1
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Ibrahim PEGF, Zuccotto F, Zachariae U, Gilbert I, Bodkin M. Accurate prediction of dynamic protein-ligand binding using P-score ranking. J Comput Chem 2024; 45:1762-1778. [PMID: 38647338 DOI: 10.1002/jcc.27370] [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: 10/20/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/25/2024]
Abstract
Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.
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Affiliation(s)
- Peter E G F Ibrahim
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Fabio Zuccotto
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Ulrich Zachariae
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Ian Gilbert
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Mike Bodkin
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
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2
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Macaya L, González D, Vöhringer-Martinez E. Nonbonded Force Field Parameters from MBIS Partitioning of the Molecular Electron Density Improve Binding Affinity Predictions of the T4-Lysozyme Double Mutant. J Chem Inf Model 2024; 64:3269-3277. [PMID: 38546407 DOI: 10.1021/acs.jcim.3c01912] [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: 04/23/2024]
Abstract
The use of computer simulation for binding affinity prediction is growing in drug discovery. However, its wider use is constrained by the accuracy of the free energy calculations. The key sources of error are the force fields used to depict molecular interactions and insufficient sampling of the configurational space. To improve the quality of the force field, we developed a Python-based computational workflow. The workflow described here uses the minimal basis iterative stockholder (MBIS) method to determine atomic charges and Lennard-Jones parameters from the polarized molecular density. This is done by performing electronic structure calculations on various configurations of the ligand when it is both bound and unbound. In addition, we validated a simulation procedure that accounts for the protein and ligand degrees of freedom to precisely calculate binding free energies. This was achieved by comparing the self-adjusted mixture sampling and nonequilibrium thermodynamic integration methods using various protein and ligand conformations. The accuracy of predicting binding affinity is improved by using MBIS-derived force field parameters and a validated simulation procedure. This improvement surpasses the chemical precision for the eight aromatic ligands, reaching a root-mean-square error of 0.7 kcal/mol.
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Affiliation(s)
- Luis Macaya
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Duván González
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
| | - Esteban Vöhringer-Martinez
- Departamento de Físico-Química, Facultad de Ciencias Químicas, Universidad de Concepción, 4070386 Concepción, Chile
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3
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Smith L, Novak B, Osato M, Mobley DL, Bowman GR. PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model. J Chem Theory Comput 2024; 20:1036-1050. [PMID: 38291966 PMCID: PMC10867841 DOI: 10.1021/acs.jctc.3c00870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 02/01/2024]
Abstract
Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.
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Affiliation(s)
- Louis
G. Smith
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Borna Novak
- Department
of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Medical
Scientist Training Program, Washington University
in St. Louis, St. Louis, Missouri 63130, United
States
| | - Meghan Osato
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - David L. Mobley
- School
of Pharmacy and Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, United States
| | - Gregory R. Bowman
- Departments
of Biochemistry & Biophysics and Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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4
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Wang H. Prediction of protein-ligand binding affinity via deep learning models. Brief Bioinform 2024; 25:bbae081. [PMID: 38446737 PMCID: PMC10939342 DOI: 10.1093/bib/bbae081] [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: 11/27/2023] [Revised: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China
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5
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Corrigan RA, Thiel AC, Lynn JR, Casavant TL, Ren P, Ponder JW, Schnieders MJ. A generalized Kirkwood implicit solvent for the polarizable AMOEBA protein model. J Chem Phys 2023; 159:054102. [PMID: 37526158 PMCID: PMC10396400 DOI: 10.1063/5.0158914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Computational simulation of biomolecules can provide important insights into protein design, protein-ligand binding interactions, and ab initio biomolecular folding, among other applications. Accurate treatment of the solvent environment is essential in such applications, but the use of explicit solvents can add considerable cost. Implicit treatment of solvent effects using a dielectric continuum model is an attractive alternative to explicit solvation since it is able to describe solvation effects without the inclusion of solvent degrees of freedom. Previously, we described the development and parameterization of implicit solvent models for small molecules. Here, we extend the parameterization of the generalized Kirkwood (GK) implicit solvent model for use with biomolecules described by the AMOEBA force field via the addition of corrections to the calculation of effective radii that account for interstitial spaces that arise within biomolecules. These include element-specific pairwise descreening scale factors, a short-range neck contribution to describe the solvent-excluded space between pairs of nearby atoms, and finally tanh-based rescaling of the overall descreening integral. We then apply the AMOEBA/GK implicit solvent to a set of ten proteins and achieve an average coordinate root mean square deviation for the experimental structures of 2.0 Å across 500 ns simulations. Overall, the continued development of implicit solvent models will help facilitate the simulation of biomolecules on mechanistically relevant timescales.
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Affiliation(s)
- Rae A. Corrigan
- Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Andrew C. Thiel
- Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Jack R. Lynn
- Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Thomas L. Casavant
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa 52242, USA
| | - Pengyu Ren
- Department of Biomedical Engineering, The University of Texas in Austin, Austin, Texas 78712, USA
| | - Jay W. Ponder
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130, USA
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6
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Ligand binding free energy evaluation by Monte Carlo Recursion. Comput Biol Chem 2023; 103:107830. [PMID: 36812825 DOI: 10.1016/j.compbiolchem.2023.107830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).
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7
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Wang Z, Zheng L, Wang S, Lin M, Wang Z, Kong AWK, Mu Y, Wei Y, Li W. A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. Brief Bioinform 2023; 24:6887112. [PMID: 36502369 DOI: 10.1093/bib/bbac520] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/17/2022] [Accepted: 10/31/2022] [Indexed: 12/14/2022] Open
Abstract
The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.
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Affiliation(s)
- Zechen Wang
- School of Physics, Shandong University, Jinan, Shandong 250100, China
| | - Liangzhen Zheng
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.,Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, China
| | - Sheng Wang
- Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, China
| | - Mingzhi Lin
- Shanghai Zelixir Biotech Company Ltd., Shanghai 200030, China
| | - Zhihao Wang
- School of Physics, Shandong University, Jinan, Shandong 250100, China
| | - Adams Wai-Kin Kong
- Rolls-Royce Corporate Lab, Nanyang Technological University, Singapore 637551, Singapore
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
| | - Yanjie Wei
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Weifeng Li
- School of Physics, Shandong University, Jinan, Shandong 250100, China
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8
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Liu N, Wang X, Shan Q, Li S, Li Y, Chu B, Wang J, Zhu Y. Single Point Mutation and Its Role in Specific Pathogenicity to Reveal the Mechanism of Related Protein Families. Microbiol Spectr 2022; 10:e0092322. [PMID: 36214694 PMCID: PMC9603606 DOI: 10.1128/spectrum.00923-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 09/21/2022] [Indexed: 12/30/2022] Open
Abstract
Pyolysin (PLO) is secreted by Trueperella pyogenes as a water-soluble monomer after forming transmembrane β-barrel channels in the cell membrane by binding cholesterol. Two significantly conserved residues at domain 1 of PLO are mutated, which provides novel evidence of a relationship between conformational change and interaction with the cell membrane and uncovers the pore formation mechanism of the cholesterol-dependent cytolysin (CDC) family. Moreover, PLO is a special member of the CDCs, which the percentage of sequence identities between PLO and other CDC members is from 31% to 45%, while others are usually from 40% to 70%. It is important to understand that at very low sequence identities, models can be different in the pathogenic mechanisms of these CDC members, which are dedicated to a large number of Gram-positive bacterial pathogens. Our studies, for the first time, located and mutated two different highly conserved structural sites in the primary structure critical for PLO structure and function that proved the importance of these sites. Together, novel and repeatable observations into the pore formation mechanism of CDCs are provided by our findings. IMPORTANCE Postpartum disease of dairy cows caused by persistent bacterial infection is a global disease, which has a serious impact on the development of the dairy industry and brings huge economic losses. As one of the most relevant pathogenic bacteria for postpartum diseases in dairy cows, Trueperella pyogenes can secrete pyolysin (PLO), a member of the cholesterol-dependent cytolysin (CDC) family and recognized as the most important toxin of T. pyogenes. However, the current research work on PLO is still insufficient. The pathogenic mechanism of this toxin can be fully explored by changing the local structure and overall function of the toxin by a previously unidentified single point mutation. These studies lay the groundwork for future studies that will explore the contribution of this large family of CDC proteins to microbial survival and human disease.
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Affiliation(s)
- Ning Liu
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Xue Wang
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Qiang Shan
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Shuxian Li
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Yanan Li
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Bingxin Chu
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Jiufeng Wang
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
| | - Yaohong Zhu
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, China Agricultural University, Beijing, China
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9
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Fu H, Zhou Y, Jing X, Shao X, Cai W. Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy. J Med Chem 2022; 65:12970-12978. [PMID: 36179112 DOI: 10.1021/acs.jmedchem.2c00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.
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Affiliation(s)
- Haohao Fu
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Yan Zhou
- School of Medicine, Nankai University, Tianjin300071, China.,Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xiang Jing
- Department of Ultrasound, Tianjin Third Central Hospital, Tianjin300170, China
| | - Xueguang Shao
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
| | - Wensheng Cai
- Research Center for Analytical Sciences, Frontiers Science Center for New Organic Matter, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin300071, China.,Haihe Laboratory of Sustainable Chemical Transformations, Tianjin300192, China
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10
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Arthur DE, Elegbe BO, Aroh AO, Soliman M. Computational drug design of novel COVID-19 inhibitor. BULLETIN OF THE NATIONAL RESEARCH CENTRE 2022; 46:210. [PMID: 35854796 PMCID: PMC9284480 DOI: 10.1186/s42269-022-00892-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND In 2003, the first case of severe acute respiratory syndrome coronavirus (SARS-CoV) was recorded. Coronaviruses (CoVs) have caused a major outbreak of human fatal pneumonia. Currently, there is no specific drug or treatment for diseases caused by SARS CoV 2. Computational approach that adopts dynamic models is widely accepted as indispensable tool in drug design but yet to be exploited in covid-19 in Zaria, Nigeria. In this study, steps were taken to advance on the successful achievements in the field of covid-19 drug, with the aid of in silico drug design technique, to create novel inhibitor drug candidates with better activity. In this study, one thousand human immunodeficiency virus (HIV1) antiviral chemical compounds from www.bindingBD.org were docked on the SARS CoV 2 main protease protein data bank identification number 6XBH (PDB ID: 6XBH) and the molecular docking score were ranked in order to identify the compounds with the highest inhibitory effects, and easy selection for future studies. RESULTS The docking studies showed some interesting results. Inhibitors with Index numbers 331, 741, and 819 had the highest binding affinity. Similarly, inhibitors with Index number 441, 847, and 46 had the lowest hydrogen bond energy. Inhibitor with index number 331 was reported with the lowest value (- 48.38kCal/mol). Five new compounds were designed from the selected six (6) compounds with the best binding score giving a total of thirty (30) novel compounds. The low binding energy of inhibitor with index no. 847b is unique, as most of the interaction energies are of H-bond type with amino acids (Thr26, Gly143, Ser144, Cys145, Glu166, Gln189, Hie164, Met49, Thr26, Thr25, Thr190, Asn142, Met165) resulting in an overall negative value (-16.31 kCal/mol) making it the best of all the newly designed inhibitors. CONCLUSIONS The novel inhibitor is 2-(2-(5-amino-2-((((3-aminobenzyl)oxy)carbonyl)amino)-5-oxopentanamido)-4-(2-(tert-butyl)-4-oxo-4-(pentan-3-ylamino) butanamido)-3-hydroxybutyl) benzoic acid. The improvement it has over the parent inhibitor is from the primary amine group attached to meta position of first benzene ring and the carboxyl group attached to the ortho position of the second benzene ring. The molecular dynamics studies also show that the novel inhibitor remains stable after the study. This result makes it a better drug candidate against SARS CoV 2 main protease when compared with the co-crystallized inhibitor or any of the 1000 docked inhibitors. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s42269-022-00892-z.
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Affiliation(s)
- David Ebuka Arthur
- Department of Pure and Applied Chemistry, University of Maiduguri, Maiduguri, Nigeria
| | | | | | - Mahmoud Soliman
- Department of Pharmaceutical Sciences, University of KwaZulu Natal, Durban, South Africa
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11
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Oshima H, Sugita Y. Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions. J Chem Inf Model 2022; 62:2846-2856. [PMID: 35639709 DOI: 10.1021/acs.jcim.1c01532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.
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Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.,Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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12
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Ezzemani W, Kettani A, Sappati S, Kondaka K, El Ossmani H, Tsukiyama-Kohara K, Altawalah H, Saile R, Kohara M, Benjelloun S, Ezzikouri S. Reverse vaccinology-based prediction of a multi-epitope SARS-CoV-2 vaccine and its tailoring to new coronavirus variants. J Biomol Struct Dyn 2022:1-22. [PMID: 35549819 DOI: 10.1080/07391102.2022.2075468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The genome feature of SARS-CoV-2 leads the virus to mutate and creates new variants of concern. Tackling viral mutations is also an important challenge for the development of a new vaccine. Accordingly, in the present study, we undertook to identify B- and T-cell epitopes with immunogenic potential for eliciting responses to SARS-CoV-2, using computational approaches and its tailoring to coronavirus variants. A total of 47 novel epitopes were identified as immunogenic triggering immune responses and no toxic after investigation with in silico tools. Furthermore, we found these peptide vaccine candidates showed a significant binding affinity for MHC I and MHC II alleles in molecular docking investigations. We consider them to be promising targets for developing peptide-based vaccines against SARS-CoV-2. Subsequently, we designed two efficient multi-epitopes vaccines against the SARS-CoV-2, the first one based on potent MHC class I and class II T-cell epitopes of S (FPNITNLCPF-NYNYLYRLFR-MFVFLVLLPLVSSQC), M (MWLSYFIASF-GLMWLSYFIASFRLF), E (LTALRLCAY-LLFLAFVVFLLVTLA), and N (SPRWYFYYL-AQFAPSASAFFGMSR). The second candidate is the result of the tailoring of the first designed vaccine according to three classes of SARS-CoV-2 variants. Molecular docking showed that the protein-protein binding interactions between the vaccines construct and TLR2-TLR4 immune receptors are stable complexes. These findings confirmed that the final multi-epitope vaccine could be easily adapted to new viral variants. Our study offers a shortlist of promising epitopes that can accelerate the development of an effective and safe vaccine against the virus and its adaptation to new variants.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Wahiba Ezzemani
- Virology Unit, Viral Hepatitis Laboratory, Institut Pasteur du Maroc, Casablanca, Morocco.,Laboratoire de Biologie et Santé (URAC34), Départment de Biologie, Faculté des Sciences Ben Msik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Anass Kettani
- Laboratoire de Biologie et Santé (URAC34), Départment de Biologie, Faculté des Sciences Ben Msik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Subrahmanyam Sappati
- Department of Pharmaceutical Technology and Biochemistry, Gdańsk University of Technology, Gdańsk, Poland.,BioTechMed Center, Gdańsk University of Technology, Gdańsk, Poland
| | - Kavya Kondaka
- Department of Pharmaceutical Technology and Biochemistry, Gdańsk University of Technology, Gdańsk, Poland
| | - Hicham El Ossmani
- Institut de Criminalistique de la Gendarmerie Royale, AMSSNuR, Rabat, Morocco
| | - Kyoko Tsukiyama-Kohara
- Transboundary Animal Diseases Centre, Joint Faculty of Veterinary Medicine, Kagoshima University, Kagoshima, Japan
| | - Haya Altawalah
- Department of Microbiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait.,Virology Unit, Yacoub Behbehani Center, Sabah Hospital, Ministry of Health, Kuwait City, Kuwait
| | - Rachid Saile
- Laboratoire de Biologie et Santé (URAC34), Départment de Biologie, Faculté des Sciences Ben Msik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Michinori Kohara
- Department of Microbiology and Cell Biology, The Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Soumaya Benjelloun
- Virology Unit, Viral Hepatitis Laboratory, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Sayeh Ezzikouri
- Virology Unit, Viral Hepatitis Laboratory, Institut Pasteur du Maroc, Casablanca, Morocco
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13
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Wang H, Liu H, Ning S, Zeng C, Zhao Y. DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model. Phys Chem Chem Phys 2022; 24:10124-10133. [PMID: 35416807 DOI: 10.1039/d1cp05558e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
| | - Haoquan Liu
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
| | - Shangbo Ning
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
| | - Chengwei Zeng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
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14
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Huggins DJ. Comparing the Performance of Different AMBER Protein Forcefields, Partial Charge Assignments, and Water Models for Absolute Binding Free Energy Calculations. J Chem Theory Comput 2022; 18:2616-2630. [PMID: 35266690 DOI: 10.1021/acs.jctc.1c01208] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Identifying chemical starting points is a vital first step in small molecule drug discovery and can take significant time and money. For this reason, computational approaches to virtual screening are of great interest as they can lower the cost and shorten timeframes. However, simple approaches such as molecular docking and pharmacophore screening are of limited accuracy and provide a low probability of success. Alchemical binding free energies represent a promising approach for virtual screening as they naturally incorporate the key effects of water molecules, protein flexibility, and binding entropy. However, the calculations are technically very challenging, with performance depending on the specific forcefield used. For this reason, it is important that the community has access to benchmark test sets to assess prediction accuracy. In this paper, we present an approach to alchemical binding free energies using OpenMM. We identify effective simulation parameters using an existing BRD4(1) test set and present two new benchmark sets (cMET and PDE2A) that can be used in the community for validation purposes. Our findings also highlight the effectiveness of some AMBER forcefields, in particular, AMBER ff15ipq.
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Affiliation(s)
- David J Huggins
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States.,Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States
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15
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Sun Q, Biswas A, Vijayan RSK, Craveur P, Forli S, Olson AJ, Castaner AE, Kirby KA, Sarafianos SG, Deng N, Levy R. Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid. J Comput Aided Mol Des 2022; 36:193-203. [PMID: 35262811 PMCID: PMC8904208 DOI: 10.1007/s10822-022-00446-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/24/2022] [Indexed: 02/07/2023]
Abstract
We have identified novel HIV-1 capsid inhibitors targeting the PF74 binding site. Acting as the building block of the HIV-1 capsid core, the HIV-1 capsid protein plays an important role in the viral life cycle and is an attractive target for antiviral development. A structure-based virtual screening workflow for hit identification was employed, which includes docking 1.6 million commercially-available drug-like compounds from the ZINC database to the capsid dimer, followed by applying two absolute binding free energy (ABFE) filters on the 500 top-ranked molecules from docking. The first employs the Binding Energy Distribution Analysis Method (BEDAM) in implicit solvent. The top-ranked compounds are then refined using the Double Decoupling method in explicit solvent. Both docking and BEDAM refinement were carried out on the IBM World Community Grid as part of the FightAIDS@Home project. Using this virtual screening workflow, we identified 24 molecules with calculated binding free energies between − 6 and − 12 kcal/mol. We performed thermal shift assays on these molecules to examine their potential effects on the stability of HIV-1 capsid hexamer and found that two compounds, ZINC520357473 and ZINC4119064 increased the melting point of the latter by 14.8 °C and 33 °C, respectively. These results support the conclusion that the two ZINC compounds are primary hits targeting the capsid dimer interface. Our simulations also suggest that the two hit molecules may bind at the capsid dimer interface by occupying a new sub-pocket that has not been exploited by existing CA inhibitors. The possible causes for why other top-scored compounds suggested by ABFE filters failed to show measurable activity are discussed.
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Affiliation(s)
- Qinfang Sun
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Avik Biswas
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - R S K Vijayan
- Institute for Applied Cancer Science, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Pierrick Craveur
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA
| | - Andres Emanuelli Castaner
- Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Karen A Kirby
- Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Stefan G Sarafianos
- Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, 30322, USA.,Children's Healthcare of Atlanta, Atlanta, GA, 30322, USA
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, NY, 10038, USA.
| | - Ronald Levy
- Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
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16
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Azimi S, Khuttan S, Wu JZ, Pal RK, Gallicchio E. Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method. J Chem Inf Model 2022; 62:309-323. [PMID: 34990555 DOI: 10.1021/acs.jcim.1c01129] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present an extension of the alchemical transfer method (ATM) for the estimation of relative binding free energies of molecular complexes applicable to conventional, as well as scaffold-hopping, alchemical transformations. Named ATM-RBFE, the method is implemented in the free and open-source OpenMM molecular simulation package and aims to provide a simpler and more generally applicable route to the calculation of relative binding free energies than what is currently available. ATM-RBFE is based on sound statistical mechanics theory and a novel coordinate perturbation scheme designed to swap the positions of a pair of ligands such that one is transferred from the bulk solvent to the receptor binding site while the other moves simultaneously in the opposite direction. The calculation is conducted directly in a single solvent box with a system prepared with conventional setup tools, without splitting of electrostatic and nonelectrostatic transformations, and without pairwise soft-core potentials. ATM-RBFE is validated here against the absolute binding free energies of the SAMPL8 GDCC host-guest benchmark set and against protein-ligand benchmark sets that include complexes of the estrogen receptor ERα and those of the methyltransferase EZH2. In each case the method yields self-consistent and converged relative binding free energy estimates in agreement with absolute binding free energies and reference literature values, as well as experimental measurements.
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Affiliation(s)
- Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Joe Z Wu
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Rajat K Pal
- Roivant Sciences, Inc., Boston, Massachusetts 02210, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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17
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Gallicchio E. Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria. Methods Mol Biol 2022; 2405:303-334. [PMID: 35298820 DOI: 10.1007/978-1-0716-1855-4_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies: (1) alchemical absolute binding free energy estimation with implicit solvation, (2) alchemical relative binding free energy estimation with explicit solvation, and (3) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method.
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Affiliation(s)
- Emilio Gallicchio
- Department of Chemistry, Ph.D. Program in Biochemistry and Ph.D. Program in Chemistry at The Graduate Center of the City University of New York, Brooklyn College of the City University of New York, New York, NY, USA.
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18
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Wu JZ, Azimi S, Khuttan S, Deng N, Gallicchio E. Alchemical Transfer Approach to Absolute Binding Free Energy Estimation. J Chem Theory Comput 2021; 17:3309-3319. [PMID: 33983730 DOI: 10.1021/acs.jctc.1c00266] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The alchemical transfer method (ATM) for the calculation of standard binding free energies of noncovalent molecular complexes is presented. The method is based on a coordinate displacement perturbation of the ligand between the receptor binding site and the explicit solvent bulk and a thermodynamic cycle connected by a symmetric intermediate in which the ligand interacts with the receptor and solvent environments with equal strength. While the approach is alchemical, the implementation of the ATM is as straightforward as that for physical pathway methods of binding. The method is applicable, in principle, with any force field, as it does not require splitting the alchemical transformations into electrostatic and nonelectrostatic steps, and it does not require soft-core pair potentials. We have implemented the ATM as a freely available and open-source plugin of the OpenMM molecular dynamics library. The method and its implementation are validated on the SAMPL6 SAMPLing host-guest benchmark set. The work paves the way to streamlined alchemical relative and absolute binding free energy implementations on many molecular simulation packages and with arbitrary energy functions including polarizable, quantum-mechanical, and artificial neural network potentials.
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Affiliation(s)
- Joe Z Wu
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York 10038, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210-2889, United States.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States.,Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, New York 10016, United States
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19
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Khuttan S, Azimi S, Wu JZ, Gallicchio E. Alchemical transformations for concerted hydration free energy estimation with explicit solvation. J Chem Phys 2021; 154:054103. [DOI: 10.1063/5.0036944] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Sheenam Khuttan
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Solmaz Azimi
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Joe Z. Wu
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
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20
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Rick SW, Schwing GJ, Summa CM. An Implementation of Replica Exchange with Dynamical Scaling for Efficient Large-Scale Simulations. J Chem Inf Model 2021; 61:810-818. [PMID: 33496583 DOI: 10.1021/acs.jcim.0c01236] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An implementation of the replica exchange with dynamical scaling (REDS) method in the commonly used molecular dynamics program GROMACS is presented. REDS is a replica exchange method that requires fewer replicas than conventional replica exchange while still providing data over a range of temperatures and can be used in either constant volume or constant pressure ensembles. Details for running REDS simulations are given, and an application to the human islet amyloid polypeptide (hIAPP) 11-25 fragment shows that the model efficiently samples conformational space.
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Affiliation(s)
- Steven W Rick
- Department of Chemistry, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Gregory J Schwing
- Department of Computer Science, University of New Orleans, New Orleans, Louisiana 70148, United States
| | - Christopher M Summa
- Department of Computer Science, University of New Orleans, New Orleans, Louisiana 70148, United States
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21
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Hao D, He X, Ji B, Zhang S, Wang J. How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations? J Chem Inf Model 2020; 60:6624-6633. [PMID: 33213150 DOI: 10.1021/acs.jcim.0c00934] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.
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Affiliation(s)
- Dongxiao Hao
- School of Physics, Xi'an Jiaotong University, Xi'an 710049, China.,Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xibing He
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Beihong Ji
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Shengli Zhang
- School of Physics, Xi'an Jiaotong University, Xi'an 710049, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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22
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Willow SY, Xie B, Lawrence J, Eisenberg RS, Minh DDL. On the polarization of ligands by proteins. Phys Chem Chem Phys 2020; 22:12044-12057. [PMID: 32421120 DOI: 10.1039/d0cp00376j] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Although ligand-binding sites in many proteins contain a high number density of charged side chains that can polarize small organic molecules and influence binding, the magnitude of this effect has not been studied in many systems. Here, we use a quantum mechanics/molecular mechanics (QM/MM) approach, in which the ligand is the QM region, to compute the ligand polarization energy of 286 protein-ligand complexes from the PDBBind Core Set (release 2016). Calculations were performed both with and without implicit solvent based on the domain decomposition Conductor-like Screening Model. We observe that the ligand polarization energy is linearly correlated with the magnitude of the electric field acting on the ligand, the magnitude of the induced dipole moment, and the classical polarization energy. The influence of protein and cation charges on the ligand polarization diminishes with the distance and is below 2 kcal mol-1 at 9 Å and 1 kcal mol-1 at 12 Å. Compared to these embedding field charges, implicit solvent has a relatively minor effect on ligand polarization. Considering both polarization and solvation appears essential to computing negative binding energies in some crystallographic complexes. Solvation, but not polarization, is essential for achieving moderate correlation with experimental binding free energies.
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Affiliation(s)
- Soohaeng Yoo Willow
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
| | - Bing Xie
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
| | - Jason Lawrence
- Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois 60616, USA
| | - Robert S Eisenberg
- Department of Applied Mathematics, Illinois Institute of Technology, Chicago, Illinois 60616, USA
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
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23
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Oshima H, Re S, Sugita Y. Prediction of Protein–Ligand Binding Pose and Affinity Using the gREST+FEP Method. J Chem Inf Model 2020; 60:5382-5394. [DOI: 10.1021/acs.jcim.0c00338] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Suyong Re
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Integrated Innovation Building 7F, 6-7-1 minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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24
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Chatterjee S, Maity A, Chowdhury S, Islam MA, Muttinini RK, Sen D. In silico analysis and identification of promising hits against 2019 novel coronavirus 3C-like main protease enzyme. J Biomol Struct Dyn 2020; 39:5290-5303. [PMID: 32608329 DOI: 10.1080/07391102.2020.1787228] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The recent outbreak of the 2019 novel coronavirus disease (COVID-19) has been proved as a global threat. No particular drug or vaccine has not yet been discovered which may act specifically against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and causes COVID-19. For this highly infectious virus, 3CL-like main protease (3CLpro) plays a key role in the virus life cycle and can be considered as a pivotal drug target. Structure-based virtual screening of DrugBank database resulted in 20 hits against 3CLpro. Atomistic 100 ns molecular dynamics of five top hits and binding energy calculation analyses were performed for main protease-hit complexes. Among the top five hits, Nafarelin and Icatibant affirmed the binding energy (g_MMPBSA) of -712.94 kJ/mol and -851.74 kJ/mol, respectively. Based on binding energy and stability of protein-ligand complex; the present work reports these two drug-like hits against SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shilpa Chatterjee
- Department of Biomedical Science, Chosun University, Gwangju, South Korea
| | - Arindam Maity
- School of Pharmaceutical Technology, Adamas University, Kolkata, India
| | - Suchana Chowdhury
- BCDA College of Pharmaceutical Technology, Hridaypur, Kolkata, India
| | - Md Ataul Islam
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,School of Health Sciences, University of Kwazulu-Natal, Durban, South Africa
| | | | - Debanjan Sen
- BCDA College of Pharmaceutical Technology, Hridaypur, Kolkata, India
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25
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D’Annessa I, Di Leva FS, La Teana A, Novellino E, Limongelli V, Di Marino D. Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We? Front Mol Biosci 2020; 7:66. [PMID: 32432124 PMCID: PMC7214840 DOI: 10.3389/fmolb.2020.00066] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 03/25/2020] [Indexed: 12/16/2022] Open
Abstract
Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.
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Affiliation(s)
- Ilda D’Annessa
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Milan, Italy
| | | | - Anna La Teana
- Department of Life and Environmental Sciences, New York-Marche Structural Biology Center (NY-MaSBiC), Polytechnic University of Marche, Ancona, Italy
| | - Ettore Novellino
- Department of Pharmacy, University of Naples Federico II, Naples, Italy
| | - Vittorio Limongelli
- Department of Pharmacy, University of Naples Federico II, Naples, Italy
- Faculty of Biomedical Sciences, Institute of Computational Science, Università della Svizzera Italiana (USI), Lugano, Switzerland
| | - Daniele Di Marino
- Department of Life and Environmental Sciences, New York-Marche Structural Biology Center (NY-MaSBiC), Polytechnic University of Marche, Ancona, Italy
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26
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Cruz J, Wickstrom L, Yang D, Gallicchio E, Deng N. Combining Alchemical Transformation with a Physical Pathway to Accelerate Absolute Binding Free Energy Calculations of Charged Ligands to Enclosed Binding Sites. J Chem Theory Comput 2020; 16:2803-2813. [PMID: 32101691 DOI: 10.1021/acs.jctc.9b01119] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We present a new approach to more accurately and efficiently compute the absolute binding free energy for receptor-ligand complexes. Currently, the double decoupling method (DDM) and the potential of mean force method (PMF) are widely used to compute the absolute binding free energy of biomolecular complexes. DDM relies on alchemically decoupling the ligand from its environments, which can be computationally challenging for large ligands and charged ligands because of the large magnitude of the decoupling free energies involved. In contrast, the PMF method uses a physical pathway to directly transfer the ligand from solution to the receptor binding pocket and thus avoids some of the aforementioned problems in DDM. However, the PMF method has its own drawbacks: because of its reliance on a ligand binding/unbinding pathway that is free of steric obstructions from the receptor atoms, the method has difficulty treating ligands with buried atoms. To overcome the limitation in the standard PMF approach and enable buried ligands to be treated, here we develop a new method called AlchemPMF in which steric obstructions along the physical pathway for binding are alchemically removed. We have tested the new approach on two important drug targets involving charged ligands. One is HIV-1 integrase bound to an allosteric inhibitor; the other is the human telomeric DNA G-quadruplex in complex with a natural product protoberberine buried in the binding pocket. For both systems, the new approach leads to more reliable estimates of absolute binding free energies with smaller error bars and closer agreements with experiments compared with those obtained from the existing methods, demonstrating the effectiveness of the new method in overcoming the hysteresis often encountered in PMF binding free energy calculations of such systems. The new approach could also be used to improve the sampling of water equilibration and resolvation of the binding pocket as the ligand is extracted.
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Affiliation(s)
- Jeffrey Cruz
- Department of Chemistry and Physical Sciences, Pace University, New York, New York 10038, United States
| | - Lauren Wickstrom
- Department of Science, Borough of Manhattan Community College, The City University of New York, New York, New York 10007, United States
| | - Danzhou Yang
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana 47907, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, New York 11210, United States.,Ph.D. Program in Biochemistry, Graduate Center, City University of New York, New York, New York 10016, United States.,Ph.D. Program in Chemistry, Graduate Center, City University of New York, New York, New York 10016, United States
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York 10038, United States
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27
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Sakae Y, Zhang BW, Levy RM, Deng N. Absolute Protein Binding Free Energy Simulations for Ligands with Multiple Poses, a Thermodynamic Path That Avoids Exhaustive Enumeration of the Poses. J Comput Chem 2020; 41:56-68. [PMID: 31621932 PMCID: PMC7140983 DOI: 10.1002/jcc.26078] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 08/07/2019] [Accepted: 08/31/2019] [Indexed: 12/28/2022]
Abstract
We propose a free energy calculation method for receptor-ligand binding, which have multiple binding poses that avoids exhaustive enumeration of the poses. For systems with multiple binding poses, the standard procedure is to enumerate orientations of the binding poses, restrain the ligand to each orientation, and then, calculate the binding free energies for each binding pose. In this study, we modify a part of the thermodynamic cycle in order to sample a broader conformational space of the ligand in the binding site. This modification leads to more accurate free energy calculation without performing separate free energy simulations for each binding pose. We applied our modification to simple model host-guest systems as a test, which have only two binding poses, by using a single decoupling method (SDM) in implicit solvent. The results showed that the binding free energies obtained from our method without knowing the two binding poses were in good agreement with the benchmark results obtained by explicit enumeration of the binding poses. Our method is applicable to other alchemical binding free energy calculation methods such as the double decoupling method (DDM) in explicit solvent. We performed a calculation for a protein-ligand system with explicit solvent using our modified thermodynamic path. The results of the free energy simulation along our modified path were in good agreement with the results of conventional DDM, which requires a separate binding free energy calculation for each of the binding poses of the example of phenol binding to T4 lysozyme in explicit solvent. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Yoshitake Sakae
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
| | - Bin W Zhang
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Temple University, Philadelphia, Pennsylvania, 19122
- Department of Chemistry, Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122
| | - Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, New York, 10038
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28
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Cui D, Zhang BW, Tan Z, Levy RM. Ligand Binding Thermodynamic Cycles: Hysteresis, the Locally Weighted Histogram Analysis Method, and the Overlapping States Matrix. J Chem Theory Comput 2019; 16:67-79. [PMID: 31743019 DOI: 10.1021/acs.jctc.9b00740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Free energy perturbation (FEP) simulations have been widely applied to obtain predictions of the relative binding free energy for a series of congeneric ligands binding to the same receptor, which is an essential component for the lead optimization process in computer-aided drug discovery. In the case of several congeneric ligands forming a perturbation map involving a closed thermodynamic cycle, the summation of the estimated free energy change along each edge in the cycle using Bennett acceptance ratio (BAR) usually will deviate from zero due to systematic and random errors, which is the hysteresis of cycle closure. In this work, the advanced reweighting techniques binless weighted histogram analysis method (UWHAM) and locally weighted histogram analysis method (LWHAM) are applied to provide statistical estimators of the free energy change along each edge in order to eliminate the hysteresis effect. As an example, we analyze a closed thermodynamic cycle involving four congeneric ligands which bind to HIV-1 integrase, a promising target which has emerged for antiviral therapy. We demonstrate that, compared with FEP and BAR, more accurate and hysteresis-free estimates of free energy differences can be achieved by using UWHAM to find a single estimate of the density of states based on all of the data in the cycle. Furthermore, by comparison of LWHAM results obtained from the inclusion of different numbers of neighboring states with UWHAM estimation involving all the states, we show how to determine the optimal neighborhood size in the LWHAM analysis to balance the trade-offs between computational cost and accuracy of the free energy prediction. Even with the smallest neighborhood, LWHAM can improve the BAR free energy estimates using the same input data as BAR. We introduce an overlapping states matrix that is constructed by using the global jump formula of LWHAM and plot its heat map. The heat map provides a quantitative measure of the overlap between pairs of alchemical/thermodynamic states. We explain how to identify and improve the FEP calculations along the edges that most likely cause large systematic errors by using the heat map of the overlapping states matrix and by comparing the BAR and UWHAM estimates of the free energy change.
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Affiliation(s)
- Di Cui
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Bin W Zhang
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Zhiqiang Tan
- Department of Statistics , Rutgers, The State University of New Jersey , Piscataway , New Jersey 08854 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Department of Chemistry, and Institute for Computational Molecular Science , Temple University , Philadelphia , Pennsylvania 19122 , United States
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29
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Pal RK, Gadhiya S, Ramsey S, Cordone P, Wickstrom L, Harding WW, Kurtzman T, Gallicchio E. Inclusion of enclosed hydration effects in the binding free energy estimation of dopamine D3 receptor complexes. PLoS One 2019; 14:e0222902. [PMID: 31568493 PMCID: PMC6768453 DOI: 10.1371/journal.pone.0222902] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 08/30/2019] [Indexed: 01/04/2023] Open
Abstract
Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.
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Affiliation(s)
- Rajat Kumar Pal
- Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210, United States of America
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
| | - Satishkumar Gadhiya
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America
| | - Steven Ramsey
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Lehman College, 250 Bedford Park Blvd. West, Bronx, NY 10468, United States of America
| | - Pierpaolo Cordone
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America
| | - Lauren Wickstrom
- Department of Science, Borough of Manhattan Community College, 199 Chambers Street, New York, NY 10007, United States of America
| | - Wayne W. Harding
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Hunter College, 695 Park Avenue, NY 10065, United States of America
| | - Tom Kurtzman
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- Department of Chemistry, Lehman College, 250 Bedford Park Blvd. West, Bronx, NY 10468, United States of America
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210, United States of America
- PhD Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- PhD Program in Chemistry, The Graduate Center of the City University of New York, New York, NY 10016, United States of America
- * E-mail:
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30
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Pal RK, Gallicchio E. Perturbation potentials to overcome order/disorder transitions in alchemical binding free energy calculations. J Chem Phys 2019; 151:124116. [DOI: 10.1063/1.5123154] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Rajat K. Pal
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, New York, New York 11210, USA
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31
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Niitsu A, Re S, Oshima H, Kamiya M, Sugita Y. De Novo Prediction of Binders and Nonbinders for T4 Lysozyme by gREST Simulations. J Chem Inf Model 2019; 59:3879-3888. [DOI: 10.1021/acs.jcim.9b00416] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ai Niitsu
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
| | - Suyong Re
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 6-7-1 Minatojima-minamimachi,
Chuo-ku, Kobe 650-0047, Japan
| | - Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 6-7-1 Minatojima-minamimachi,
Chuo-ku, Kobe 650-0047, Japan
| | - Motoshi Kamiya
- Computational Biophysics Research Team, RIKEN Center for Computational Science, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Hirosawa 2-1, Wako, Saitama 351-0198, Japan
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, 6-7-1 Minatojima-minamimachi,
Chuo-ku, Kobe 650-0047, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, 6-7-1 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
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32
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Cole DJ, Cabeza de Vaca I, Jorgensen WL. Computation of protein-ligand binding free energies using quantum mechanical bespoke force fields. MEDCHEMCOMM 2019; 10:1116-1120. [PMID: 31391883 PMCID: PMC6644397 DOI: 10.1039/c9md00017h] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 02/26/2019] [Indexed: 12/17/2022]
Abstract
A quantum mechanical bespoke molecular mechanics force field is derived for the L99A mutant of T4 lysozyme and used to compute absolute binding free energies of six benzene analogs to the protein. Promising agreement between theory and experiment highlights the potential for future use of system-specific force fields in computer-aided drug design.
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Affiliation(s)
- Daniel J Cole
- School of Natural and Environmental Sciences , Newcastle University , Newcastle upon Tyne NE1 7RU , UK .
| | - Israel Cabeza de Vaca
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , USA
| | - William L Jorgensen
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , USA
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33
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Abstract
![]()
A correct estimate
of ligand binding modes and a ratio of their
occupancies is crucial for calculations of binding free energies.
The newly developed method BLUES combines molecular dynamics with
nonequilibrium candidate Monte Carlo. Nonequilibrium candidate Monte
Carlo generates a plethora of possible binding modes and molecular
dynamics enables the system to relax. We used BLUES to investigate
binding modes of caffeine in the active site of its metabolizing enzyme
Cytochrome P450 1A2 with the aim of elucidating metabolite-formation
profiles at different concentrations. Because the activation energies
of all sites of metabolism do not show a clear preference for one
metabolite over the others, the orientations in the active site must
play a key role. In simulations with caffeine located in a spacious
pocket above the I-helix, it points N3 and N1 to the heme iron, whereas
in simulations where caffeine is in close proximity to the heme N7
and C8 are preferably oriented toward the heme iron. We propose a
mechanism where at low caffeine concentrations caffeine binds to the
upper part of the active site, leading to formation of the main metabolite
paraxanthine. On the other hand, at high concentrations two molecules
are located in the active site, forcing one molecule into close proximity
to the heme and yielding metabolites theophylline and trimethyluretic
acid. Our results offer an explanation of previously published experimental
results.
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Affiliation(s)
- Zuzana Jandova
- Institute of Molecular Modeling and Simulation , University of Natural Resources and Life Sciences, Vienna , 1180 Vienna , Austria
| | | | | | | | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation , University of Natural Resources and Life Sciences, Vienna , 1180 Vienna , Austria
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Xia J, Flynn W, Gallicchio E, Uplinger K, Armstrong JD, Forli S, Olson AJ, Levy RM. Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network. J Chem Inf Model 2019; 59:1382-1397. [PMID: 30758197 PMCID: PMC6496938 DOI: 10.1021/acs.jcim.8b00817] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing grid and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes. In total there are ∼10000 simulated complexes, ∼1 million replicas, and ∼2000 μs of aggregated MD simulations. Running AsyncRE MD simulations on the WCG requires accepting a trade-off between the number of replicas that can be run (breadth) and the number of full RE cycles that can be completed per replica (depth). As compared with synchronous Replica Exchange (SyncRE) running on tightly coupled clusters like XSEDE, on the WCG many more replicas can be launched simultaneously on heterogeneous distributed hardware, but each full RE cycle requires more overhead. We compared the WCG results with that from AutoDock and more advanced RE simulations including the use of flattening potentials to accelerate sampling of selected degrees of freedom of ligands and/or receptors related to slow dynamics due to high energy barriers. We propose a suitable strategy of RE simulations to refine high throughput docking results which can be matched to corresponding computing resources: from HPC clusters, to small or medium-size distributed campus grids, and finally to massive-scale computing networks including millions of CPUs like the resources available on the WCG.
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Affiliation(s)
- Junchao Xia
- Center for Biophysics and Computational Biology and Department of Physics , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - William Flynn
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Emilio Gallicchio
- Department of Chemistry , CUNY Brooklyn College , Brooklyn , New York 11210 , United States
| | - Keith Uplinger
- IBM WCG Team, 1177 South Belt Line Road , Coppell , Texas 75019 , United States
| | - Jonathan D Armstrong
- IBM WCG Team, 11400 Burnet Road , 0453B129, Austin , Texas 78758 , United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037-1000 , United States
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037-1000 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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Zhang BW, Arasteh S, Levy RM. The UWHAM and SWHAM Software Package. Sci Rep 2019; 9:2803. [PMID: 30808938 PMCID: PMC6391495 DOI: 10.1038/s41598-019-39420-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 01/21/2019] [Indexed: 11/09/2022] Open
Abstract
We introduce the UWHAM (binless weighted histogram analysis method) and SWHAM (stochastic UWHAM) software package that can be used to estimate the density of states and free energy differences based on the data generated by multi-state simulations. The programs used to solve the UWHAM equations are written in the C++ language and operated via the command line interface. In this paper, first we review the theoretical bases of UWHAM, its stochastic solver RE-SWHAM (replica exchange-like SWHAM)and ST-SWHAM (serial tempering-like SWHAM). Then we provide a tutorial with examples that explains how to apply the UWHAM program package to analyze the data generated by different types of multi-state simulations: umbrella sampling, replica exchange, free energy perturbation simulations, etc. The tutorial examples also show that the UWHAM equations can be solved stochastically by applying the RE-SWHAM and ST-SWHAM programs when the data ensemble is large. If the simulations at some states are far from equilibrium, the Stratified RE-SWHAM program can be applied to obtain the equilibrium distribution of the state of interest. All the source codes and the tutorial examples are available from our group's web page: https://ronlevygroup.cst.temple.edu/software/UWHAM_and_SWHAM_webpage/index.html .
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Affiliation(s)
- Bin W Zhang
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122, United States.
| | - Shima Arasteh
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122, United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania, 19122, United States
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36
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Kilburg D, Gallicchio E. Analytical Model of the Free Energy of Alchemical Molecular Binding. J Chem Theory Comput 2018; 14:6183-6196. [DOI: 10.1021/acs.jctc.8b00967] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Denise Kilburg
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, Brooklyn, New York 11210, United States
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37
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Menzer WM, Li C, Sun W, Xie B, Minh DDL. Simple Entropy Terms for End-Point Binding Free Energy Calculations. J Chem Theory Comput 2018; 14:6035-6049. [PMID: 30296084 DOI: 10.1021/acs.jctc.8b00418] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We introduce a number of computationally inexpensive modifications to the MM/PBSA and MM/GBSA estimators for binding free energies, which are based on average receptor-ligand interaction energies in simulations of a noncovalent complex, to improve the treatment of entropy: second- and higher-order terms in a cumulant expansion and a confining potential on ligand external degrees of freedom. We also consider a filter for snapshots where ligands have drifted from the initial binding pose. The variations were tested on six sets of systems for which binding modes and free energies have previously been experimentally determined. For some data sets, none of the tested estimators led to results significantly correlated with measured free energies. In data sets with nontrivial correlation, a ligand RMSD cutoff of 3 Å and a second-order truncation of the cumulant expansion was found to be comparable or better than the average interaction energy by several statistical metrics.
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38
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Xia J, Flynn W, Levy RM. Improving Prediction Accuracy of Binding Free Energies and Poses of HIV Integrase Complexes Using the Binding Energy Distribution Analysis Method with Flattening Potentials. J Chem Inf Model 2018; 58:1356-1371. [PMID: 29927237 PMCID: PMC6287956 DOI: 10.1021/acs.jcim.8b00194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
To accelerate conformation sampling of slow dynamics from receptor or ligand, we introduced flattening potentials on selected bonded and nonbonded intramolecular interactions to the binding energy distribution analysis method (BEDAM) for calculating absolute binding free energies of protein-ligand complexes using an implicit solvent model and implemented flattening BEDAM using the asynchronous replica exchange (AsyncRE) framework for performing large scale replica exchange molecular dynamics (REMD) simulations. The advantage of using the flattening feature to reduce high energy barriers was exhibited first by the p-xylene-T4 lysozyme complex, where the intramolecular interactions of a protein side chain on the binding site were flattened to accelerate the conformational transition of the side chain from the trans to the gauche state when the p-xylene ligand is present in the binding site. Much more extensive flattening BEDAM simulations were performed for 53 experimental binders and 248 nonbinders of HIV-1 integrase which formed the SAMPL4 challenge, with the total simulation time of 24.3 μs. We demonstrated that the flattening BEDAM simulations not only substantially increase the number of true positives (and reduce false negatives) but also improve the prediction accuracy of binding poses of experimental binders. Furthermore, the values of area under the curve (AUC) of receiver operating characteristic (ROC) and the enrichment factors at 20% cutoff calculated from the flattening BEDAM simulations were improved significantly in comparison with that of simulations without flattening as we previously reported for the whole SAMPL4 database. Detailed analysis found that the improved ability to discriminate the binding free energies between the binders and nonbinders is due to the fact that the flattening simulations reduce the reorganization free energy penalties of binders and decrease the overlap of binding free energy distributions of binders relative to that of nonbinders. This happens because the conformational ensemble distributions for both the ligand and protein in solution match those at the fully coupled (complex) state more closely when the systems are more fully sampled after the flattening potentials are applied to the intermediate states.
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Affiliation(s)
- Junchao Xia
- Center for Biophysics and Computational Biology and Department of Physics , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - William Flynn
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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Deng N, Cui D, Zhang BW, Xia J, Cruz J, Levy R. Comparing alchemical and physical pathway methods for computing the absolute binding free energy of charged ligands. Phys Chem Chem Phys 2018; 20:17081-17092. [PMID: 29896599 DOI: 10.1039/c8cp01524d] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurately predicting absolute binding free energies of protein-ligand complexes is important as a fundamental problem in both computational biophysics and pharmaceutical discovery. Calculating binding free energies for charged ligands is generally considered to be challenging because of the strong electrostatic interactions between the ligand and its environment in aqueous solution. In this work, we compare the performance of the potential of mean force (PMF) method and the double decoupling method (DDM) for computing absolute binding free energies for charged ligands. We first clarify an unresolved issue concerning the explicit use of the binding site volume to define the complexed state in DDM together with the use of harmonic restraints. We also provide an alternative derivation for the formula for absolute binding free energy using the PMF approach. We use these formulas to compute the binding free energy of charged ligands at an allosteric site of HIV-1 integrase, which has emerged in recent years as a promising target for developing antiviral therapy. As compared with the experimental results, the absolute binding free energies obtained by using the PMF approach show unsigned errors of 1.5-3.4 kcal mol-1, which are somewhat better than the results from DDM (unsigned errors of 1.6-4.3 kcal mol-1) using the same amount of CPU time. According to the DDM decomposition of the binding free energy, the ligand binding appears to be dominated by nonpolar interactions despite the presence of very large and favorable intermolecular ligand-receptor electrostatic interactions, which are almost completely cancelled out by the equally large free energy cost of desolvation of the charged moiety of the ligands in solution. We discuss the relative strengths of computing absolute binding free energies using the alchemical and physical pathway methods.
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Affiliation(s)
- Nanjie Deng
- Department of Chemistry and Physical Sciences, Pace University, New York, NY 10038, USA.
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40
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Toward Expanded Diversity of Host–Guest Interactions via Synthesis and Characterization of Cyclodextrin Derivatives. J SOLUTION CHEM 2018. [DOI: 10.1007/s10953-018-0769-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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41
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Remsing RC, Weeks JD. Alchemical free energy calculations and umbrella sampling with local molecular field theory. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2018. [DOI: 10.1142/s0219633618400035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Understanding the thermodynamic driving forces underlying any chemical process requires a description of the underlying free energy surface. However, computation of free energies is difficult, often requiring advanced sampling techniques. Moreover, these computations can be further complicated by the evaluation of any long-ranged interactions in the system of interest, such as Coulomb interactions in charged and polar media. Local molecular field theory is a promising approach to avoid many of the conceptual and computational difficulties associated with long-ranged interactions. We present frameworks for performing alchemical free energy calculations and non-Boltzmann sampling with local molecular field theory. We demonstrate that local molecular field theory can be used to perform these free energy calculations with accuracy comparable to traditional methodologies while eliminating the need for explicit treatment of long-ranged interactions in simulations.
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Affiliation(s)
- Richard C. Remsing
- Institute for Computational Molecular Science, Department of Chemistry, Temple University, Philadelphia, PA 19122, USA
| | - John D. Weeks
- Institute for Physical Science and Technology, Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
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42
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Gill SC, Lim NM, Grinaway PB, Rustenburg AS, Fass J, Ross GA, Chodera JD, Mobley DL. Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo. J Phys Chem B 2018; 122:5579-5598. [PMID: 29486559 PMCID: PMC5980761 DOI: 10.1021/acs.jpcb.7b11820] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.
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Affiliation(s)
- Samuel C. Gill
- Department of Chemistry, University of California, Irvine
| | - Nathan M. Lim
- Department of Pharmaceutical Sciences, University of California, Irvine
| | - Patrick B. Grinaway
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Ariën S. Rustenburg
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Medical College, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Josh Fass
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Computational Biology and Medicine, New York, NY 10065
| | - Gregory A. Ross
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | - David L. Mobley
- Department of Chemistry, University of California, Irvine
- Department of Pharmaceutical Sciences, University of California, Irvine
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43
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Cabeza de Vaca I, Qian Y, Vilseck JZ, Tirado-Rives J, Jorgensen WL. Enhanced Monte Carlo Methods for Modeling Proteins Including Computation of Absolute Free Energies of Binding. J Chem Theory Comput 2018; 14:3279-3288. [PMID: 29708338 DOI: 10.1021/acs.jctc.8b00031] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The generation of a complete ensemble of geometrical configurations is required to obtain reliable estimations of absolute binding free energies by alchemical free energy methods. Molecular dynamics (MD) is the most popular sampling method, but the representation of large biomolecular systems may be incomplete owing to energetic barriers that impede efficient sampling of the configurational space. Monte Carlo (MC) methods can possibly overcome this issue by adapting the attempted movement sizes to facilitate transitions between alternative local-energy minima. In this study, we present an MC statistical mechanics algorithm to explore the protein-ligand conformational space with emphasis on the motions of the protein backbone and side chains. The parameters for each MC move type were optimized to better reproduce conformational distributions of 18 dipeptides and the well-studied T4-lysozyme L99A protein. Next, the performance of the improved MC algorithms was evaluated by computing absolute free energies of binding for L99A lysozyme with benzene and seven analogs. Results for benzene with L99A lysozyme from MD and the optimized MC protocol were found to agree within 0.6 kcal/mol, while a mean unsigned error of 1.2 kcal/mol between MC results and experiment was obtained for the seven benzene analogs. Significant advantages in computation speed are also reported with MC over MD for similar extents of configurational sampling.
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Affiliation(s)
- Israel Cabeza de Vaca
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , United States
| | - Yue Qian
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , United States
| | - Jonah Z Vilseck
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , United States
| | - Julian Tirado-Rives
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , United States
| | - William L Jorgensen
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , United States
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44
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Tsukamoto S, Sakae Y, Itoh Y, Suzuki T, Okamoto Y. Computational analysis for selectivity of histone deacetylase inhibitor by replica-exchange umbrella sampling molecular dynamics simulations. J Chem Phys 2018; 148:125102. [DOI: 10.1063/1.5019209] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Shuichiro Tsukamoto
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
| | - Yoshitake Sakae
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
| | - Yukihiro Itoh
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
- Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Kyoto 606-0823, Japan
| | - Takayoshi Suzuki
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
- Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Kyoto 606-0823, Japan
| | - Yuko Okamoto
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan
- Structural Biology Research Center, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan
- Center for Computational Science, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
- Information Technology Center, Nagoya University, Nagoya, Aichi 464-8601, Japan
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45
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Nguyen TH, Minh DDL. Implicit ligand theory for relative binding free energies. J Chem Phys 2018; 148:104114. [PMID: 29544299 DOI: 10.1063/1.5017136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Implicit ligand theory enables noncovalent binding free energies to be calculated based on an exponential average of the binding potential of mean force (BPMF)-the binding free energy between a flexible ligand and rigid receptor-over a precomputed ensemble of receptor configurations. In the original formalism, receptor configurations were drawn from or reweighted to the apo ensemble. Here we show that BPMFs averaged over a holo ensemble yield binding free energies relative to the reference ligand that specifies the ensemble. When using receptor snapshots from an alchemical simulation with a single ligand, the new statistical estimator outperforms the original.
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Affiliation(s)
- Trung Hai Nguyen
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois 60616, USA
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46
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Kilburg D, Gallicchio E. Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes. Front Mol Biosci 2018; 5:22. [PMID: 29568737 PMCID: PMC5852065 DOI: 10.3389/fmolb.2018.00022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 02/21/2018] [Indexed: 01/24/2023] Open
Abstract
The computational modeling of peptide inhibitors to target protein-protein binding interfaces is growing in interest as these are often too large, too shallow, and too feature-less for conventional small molecule compounds. Here, we present a rare successful application of an alchemical binding free energy method for the calculation of converged absolute binding free energies of a series of protein-peptide complexes. Specifically, we report the binding free energies of a series of cyclic peptides derived from the LEDGF/p75 protein to the integrase receptor of the HIV1 virus. The simulations recapitulate the effect of mutations relative to the wild-type binding motif of LEDGF/p75, providing structural, energetic and dynamical interpretations of the observed trends. The equilibration and convergence of the calculations are carefully analyzed. Convergence is aided by the adoption of a single-decoupling alchemical approach with implicit solvation, which circumvents the convergence difficulties of conventional double-decoupling protocols. We hereby present the single-decoupling methodology and critically evaluate its advantages and limitations. We also discuss some of the challenges and potential pitfalls of binding free energy calculations for complex molecular systems which have generally limited their applicability to the quantitative study of protein-peptide binding equilibria.
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Affiliation(s)
- Denise Kilburg
- Department of Chemistry, Brooklyn College, Brooklyn, NY, United States.,Ph.D. Program in Chemistry, The Graduate Center, City University of New York, New York, NY, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, Brooklyn, NY, United States.,Ph.D. Program in Chemistry, The Graduate Center, City University of New York, New York, NY, United States.,Ph.D. Program in Biochemistry, The Graduate Center, City University of New York, New York, NY, United States
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47
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Nguyen TH, Zhou HX, Minh DDL. Using the fast fourier transform in binding free energy calculations. J Comput Chem 2017; 39:621-636. [PMID: 29270990 DOI: 10.1002/jcc.25139] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 09/23/2017] [Accepted: 11/27/2017] [Indexed: 12/21/2022]
Abstract
According to implicit ligand theory, the standard binding free energy is an exponential average of the binding potential of mean force (BPMF), an exponential average of the interaction energy between the unbound ligand ensemble and a rigid receptor. Here, we use the fast Fourier transform (FFT) to efficiently evaluate BPMFs by calculating interaction energies when rigid ligand configurations from the unbound ensemble are discretely translated across rigid receptor conformations. Results for standard binding free energies between T4 lysozyme and 141 small organic molecules are in good agreement with previous alchemical calculations based on (1) a flexible complex ( R≈0.9 for 24 systems) and (2) flexible ligand with multiple rigid receptor configurations ( R≈0.8 for 141 systems). While the FFT is routinely used for molecular docking, to our knowledge this is the first time that the algorithm has been used for rigorous binding free energy calculations. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Trung Hai Nguyen
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
| | - Huan-Xiang Zhou
- Departments of Chemistry and Physics, University of Illinois at Chicago, Chicago, Illinois, 60607
| | - David D L Minh
- Department of Chemistry, Illinois Institute of Technology, Chicago, Illinois, 60616
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48
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Giovannelli E, Procacci P, Cardini G, Pagliai M, Volkov V, Chelli R. Binding Free Energies of Host–Guest Systems by Nonequilibrium Alchemical Simulations with Constrained Dynamics: Theoretical Framework. J Chem Theory Comput 2017; 13:5874-5886. [DOI: 10.1021/acs.jctc.7b00594] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Edoardo Giovannelli
- Dipartimento
di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
| | - Piero Procacci
- Dipartimento
di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
| | - Gianni Cardini
- Dipartimento
di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
| | - Marco Pagliai
- Dipartimento
di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
| | - Victor Volkov
- Interdisciplinary
Biomedical Research Center, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, U.K
| | - Riccardo Chelli
- Dipartimento
di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
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49
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Williams-Noonan BJ, Yuriev E, Chalmers DK. Free Energy Methods in Drug Design: Prospects of “Alchemical Perturbation” in Medicinal Chemistry. J Med Chem 2017; 61:638-649. [DOI: 10.1021/acs.jmedchem.7b00681] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Billy J. Williams-Noonan
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
| | - Elizabeth Yuriev
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
| | - David K. Chalmers
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
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50
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Lapelosa M. Free Energy of Binding and Mechanism of Interaction for the MEEVD-TPR2A Peptide-Protein Complex. J Chem Theory Comput 2017; 13:4514-4523. [PMID: 28723223 DOI: 10.1021/acs.jctc.7b00105] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The association between the MEEVD C-terminal peptide from the heat shock protein 90 (Hsp90) and tetratricopeptide repeat A (TPR2A) domain of the heat shock organizing protein (Hop) is a useful prototype to study the fundamental molecular details about the Hop-Hsp90 interaction. We study here the mechanism of binding/unbinding and compute the standard binding free energy and potential of mean force for the association of the MEEVD peptide to the TPR2A domain using the Adaptive Biasing Force (ABF) methodology. We observe conformational changes of the peptide and the protein receptor induced by binding. We measure the binding free energy of -8.4 kcal/mol, which is consistent with experimental estimates. The simulations achieve multiple unbinding and rebinding events along a consistent pathway connecting the binding site to solvent. The MEEVD peptide slowly dissociates disrupting the hydrogen bonds first, then tilting on the side while preserving the interaction with the side chain of residue Asp 5 of the peptide. After this initial displacement, the peptide completely dissociates and moves into the solvent. Rebinding of the MEEVD peptide from the solvent to the receptor binding site occurs slowly through the portal of entry. Unbinding and rebinding go through intermediate states characterized by the peptide interacting with a lateral helix, helix A1, of the receptor with mainly Asp 5, Val 4, and Glu 3 of the peptide. This newly discovered intermediate structure is characterized by numerous contacts with the receptor which lead to complete formation of the bound complex. The structure of the bound complex obtained after rebinding is structurally very similar to the crystal structure of the complex (0.48 Å root-mean square deviation). The residues Asp 5, Val 4, and Glu 3 adopt conformations and intermolecular contacts with excellent structural similarity with the native ones. Finally, the dissociation and reassociation of MEEVD induce hydration/dehydration transitions, which provide insights on the role of desolvation and solvation processes in protein-peptide binding.
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Affiliation(s)
- Mauro Lapelosa
- Department of Drug Discovery and Development, Italian Institute of Technology , Via Morego 30, Genova 16163, Italy
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