rate constant (kon) when binding is primarily diffusion driven. We show that the WEM method can efficiently calculate multiple experimental observables describing ligand-receptor binding/unbinding and is a promising candidate for computer-aided inhibitor design.
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Affiliation(s)
- Dhiman Ray
- Department of Chemistry, University of California Irvine, Irvine, California 92697, USA
| | - Trevor Gokey
- Department of Chemistry, University of California Irvine, Irvine, California 92697, USA
| | - David L Mobley
- Department of Chemistry, University of California Irvine, Irvine, California 92697, USA
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, Irvine, California 92697, USA
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12
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Du Y, Wang R. Revealing the Unbinding Kinetics and Mechanism of Type I and Type II Protein Kinase Inhibitors by Local-Scaled Molecular Dynamics Simulations. J Chem Theory Comput 2020; 16:6620-6632. [PMID: 32841004 DOI: 10.1021/acs.jctc.0c00342] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein kinase inhibitors disrupt phosphorylation of the target kinases, which are an important class of drug for treating cancer and other diseases. Conventional structure-based design methods (such as molecular docking) focus on the static binding mode of the kinase inhibitor with its target. However, dissociation kinetic properties of a drug molecule are found to correlate with its residence time in vivo and thus have drawn the attention of drug designers in recent years. In this study, we have applied the local-scaled molecular dynamics (MD) simulation enabled in GROMACS software to explore the unbinding mechanism of a total of 41 type I and type II kinase inhibitors. Our simulation considered multiple starting configurations as well as possible protonation states of kinase inhibitors. Based on our local-scaled MD results, we discovered that the integrals of the favorable binding energy during dissociation correlated well (R2 = 0.64) with the experimental dissociation rate constants of those kinase inhibitors on the entire data set. Given its accuracy and technical advantage, this method may serve as a practical option for estimating this important property in reality. Our simulation also provided a reasonable explanation of the dynamic properties of kinase and its inhibitor as well as the role of relevant water molecules in dissociation.
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Affiliation(s)
- Yu Du
- 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.,University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Renxiao Wang
- 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.,University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.,Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, People's Republic of China
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13
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Jagger BR, Ojha AA, Amaro RE. Predicting Ligand Binding Kinetics Using a Markovian Milestoning with Voronoi Tessellations Multiscale Approach. J Chem Theory Comput 2020; 16:5348-5357. [DOI: 10.1021/acs.jctc.0c00495] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Benjamin R. Jagger
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Anupam A. Ojha
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Rommie E. Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
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14
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Ray D, Andricioaei I. Weighted ensemble milestoning (WEM): A combined approach for rare event simulations. J Chem Phys 2020; 152:234114. [DOI: 10.1063/5.0008028] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Affiliation(s)
- Dhiman Ray
- Department of Chemistry, University of California Irvine, California 92697, USA
| | - Ioan Andricioaei
- Department of Chemistry, University of California Irvine, California 92697, USA
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15
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Hall R, Dixon T, Dickson A. On Calculating Free Energy Differences Using Ensembles of Transition Paths. Front Mol Biosci 2020; 7:106. [PMID: 32582764 PMCID: PMC7291376 DOI: 10.3389/fmolb.2020.00106] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/06/2020] [Indexed: 12/30/2022] Open
Abstract
The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of association (kon) and dissociation (koff)—have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Some methods exist to calculate binding kinetics from molecular simulations, although these are typically more difficult to calculate than the binding affinity as they depend on details of the transition path ensemble. Assessing these rate constants can be difficult, due to uncertainty in the definition of the bound and unbound states, large error bars and the lack of experimental data. As an additional consistency check, rate constants from simulation can be used to calculate free energies (using the log of their ratio) which can then be compared to free energies obtained experimentally or using alchemical free energy perturbation. However, in this calculation it is not straightforward to account for common, practical details such as the finite simulation volume or the particular definition of the “bound” and “unbound” states. Here we derive a set of correction terms that can be applied to calculations of binding free energies using full reactive trajectories. We apply these correction terms to revisit the calculation of binding free energies from rate constants for a host-guest system that was part of a blind prediction challenge, where significant deviations were observed between free energies calculated with rate ratios and those calculated from alchemical perturbation. The correction terms combine to significantly decrease the error with respect to computational benchmarks, from 3.4 to 0.76 kcal/mol. Although these terms were derived with weighted ensemble simulations in mind, some of the correction terms are generally applicable to free energies calculated using physical pathways via methods such as Markov state modeling, metadynamics, milestoning, or umbrella sampling.
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Affiliation(s)
- Robert Hall
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Tom Dixon
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.,Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Alex Dickson
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, United States.,Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, United States
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16
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Wan H, Voelz VA. Adaptive Markov state model estimation using short reseeding trajectories. J Chem Phys 2020; 152:024103. [PMID: 31941308 DOI: 10.1063/1.5142457] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow time scales. A promising approach to enhanced sampling of MSMs is to use "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape and mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.
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Affiliation(s)
- Hongbin Wan
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, USA
| | - Vincent A Voelz
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, USA
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17
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Limongelli V. Ligand binding free energy and kinetics calculation in 2020. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1455] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science – Center for Computational Medicine in Cardiology Università della Svizzera italiana (USI) Lugano Switzerland
- Department of Pharmacy University of Naples “Federico II” Naples Italy
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18
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Bogetti AT, Mostofian B, Dickson A, Pratt AJ, Saglam AS, Harrison PO, Adelman JL, Dudek M, Torrillo PA, DeGrave AJ, Adhikari U, Zwier MC, Zuckerman DM, Chong LT. A Suite of Tutorials for the WESTPA Rare-Events Sampling Software [Article v1.0]. ACTA ACUST UNITED AC 2019; 1. [PMID: 32395705 DOI: 10.33011/livecoms.1.2.10607] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present five tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. Users are expected to already have significant experience with running standard molecular dynamics simulations using the underlying dynamics engine of interest (e.g. Amber, Gromacs, OpenMM). The tutorials range from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding.
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Affiliation(s)
| | - Barmak Mostofian
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - Alex Dickson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI
| | - A J Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Ali S Saglam
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Page O Harrison
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Joshua L Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA; currently unaffiliated
| | - Max Dudek
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Paul A Torrillo
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
| | - Alex J DeGrave
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA.,Current address: Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA
| | - Upendra Adhikari
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR.,Current address: Department of Chemistry, Missouri Valley College, Marshall, MO
| | | | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA
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19
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Donyapour N, Roussey NM, Dickson A. REVO: Resampling of ensembles by variation optimization. J Chem Phys 2019; 150:244112. [PMID: 31255090 PMCID: PMC7043833 DOI: 10.1063/1.5100521] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 05/31/2019] [Indexed: 11/17/2022] Open
Abstract
Conventional molecular dynamics simulations are incapable of sampling many important interactions in biomolecular systems due to their high dimensionality and rough energy landscapes. To observe rare events and calculate transition rates in these systems, enhanced sampling is a necessity. In particular, the study of ligand-protein interactions necessitates a diverse ensemble of protein conformations and transition states, and for many systems, this occurs on prohibitively long time scales. Previous strategies such as WExplore that can be used to determine these types of ensembles are hindered by problems related to the regioning of conformational space. Here, we propose a novel, regionless, enhanced sampling method that is based on the weighted ensemble framework. In this method, a value referred to as "trajectory variation" is optimized after each cycle through cloning and merging operations. This method allows for a more consistent measurement of observables and broader sampling resulting in the efficient exploration of previously unexplored conformations. We demonstrate the performance of this algorithm with the N-dimensional random walk and the unbinding of the trypsin-benzamidine system. The system is analyzed using conformation space networks, the residence time of benzamidine is confirmed, and a new unbinding pathway for the trypsin-benzamidine system is found. We expect that resampling of ensembles by variation optimization will be a useful general tool to broadly explore free energy landscapes.
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Affiliation(s)
- Nazanin Donyapour
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan 48824-1312, USA
| | - Nicole M Roussey
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824-1312, USA
| | - Alex Dickson
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan 48824-1312, USA
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20
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Abstract
The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/ koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.
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Affiliation(s)
- Mattia Bernetti
- Department of Pharmacy and Biotechnology, University of Bologna, I-40126 Bologna, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, University of Bologna, I-40126 Bologna, Italy
| | - Walter Rocchia
- CONCEPT Laboratory, Istituto Italiano di Tecnologia, I-16163 Genova, Italy
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology, University of Bologna, I-40126 Bologna, Italy
- Computational Sciences Domain, Istituto Italiano di Tecnologia, I-16163 Genova, Italy
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21
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Kokh DB, Kaufmann T, Kister B, Wade RC. Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times. Front Mol Biosci 2019; 6:36. [PMID: 31179286 PMCID: PMC6543870 DOI: 10.3389/fmolb.2019.00036] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 05/02/2019] [Indexed: 12/25/2022] Open
Abstract
Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.
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Affiliation(s)
- Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Tom Kaufmann
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.,Department of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Bastian Kister
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.,Department of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany.,Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.,Department of Physics, Heidelberg University, Heidelberg, Germany
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22
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Pramanik D, Smith Z, Kells A, Tiwary P. Can One Trust Kinetic and Thermodynamic Observables from Biased Metadynamics Simulations?: Detailed Quantitative Benchmarks on Millimolar Drug Fragment Dissociation. J Phys Chem B 2019; 123:3672-3678. [DOI: 10.1021/acs.jpcb.9b01813] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Debabrata Pramanik
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
| | - Zachary Smith
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
| | - Adam Kells
- Department of Chemistry, King’s College London, SE1 1DB, London, U.K
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
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23
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Saglam AS, Chong LT. Protein-protein binding pathways and calculations of rate constants using fully-continuous, explicit-solvent simulations. Chem Sci 2019; 10:2360-2372. [PMID: 30881664 PMCID: PMC6385678 DOI: 10.1039/c8sc04811h] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 12/26/2018] [Indexed: 11/21/2022] Open
Abstract
A grand challenge in the field of biophysics has been the complete characterization of protein-protein binding processes at atomic resolution. This characterization requires the direct simulation of binding pathways starting from the initial, unbound state and proceeding through states that are too transient to be captured by experiment. Here, we applied the weighted ensemble path sampling strategy to orchestrate atomistic simulation of protein-protein binding pathways. Our simulation generated 203 fully-continuous and independent pathways along with rate constants for the binding process involving the barnase and barstar proteins. Results reveal multiple binding pathways along a "funnel-like" free energy landscape in which the formation of the "encounter complex" intermediate is rate-limiting followed by a relatively rapid rearrangement of the encounter complex to the bound state. Among all diffusional collisions, only ∼11% were productive. In the most probable binding pathways, the proteins rotated to a large extent (likely via electrostatic steering) in order to collide productively followed by "rolling" of the proteins along each other's binding interfaces to reach the bound state. Consistent with experiment, R59 was identified as the most kinetically important barnase residue for the binding process. Furthermore, protein desolvation occurs late in the binding process during the rearrangement of the encounter complex to the bound state. Notably, the positions of crystallographic water molecules that bridge hydrogen bonds between barnase and barstar are occupied in the bound-state ensemble. Our simulation was completed in a month using 1600 CPU cores at a time, demonstrating that it is now practical to carry out atomistic simulations of protein-protein binding.
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Affiliation(s)
- Ali S Saglam
- University of Pittsburgh , Department of Chemistry , 219 Parkman Avenue , Pittsburgh , PA 15260 , USA . ; Tel: +1-412-624-6026
| | - Lillian T Chong
- University of Pittsburgh , Department of Chemistry , 219 Parkman Avenue , Pittsburgh , PA 15260 , USA . ; Tel: +1-412-624-6026
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24
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Nunes-Alves A, Zuckerman DM, Arantes GM. Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways. Biophys J 2019. [PMID: 29539393 DOI: 10.1016/j.bpj.2018.01.014] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The T4 lysozyme L99A mutant is often used as a model system to study small-molecule binding to proteins, but pathways for ligand entry and exit from the buried binding site and the associated protein conformational changes have not been fully resolved. Here, molecular dynamics simulations were employed to model benzene exit from its binding cavity using the weighted ensemble (WE) approach to enhance sampling of low-probability unbinding trajectories. Independent WE simulations revealed four pathways for benzene exit, which correspond to transient tunnels spontaneously formed in previous simulations of apo T4 lysozyme. Thus, benzene unbinding occurs through multiple pathways partially created by intrinsic protein structural fluctuations. Motions of several α-helices and side chains were involved in ligand escape from metastable microstates. WE simulations also provided preliminary estimates of rate constants for each exit pathway. These results complement previous works and provide a semiquantitative characterization of pathway heterogeneity for binding of small molecules to proteins.
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Affiliation(s)
- Ariane Nunes-Alves
- Department of Biochemistry, Instituto de Química, Universidade de São Paulo, São Paulo, Brazil
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, Oregon.
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25
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Betz RM, Dror RO. How Effectively Can Adaptive Sampling Methods Capture Spontaneous Ligand Binding? J Chem Theory Comput 2019; 15:2053-2063. [PMID: 30645108 PMCID: PMC6795214 DOI: 10.1021/acs.jctc.8b00913] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
![]()
Molecular dynamics (MD) simulations
that capture the spontaneous
binding of drugs and other ligands to their target proteins can reveal
a great deal of useful information, but most drug-like ligands bind
on time scales longer than those accessible to individual MD simulations.
Adaptive sampling methods―in which one performs multiple rounds
of simulation, with the initial conditions of each round based on
the results of previous rounds―offer a promising potential
solution to this problem. No comprehensive analysis of the performance
gains from adaptive sampling is available for ligand binding, however,
particularly for protein–ligand systems typical of those encountered
in drug discovery. Moreover, most previous work presupposes knowledge
of the ligand’s bound pose. Here we outline existing methods
for adaptive sampling of the ligand-binding process and introduce
several improvements, with a focus on methods that do not require
prior knowledge of the binding site or bound pose. We then evaluate
these methods by comparing them to traditional, long MD simulations
for realistic protein–ligand systems. We find that adaptive
sampling simulations typically fail to reach the bound pose more efficiently
than traditional MD. However, adaptive sampling identifies multiple
potential binding sites more efficiently than traditional MD and also
provides better characterization of binding pathways. We explain these
results by showing that protein–ligand binding is an example
of an exploration–exploitation dilemma. Existing adaptive sampling
methods for ligand binding in the absence of a known bound pose vastly
favor the broad exploration of protein–ligand space, sometimes
failing to sufficiently exploit intermediate states as they are discovered.
We suggest potential avenues for future research to address this shortcoming.
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Affiliation(s)
- Robin M Betz
- Biophysics Program , Stanford University , Stanford , California 94305 , United States.,Department of Computer Science , Stanford University , Stanford, California 94305 , United States.,Department of Molecular and Cellular Physiology , Stanford University , Stanford , California 94305 , United States.,Department of Structural Biology , Stanford University , Stanford , California 94305 , United States.,Institute for Computational and Mathematical Engineering , Stanford University , Stanford , California 94305 , United States
| | - Ron O Dror
- Biophysics Program , Stanford University , Stanford , California 94305 , United States.,Department of Computer Science , Stanford University , Stanford, California 94305 , United States.,Department of Molecular and Cellular Physiology , Stanford University , Stanford , California 94305 , United States.,Department of Structural Biology , Stanford University , Stanford , California 94305 , United States.,Institute for Computational and Mathematical Engineering , Stanford University , Stanford , California 94305 , United States
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26
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Ribeiro JML, Tsai ST, Pramanik D, Wang Y, Tiwary P. Kinetics of Ligand-Protein Dissociation from All-Atom Simulations: Are We There Yet? Biochemistry 2018; 58:156-165. [PMID: 30547565 DOI: 10.1021/acs.biochem.8b00977] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Large parallel gains in the development of both computational resources and sampling methods have now made it possible to simulate dissociation events in ligand-protein complexes with all-atom resolution. Such encouraging progress, together with the inherent spatiotemporal resolution associated with molecular simulations, has left their use for investigating dissociation processes brimming with potential, both in rational drug design, where it can be an invaluable tool for determining the mechanistic driving forces behind dissociation rate constants, and in force-field development, where it can provide a catalog of transient molecular structures with which to refine force fields. Although much progress has been made in making force fields more accurate, reducing their error for transient structures along a transition path could yet prove to be a critical development helping to make kinetic predictions much more accurate. In what follows, we will provide a state-of-the-art compilation of the enhanced sampling methods based on molecular dynamics (MD) simulations used to investigate the kinetics and mechanisms of ligand-protein dissociation processes. Due to the time scales of such processes being slower than what is accessible using straightforward MD simulations, several ingenious schemes are being devised at a rapid rate to overcome this obstacle. Here we provide an up-to-date compendium of such methods and their achievements and shortcomings in extracting mechanistic insight into ligand-protein dissociation. We conclude with a critical and provocative appraisal attempting to answer the title of this Perspective.
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Affiliation(s)
- João Marcelo Lamim Ribeiro
- Department of Chemistry and Biochemistry , University of Maryland , College Park , Maryland 20742 , United States.,Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States
| | - Sun-Ting Tsai
- Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States.,Department of Physics , University of Maryland , College Park , Maryland 20742 , United States
| | - Debabrata Pramanik
- Department of Chemistry and Biochemistry , University of Maryland , College Park , Maryland 20742 , United States.,Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States
| | - Yihang Wang
- Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States.,Biophysics Program , University of Maryland , College Park , Maryland 20742 , United States
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry , University of Maryland , College Park , Maryland 20742 , United States.,Institute for Physical Science and Technology , University of Maryland , College Park , Maryland 20742 , United States
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27
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Schuetz DA, Bernetti M, Bertazzo M, Musil D, Eggenweiler HM, Recanatini M, Masetti M, Ecker GF, Cavalli A. Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics. J Chem Inf Model 2018; 59:535-549. [DOI: 10.1021/acs.jcim.8b00614] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Doris A. Schuetz
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Mattia Bernetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
| | - Martina Bertazzo
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
| | - Djordje Musil
- Discovery Technologies, Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | | | - Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry, University of Vienna, UZA 2, Althanstrasse 14, 1090 Vienna, Austria
| | - Andrea Cavalli
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy
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28
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Dickson A. Mapping the Ligand Binding Landscape. Biophys J 2018; 115:1707-1719. [PMID: 30327139 PMCID: PMC6224774 DOI: 10.1016/j.bpj.2018.09.021] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 09/24/2018] [Accepted: 09/25/2018] [Indexed: 12/31/2022] Open
Abstract
The interaction between a ligand and a protein involves a multitude of conformational states. To achieve a particular deeply bound pose, the ligand must search across a rough free-energy landscape with many metastable minima. Creating maps of the ligand binding landscape is a great challenge, as binding and release events typically occur on timescales that are beyond the reach of molecular simulation. The WExplore enhanced sampling method is well suited to build these maps because it is designed to broadly explore free-energy landscapes and is capable of simulating ligand release pathways that occur on timescales as long as minutes. WExplore also uses only unbiased trajectory segments, allowing for the construction of Markov state models (MSMs) and conformation space networks that combine the results of multiple simulations. Here, we use WExplore to study two bromodomain-inhibitor systems using multiple docked starting poses (Brd4-MS436 and Baz2B-ICR7) and synthesize our results using a series of MSMs using time-lagged independent component analysis. Ranking the starting poses by exit rate agrees with the crystal structure pose in both cases. We also predict the most stable pose using the equilibrium populations from the MSM but find that the prediction is not robust as a function of MSM parameters. The simulated trajectories are synthesized into network models that visualize the entire binding landscape for each system, and we examine transition paths between deeply bound stable states. We find that, on average, transitions between deeply bound states convert through the unbound state 81% of the time, implying a trial-and-error approach to ligand binding. We conclude with a discussion of the implications of this result for both kinetics-based drug discovery and virtual screening pipelines that incorporate molecular dynamics.
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Affiliation(s)
- Alex Dickson
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan.
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29
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Jagger BR, Lee CT, Amaro RE. Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach. J Phys Chem Lett 2018; 9:4941-4948. [PMID: 30070844 PMCID: PMC6443090 DOI: 10.1021/acs.jpclett.8b02047] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Efficient prediction and ranking of small molecule binders by their kinetic ( kon and koff) and thermodynamic ( Δ G) properties can be a valuable metric for drug lead optimization, as these quantities are often indicators of in vivo efficacy. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon's, koff's, and Δ G's. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, β-cyclodextrin, based on predicted kon's and koff's. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long time scale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and Δ G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish "best practices" for its future use.
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Affiliation(s)
- Benjamin R Jagger
- Department of Chemistry and Biochemistry , University of California, San Diego , 9500 Gilman Drive , La Jolla , California 92093-0340 , United States
| | - Christopher T Lee
- Department of Chemistry and Biochemistry , University of California, San Diego , 9500 Gilman Drive , La Jolla , California 92093-0340 , United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry , University of California, San Diego , 9500 Gilman Drive , La Jolla , California 92093-0340 , United States
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30
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Ahn SH, Grate JW, Darve EF. Investigating the role of non-covalent interactions in conformation and assembly of triazine-based sequence-defined polymers. J Chem Phys 2018; 149:072330. [DOI: 10.1063/1.5024552] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Surl-Hee Ahn
- Chemistry Department, Stanford University, Stanford, California 94305, USA
| | - Jay W. Grate
- Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Eric F. Darve
- Mechanical Engineering Department, Stanford University, Stanford, California 94305, USA
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31
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Prabhu DS, Rajeswari VD. In vitro and in silico analyses of Vicia faba L. on Peroxisome proliferator-activated receptor gamma. J Cell Biochem 2018; 119:7729-7737. [PMID: 29923224 DOI: 10.1002/jcb.27123] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 05/07/2018] [Indexed: 01/08/2023]
Abstract
The agonists of peroxisome proliferator-activated receptor gamma (PPARγ) from natural victual products were used as antidiabetic agents. Faba bean (Vicia faba L.) is a consequential legume that was known to possess potential antidiabetic activity, whose mechanism of action was unknown. The current study was focused to ascertain gene expression of the nuclear receptor PPARγ by Faba bean pod extract in rat cell lines (RINm5F).The real-time polymerase chain reaction analysis demonstrated that Faba bean pod extract in concentrations of 160 µg/mL have shown 4.97-fold stimulation compared with control. The cells treated with 320 µg/mL has shown 5.89-fold upregulation, respectively. Furthermore, in silico docking analysis was carried out against PPARγ, using the bioactive compounds identified from Faba bean pod extracts, which were known reported compounds from the literature. The results suggest that gene expression of PPARγ was inhibited by the constituents in Faba bean. In silico analysis prognosticates, butein has a high binding energy (-8.6 kcal/mol) with an atomic contact energy of -214.10, followed by Apigenin and Quercetin against PPARγ. Similarly, the percentage of interaction was high for butein, followed by Apigenin and Quercetin than other compounds comparatively. Hence, the results conclude inhibition of PPARγ by the bioactive compounds from Faba bean, which may provide insights into developing future therapeutic molecules for diabetes mellitus.
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Affiliation(s)
- D Sathya Prabhu
- Department of Biomedical Sciences, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India
| | - V Devi Rajeswari
- Department of Biomedical Sciences, School of Biosciences and Technology, VIT University, Vellore, Tamil Nadu, India
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Kokh DB, Amaral M, Bomke J, Grädler U, Musil D, Buchstaller HP, Dreyer MK, Frech M, Lowinski M, Vallee F, Bianciotto M, Rak A, Wade RC. Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations. J Chem Theory Comput 2018; 14:3859-3869. [PMID: 29768913 DOI: 10.1021/acs.jctc.8b00230] [Citation(s) in RCA: 155] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Drug-target residence time (τ), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, τ-random acceleration molecular dynamics (τRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed τRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that τRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.
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Affiliation(s)
- Daria B Kokh
- Molecular and Cellular Modeling Group , Heidelberg Institute for Theoretical Studies , Heidelberg 69118 , Germany
| | - Marta Amaral
- Molecular Interactions and Biophysics , Merck KGaA , Darmstadt 64293 , Germany.,Instituto de Biologia Experimental e Tecnológica, Oeiras 2780-157 , Portugal
| | - Joerg Bomke
- Molecular Pharmacology , Merck KGaA , Darmstadt 64293 , Germany
| | - Ulrich Grädler
- Molecular Interactions and Biophysics , Merck KGaA , Darmstadt 64293 , Germany
| | - Djordje Musil
- Molecular Interactions and Biophysics , Merck KGaA , Darmstadt 64293 , Germany
| | | | - Matthias K Dreyer
- R&D Integrated Drug Discovery , Sanofi-Aventis Deutschland GmbH , Frankfurt am Main 65926 , Germany
| | - Matthias Frech
- Molecular Interactions and Biophysics , Merck KGaA , Darmstadt 64293 , Germany
| | - Maryse Lowinski
- Integrated Drug Discovery , Sanofi R&D , Vitry-sur-Seine F-94403 , France
| | - Francois Vallee
- Integrated Drug Discovery , Sanofi R&D , Vitry-sur-Seine F-94403 , France
| | - Marc Bianciotto
- Integrated Drug Discovery , Sanofi R&D , Vitry-sur-Seine F-94403 , France
| | - Alexey Rak
- Integrated Drug Discovery , Sanofi R&D , Vitry-sur-Seine F-94403 , France
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group , Heidelberg Institute for Theoretical Studies , Heidelberg 69118 , Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance , Heidelberg University , Heidelberg 69120 , Germany.,Interdisciplinary Center for Scientific Computing (IWR) , Heidelberg University , Heidelberg 69120 , Germany
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33
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You W, Chang CEA. Role of Molecular Interactions and Protein Rearrangement in the Dissociation Kinetics of p38α MAP Kinase Type-I/II/III Inhibitors. J Chem Inf Model 2018; 58:968-981. [PMID: 29620886 PMCID: PMC5975198 DOI: 10.1021/acs.jcim.7b00640] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Understanding the governing factors of fast or slow inhibitor binding/unbinding assists in developing drugs with preferred kinetic properties. For inhibitors with the same binding affinity targeting different binding sites of the same protein, the kinetic behavior can profoundly differ. In this study, we investigated unbinding kinetics and mechanisms of fast (type-I) and slow (type-II/III) binders of p38α mitogen-activated protein kinase, where the crystal structures showed that type-I and type-II/III inhibitors bind to pockets with different conformations of the Asp-Phe-Gly (DFG) motif. The work used methods that combine conventional molecular dynamics (MD), accelerated molecular dynamics (AMD) simulations, and the newly developed pathway search guided by internal motions (PSIM) method to find dissociation pathways. The study focuses on revealing key interactions and molecular rearrangements that hinder ligand dissociation by using umbrella sampling and post-MD processing to examine changes in free energy during ligand unbinding. As anticipated, the initial dissociation steps all require breaking interactions that appeared in crystal structures of the bound complexes. Interestingly, for type-I inhibitors such as SB2, p38α keeps barrier-free conformational fluctuation in the ligand-bound complex and during ligand dissociation. In contrast, with a type-II/III inhibitor such as BIRB796, with the rearrangements of p38α in its bound state, ligand unbinding features energetically unfavorable protein-ligand concerted movement. Our results also show that the type-II/III inhibitors preferred dissociation pathways through the allosteric channel, which is consistent with an existing publication. The study suggests that the level of required protein rearrangement is one major determining factor of drug binding kinetics in p38α systems, providing useful information for development of inhibitors.
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Affiliation(s)
- Wanli You
- Department of Chemistry, University of California at Riverside, Riverside, California 92521, United States
| | - Chia-en A. Chang
- Department of Chemistry, University of California at Riverside, Riverside, California 92521, United States
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Abstract
The design of protein conformational switches—or proteins that change conformations in response to a signal such as ligand binding—has great potential for developing novel biosensors, diagnostic tools, and therapeutic agents. Among the defining properties of such switches, the response time has been the most challenging to optimize. Here we apply a computational design strategy in synergistic combination with biophysical experiments to rationally improve the response time of an engineered protein-based Ca2+-sensor in which the switching process occurs via mutually exclusive folding of two alternate frames. Notably, our strategy identifies mutations that increase switching rates by as much as 32-fold, achieving response times on the order of fast physiological Ca2+ fluctuations. Our computational design strategy is general and may aid in optimizing the kinetics of other protein conformational switches. The rational optimization of response times of protein conformational switches is a major challenge for biomolecular switch design. Here the authors present a generally applicable computational design strategy that in combination with biophysical experiments can improve response times using a Ca2+-sensor as an example.
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35
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Wong CF. Steered molecular dynamics simulations for uncovering the molecular mechanisms of drug dissociation and for drug screening: A test on the focal adhesion kinase. J Comput Chem 2018; 39:1307-1318. [DOI: 10.1002/jcc.25201] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 01/13/2018] [Accepted: 02/12/2018] [Indexed: 01/01/2023]
Affiliation(s)
- Chung F. Wong
- Department of Chemistry and Biochemistry and Center for Nanoscience; University of Missouri-Saint Louis; Saint Louis Missouri 63121
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36
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Lotz SD, Dickson A. Unbiased Molecular Dynamics of 11 min Timescale Drug Unbinding Reveals Transition State Stabilizing Interactions. J Am Chem Soc 2018; 140:618-628. [DOI: 10.1021/jacs.7b08572] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Samuel D Lotz
- Michigan State University, East Lansing, Michigan 48823, United States
| | - Alex Dickson
- Michigan State University, East Lansing, Michigan 48823, United States
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Adusumalli SR, Rawale DG, Rai V. Aldehydes can switch the chemoselectivity of electrophiles in protein labeling. Org Biomol Chem 2018; 16:9377-9381. [DOI: 10.1039/c8ob02897d] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The derivatization of an electrophile can switch its chemoselectivity. The aldehyde-conjugated epoxide and sulfonate ester provide the proof of principle and deliver N-terminus tagged proteins.
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Affiliation(s)
- Srinivasa Rao Adusumalli
- Organic and Bioconjugate Chemistry Laboratory
- Department of Chemistry, Indian Institute of Science Education and Research Bhopal
- Bhopal 462066
- India
| | - Dattatraya Gautam Rawale
- Organic and Bioconjugate Chemistry Laboratory
- Department of Chemistry, Indian Institute of Science Education and Research Bhopal
- Bhopal 462066
- India
| | - Vishal Rai
- Organic and Bioconjugate Chemistry Laboratory
- Department of Chemistry, Indian Institute of Science Education and Research Bhopal
- Bhopal 462066
- India
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38
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Dickson A, Lotz SD. Multiple Ligand Unbinding Pathways and Ligand-Induced Destabilization Revealed by WExplore. Biophys J 2017; 112:620-629. [PMID: 28256222 DOI: 10.1016/j.bpj.2017.01.006] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 12/22/2016] [Accepted: 01/03/2017] [Indexed: 11/28/2022] Open
Abstract
We report simulations of full ligand exit pathways for the trypsin-benzamidine system, generated using the sampling technique WExplore. WExplore is able to observe millisecond-scale unbinding events using many nanosecond-scale trajectories that are run without introducing biasing forces. The algorithm generates rare events by dividing the coordinate space into regions, on-the-fly, and balancing computational effort between regions through cloning and merging steps, as in the weighted ensemble method. The averaged exit flux yields a ligand exit rate of 180 μs, which is within an order of magnitude of the experimental value. We obtain broad sampling of ligand exit pathways, and visualize our findings using conformation space networks. The analysis shows three distinct exit channels, two of which are formed through large, rare motions of the loop regions in trypsin. This broad set of ligand-bound poses is then used to investigate general properties of ligand binding: we observe both a direct stabilizing effect of ligand-protein interactions and an indirect destabilizing effect on intraprotein interactions that is induced by the ligand. Significantly, the crystallographic binding poses are distinguished not only because their ligands induce large stabilizing effects, but also because they induce relatively low indirect destabilizations.
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Affiliation(s)
- Alex Dickson
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan; Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, Michigan.
| | - Samuel D Lotz
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan
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Abstract
The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling-the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes-protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.
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Affiliation(s)
- Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239;
| | - Lillian T Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260;
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40
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Path-sampling strategies for simulating rare events in biomolecular systems. Curr Opin Struct Biol 2016; 43:88-94. [PMID: 27984811 DOI: 10.1016/j.sbi.2016.11.019] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 11/18/2016] [Accepted: 11/21/2016] [Indexed: 12/20/2022]
Abstract
Despite more than three decades of effort with molecular dynamics simulations, long-timescale (ms and beyond) biologically relevant phenomena remain out of reach in most systems of interest. This is largely because important transitions, such as conformational changes and (un)binding events, tend to be rare for conventional simulations (<10μs). That is, conventional simulations will predominantly dwell in metastable states instead of making large transitions in complex biomolecular energy landscapes. In contrast, path sampling approaches focus computing effort specifically on transitions of interest. Such approaches have been in use for nearly 20 years in biomolecular systems and enabled the generation of pathways and calculation of rate constants for ms processes, including large protein conformational changes, protein folding, and protein (un)binding.
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Zwier MC, Pratt AJ, Adelman JL, Kaus JW, Zuckerman DM, Chong LT. Efficient Atomistic Simulation of Pathways and Calculation of Rate Constants for a Protein-Peptide Binding Process: Application to the MDM2 Protein and an Intrinsically Disordered p53 Peptide. J Phys Chem Lett 2016; 7:3440-5. [PMID: 27532687 PMCID: PMC5008990 DOI: 10.1021/acs.jpclett.6b01502] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
The characterization of protein binding processes - with all of the key conformational changes - has been a grand challenge in the field of biophysics. Here, we have used the weighted ensemble path sampling strategy to orchestrate molecular dynamics simulations, yielding atomistic views of protein-peptide binding pathways involving the MDM2 oncoprotein and an intrinsically disordered p53 peptide. A total of 182 independent, continuous binding pathways were generated, yielding a kon that is in good agreement with experiment. These pathways were generated in 15 days using 3500 cores of a supercomputer, substantially faster than would be possible with "brute force" simulations. Many of these pathways involve the anchoring of p53 residue F19 into the MDM2 binding cleft when forming the metastable encounter complex, indicating that F19 may be a kinetically important residue. Our study demonstrates that it is now practical to generate pathways and calculate rate constants for protein binding processes using atomistic simulation on typical computing resources.
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Affiliation(s)
- Matthew C. Zwier
- Department of Chemistry, Drake University, Des Moines, Iowa 50311, United States
| | - Adam J. Pratt
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Joshua L. Adelman
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Joseph W. Kaus
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Daniel M. Zuckerman
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Lillian T. Chong
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Institute of Biochemistry and Biotechnology, Martin-Luther Universität Halle-Wittenberg, Halle 06120, Germany
- Corresponding Author:
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