1
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Pirnia A, Maqdisi R, Mittal S, Sener M, Singharoy A. Perspective on Integrative Simulations of Bioenergetic Domains. J Phys Chem B 2024; 128:3302-3319. [PMID: 38562105 DOI: 10.1021/acs.jpcb.3c07335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.
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Affiliation(s)
- Adam Pirnia
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
| | - Ranel Maqdisi
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
| | - Sumit Mittal
- VIT Bhopal University, Sehore 466114, Madhya Pradesh, India
| | - Melih Sener
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287-1004, United States
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2
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Carracedo-Reboredo P, Aranzamendi E, He S, Arrasate S, Munteanu CR, Fernandez-Lozano C, Sotomayor N, Lete E, González-Díaz H. MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products. J Cheminform 2024; 16:9. [PMID: 38254200 PMCID: PMC10804835 DOI: 10.1186/s13321-024-00802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Eider Aranzamendi
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Shan He
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain
| | - Cristian R Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
| | - Nuria Sotomayor
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Esther Lete
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain.
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3
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Bowers SR, Lockhart C, Klimov DK. Replica Exchange with Hybrid Tempering Efficiently Samples PGLa Peptide Binding to Anionic Bilayer. J Chem Theory Comput 2023; 19:6532-6550. [PMID: 37676235 DOI: 10.1021/acs.jctc.3c00787] [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: 09/08/2023]
Abstract
We evaluated the utility of a variant of the replica exchange method, a replica exchange with hybrid tempering (REHT), for all-atom explicit water biomolecular simulations and compared it with a more traditional replica exchange with the solute tempering (REST) algorithm. As a test system, we selected a 21-mer antimicrobial peptide PGLa binding to an anionic DMPC/DMPG lipid bilayer. Application of REHT revealed the following binding mechanism. Due to the strong hydrophobic moment, the bound PGLa adopts an extensive helical structure. The binding free energy landscape identifies two major bound states, a metastable surface bound state and a dominant inserted state. In both states, positively charged PGLa amino acids maintain electrostatic interactions with anionic phosphate groups by rotating the PGLa helix around its axis. PGLa binding causes an influx of anionic DMPG and an efflux of zwitterionic DMPC lipids from the peptide proximity. PGLa thins the bilayer and disorders the adjacent fatty acid tails. Deep invasion of water wires into the bilayer hydrophobic core is detected in the inserted peptide state. The analysis of charge density distributions indicated that peptide positive charges are nearly compensated for by lipid negative charges and water dipole ordering, whereas ions play no role in peptide binding. Thus, electrostatic interactions are the key energetic factor in binding cationic PGLa to an anionic DMPC/DMPG bilayer. Comparison of REHT and REST shows that due to exclusion of lipids from tempered partition, REST lags behind REHT in peptide equilibration, particularly, with respect to peptide insertion and helix acquisition. As a result, REST struggles to provide accurate details of PGLa binding, although it still qualitatively maps the bimodal binding mechanism. Importantly, REHT not only equilibrates PGLa in the bilayer faster than REST, but also with less computational effort. We conclude that REHT is a preferable choice for studying interfacial biomolecular systems.
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Affiliation(s)
- Steven R Bowers
- School of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Christopher Lockhart
- School of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
| | - Dmitri K Klimov
- School of Systems Biology, George Mason University, Manassas, Virginia 20110, United States
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4
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de Oliveira C, Leswing K, Feng S, Kanters R, Abel R, Bhat S. FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning. J Chem Inf Model 2023; 63:5592-5603. [PMID: 37594480 DOI: 10.1021/acs.jcim.3c00681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.
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Affiliation(s)
- César de Oliveira
- Schrodinger, Inc., 9868 Scranton Road, Suite 3200, San Diego, California 92121, United States
| | - Karl Leswing
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Shulu Feng
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - René Kanters
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Robert Abel
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Sathesh Bhat
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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5
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex. J Chem Theory Comput 2023; 19:4863-4882. [PMID: 37450482 PMCID: PMC11219094 DOI: 10.1021/acs.jctc.3c00333] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.
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Affiliation(s)
- Ivy Zhang
- 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, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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6
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Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD. Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530278. [PMID: 36945557 PMCID: PMC10028896 DOI: 10.1101/2023.03.07.530278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .
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Affiliation(s)
- Ivy Zhang
- 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, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Dominic A. Rufa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Tri-Institutional PhD Program in Chemical Biology, Weill Cornell Medical College, Cornell University, New York, NY 10065
| | - Iván Pulido
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | | | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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7
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Roy R, Poddar S, Kar P. Comparison of the conformational dynamics of an N-glycan in implicit and explicit solvents. Carbohydr Res 2022; 522:108700. [DOI: 10.1016/j.carres.2022.108700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 11/28/2022]
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8
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Nagai T, Tsurumaki S, Urano R, Fujimoto K, Shinoda W, Okazaki S. Position-Dependent Diffusion Constant of Molecules in Heterogeneous Systems as Evaluated by the Local Mean Squared Displacement. J Chem Theory Comput 2020; 16:7239-7254. [DOI: 10.1021/acs.jctc.0c00448] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Tetsuro Nagai
- Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Shuhei Tsurumaki
- Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Ryo Urano
- Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Kazushi Fujimoto
- Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Wataru Shinoda
- Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Susumu Okazaki
- Department of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Department of Materials Chemistry, Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
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9
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Sasmal S, Gill SC, Lim NM, Mobley DL. Sampling Conformational Changes of Bound Ligands Using Nonequilibrium Candidate Monte Carlo and Molecular Dynamics. J Chem Theory Comput 2020; 16:1854-1865. [PMID: 32058713 PMCID: PMC7325746 DOI: 10.1021/acs.jctc.9b01066] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Flexible ligands often have multiple binding modes or bound conformations that differ by rotation of a portion of the molecule around internal rotatable bonds. Knowledge of these binding modes is important for understanding the interactions stabilizing the ligand in the binding pocket, and other studies indicate it is important for calculating accurate binding affinities. In this work, we use a hybrid molecular dynamics (MD)/nonequilibrium candidate Monte Carlo (NCMC) method to sample the different binding modes of several flexible ligands and also to estimate the population distribution of the modes. The NCMC move proposal is divided into three parts. The flexible part of the ligand is alchemically turned off by decreasing the electrostatics and steric interactions gradually, followed by rotating the rotatable bond by a random angle and then slowly turning the ligand back on to its fully interacting state. The alchemical steps prior to and after the move proposal help the surrounding protein and water atoms in the binding pocket relax around the proposed ligand conformation and increase move acceptance rates. The protein-ligand system is propagated using classical MD in between the NCMC proposals. Using this MD/NCMC method, we were able to correctly reproduce the different binding modes of inhibitors binding to two kinase targets-c-Jun N-terminal kinase-1 and cyclin-dependent kinase 2-at a much lower computational cost compared to conventional MD and umbrella sampling. This method is available as a part of the BLUES software package.
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Affiliation(s)
- Sukanya Sasmal
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
| | - Samuel C Gill
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Nathan M Lim
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
| | - David L Mobley
- Department of Chemistry, University of California, Irvine, California 92697, United States
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States
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10
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Limongelli V. Ligand binding free energy and kinetics calculation in 2020. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1455] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vittorio Limongelli
- Faculty of Biomedical Sciences, Institute of Computational Science – Center for Computational Medicine in Cardiology Università della Svizzera italiana (USI) Lugano Switzerland
- Department of Pharmacy University of Naples “Federico II” Naples Italy
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11
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Hu X, Maffucci I, Contini A. Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations. Curr Med Chem 2020; 26:7598-7622. [DOI: 10.2174/0929867325666180514110824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
Background:
The inclusion of direct effects mediated by water during the ligandreceptor
recognition is a hot-topic of modern computational chemistry applied to drug discovery
and development. Docking or virtual screening with explicit hydration is still debatable,
despite the successful cases that have been presented in the last years. Indeed, how to select
the water molecules that will be included in the docking process or how the included waters
should be treated remain open questions.
Objective:
In this review, we will discuss some of the most recent methods that can be used in
computational drug discovery and drug development when the effect of a single water, or of a
small network of interacting waters, needs to be explicitly considered.
Results:
Here, we analyse the software to aid the selection, or to predict the position, of water
molecules that are going to be explicitly considered in later docking studies. We also present
software and protocols able to efficiently treat flexible water molecules during docking, including
examples of applications. Finally, we discuss methods based on molecular dynamics
simulations that can be used to integrate docking studies or to reliably and efficiently compute
binding energies of ligands in presence of interfacial or bridging water molecules.
Conclusions:
Software applications aiding the design of new drugs that exploit water molecules,
either as displaceable residues or as bridges to the receptor, are constantly being developed.
Although further validation is needed, workflows that explicitly consider water will
probably become a standard for computational drug discovery soon.
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Affiliation(s)
- Xiao Hu
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| | - Irene Maffucci
- Pasteur, Département de Chimie, École Normale Supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 75005 Paris, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
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12
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Cole DJ, Mones L, Csányi G. A machine learning based intramolecular potential for a flexible organic molecule. Faraday Discuss 2020; 224:247-264. [DOI: 10.1039/d0fd00028k] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Here, we employ the kernel regression machine learning technique to construct an analytical potential that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine.
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Affiliation(s)
- Daniel J. Cole
- School of Natural and Environmental Sciences
- Newcastle University
- Newcastle upon Tyne NE1 7RU
- UK
| | - Letif Mones
- Engineering Laboratory
- University of Cambridge
- Cambridge CB2 1PZ
- UK
| | - Gábor Csányi
- Engineering Laboratory
- University of Cambridge
- Cambridge CB2 1PZ
- UK
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13
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Chen J, Wang J, Pang L, Wang W, Zhao J, Zhu W. Deciphering molecular mechanism behind conformational change of the São Paolo metallo-β-lactamase 1 by using enhanced sampling. J Biomol Struct Dyn 2019; 39:140-151. [DOI: 10.1080/07391102.2019.1707121] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Jianzhong Chen
- School of Science, Shandong Jiaotong University, Jinan, China
| | - Jinan Wang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Laixue Pang
- School of Science, Shandong Jiaotong University, Jinan, China
| | - Wei Wang
- School of Science, Shandong Jiaotong University, Jinan, China
| | - Juan Zhao
- School of Science, Shandong Jiaotong University, Jinan, China
| | - Weiliang Zhu
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
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14
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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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15
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Manz TA, Chen T, Cole DJ, Limas NG, Fiszbein B. New scaling relations to compute atom-in-material polarizabilities and dispersion coefficients: part 1. Theory and accuracy. RSC Adv 2019; 9:19297-19324. [PMID: 35519408 PMCID: PMC9064874 DOI: 10.1039/c9ra03003d] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 06/03/2019] [Indexed: 11/21/2022] Open
Abstract
Polarizabilities and London dispersion forces are important to many chemical processes. Force fields for classical atomistic simulations can be constructed using atom-in-material polarizabilities and C n (n = 6, 8, 9, 10…) dispersion coefficients. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and m-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (e.g., polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). We also present results for the HIV reverse transcriptase enzyme complexed with an inhibitor molecule. MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C6 coefficients. Overall, MCLF has improved accuracy compared to the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C8, C9, and C10 dispersion coefficients and quantum Drude oscillator parameters. This method should find widespread applications to parameterize classical force fields and density functional theory (DFT) + dispersion methods.
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Affiliation(s)
- Thomas A Manz
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| | - Taoyi Chen
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University Newcastle upon Tyne NE1 7RU UK
| | - Nidia Gabaldon Limas
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
| | - Benjamin Fiszbein
- Department of Chemical & Materials Engineering, New Mexico State University Las Cruces New Mexico 88003-8001 USA
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16
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Gilabert JF, Lecina D, Estrada J, Guallar V. Monte Carlo Techniques for Drug Design: The Success Case of PELE. BIOMOLECULAR SIMULATIONS IN STRUCTURE-BASED DRUG DISCOVERY 2018. [DOI: 10.1002/9783527806836.ch5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Joan F. Gilabert
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Daniel Lecina
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Jorge Estrada
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Victor Guallar
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
- ICREA; Passeig Lluís Companys 23 08010 Barcelona Spain
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17
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Eslami H, Khanjari N, Müller-Plathe F. Self-Assembly Mechanisms of Triblock Janus Particles. J Chem Theory Comput 2018; 15:1345-1354. [DOI: 10.1021/acs.jctc.8b00713] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Hossein Eslami
- Department of Chemistry, College of Sciences, Persian Gulf University, Boushehr 75168, Iran
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie and Profile Area Thermo-Fluids & Interfaces, Technische Universität Darmstadt, Alarich-Weiss-Strasse 8, 64287 Darmstadt, Germany
| | - Neda Khanjari
- Department of Chemistry, College of Sciences, Persian Gulf University, Boushehr 75168, Iran
| | - Florian Müller-Plathe
- Eduard-Zintl-Institut für Anorganische und Physikalische Chemie and Profile Area Thermo-Fluids & Interfaces, Technische Universität Darmstadt, Alarich-Weiss-Strasse 8, 64287 Darmstadt, Germany
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18
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Srivastava A, Nagai T, Srivastava A, Miyashita O, Tama F. Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics. Int J Mol Sci 2018; 19:E3401. [PMID: 30380757 PMCID: PMC6274748 DOI: 10.3390/ijms19113401] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 10/20/2018] [Accepted: 10/27/2018] [Indexed: 12/13/2022] Open
Abstract
Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.
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Affiliation(s)
- Ashutosh Srivastava
- Institute of Transformative Bio-Molecules (WPI), Nagoya University, Nagoya, Aichi 464-8601, Japan.
| | - Tetsuro Nagai
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.
| | - Arpita Srivastava
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.
| | - Osamu Miyashita
- RIKEN-Center for Computational Science, Kobe, Hyogo 650-0047, Japan.
| | - Florence Tama
- Institute of Transformative Bio-Molecules (WPI), Nagoya University, Nagoya, Aichi 464-8601, Japan.
- Department of Physics, Graduate School of Science, Nagoya University, Nagoya, Aichi 464-8602, Japan.
- RIKEN-Center for Computational Science, Kobe, Hyogo 650-0047, Japan.
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19
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Yamamoto T, Arefi H, Shanker S, Sato H, Hiraoka S. Self-Assembly of Nanocubic Molecular Capsules via Solvent-Guided Formation of Rectangular Blocks. J Phys Chem Lett 2018; 9:6082-6088. [PMID: 30274518 DOI: 10.1021/acs.jpclett.8b02624] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We investigate the mechanism underlying the self-assembly of gear-shaped amphiphilic molecules into a highly ordered nanocubic capsule ("nanocube") in aqueous methanol. Simulation results show that the solvent molecules play a significant role in the assembly process by directing the primitive intermediates to orthogonal/rectangular shapes, thus creating appropriate building blocks for cubic assembly while avoiding off-pathway stacked aggregates. Free-energy analyses reveal that the interplay of the direct intermonomer interaction and the solvent-mediated repulsion between large aromatic cores (via preferential solvation of methanol on hydrophobic surfaces) leads to the strong trend for perpendicular binding of monomers and hence the solvent-guided formation of rectangular blocks. Furthermore, we report the self-assembly simulation of the nanocube using replica exchange with solute tempering and demonstrate that the simulation can predict a highly ordered nanocapsule structure, assembly intermediates, and encapsulated molecules, which helps promote computer-aided design of functional molecular self-assemblies in explicit solvent.
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Affiliation(s)
- Takeshi Yamamoto
- Department of Chemistry , Graduate School of Science, Kyoto University , Kyoto 606-8502 , Japan
| | - Hadi Arefi
- Department of Chemistry , Graduate School of Science, Kyoto University , Kyoto 606-8502 , Japan
| | - Sudhanshu Shanker
- Department of Chemistry , Graduate School of Science, Kyoto University , Kyoto 606-8502 , Japan
| | - Hirofumi Sato
- Department of Molecular Engineering , Graduate School of Engineering, Kyoto University , Kyoto 615-8510 , Japan
| | - Shuichi Hiraoka
- Department of Basic Science , Graduate School of Arts and Science, The University of Tokyo , Tokyo 153-8902 , Japan
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20
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Wang L, Berne BJ. Efficient sampling of puckering states of monosaccharides through replica exchange with solute tempering and bond softening. J Chem Phys 2018; 149:072306. [PMID: 30134707 DOI: 10.1063/1.5024389] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A molecular-level understanding of the structure, dynamics, and reactivity of carbohydrates is fundamental to the understanding of a range of key biological processes. The six-membered pyranose ring, a central component of biological monosaccharides and carbohydrates, has many different puckering conformations, and the conformational free energy landscape of these biologically important monosaccharides remains elusive. The puckering conformations of monosaccharides are separated by high energy barriers, which pose a great challenge for the complete sampling of these important conformations and accurate modeling of these systems. While metadynamics or umbrella sampling methods have been used to study the conformational space of monosaccharides, these methods might be difficult to generalize to other complex ring systems with more degrees of freedom. In this paper, we introduce a new enhanced sampling method for the rapid sampling over high energy barriers that combines our previously developed enhanced sampling method REST (replica exchange with solute tempering) with a bond softening (BOS) scheme that makes a chemical bond in the ring weaker as one ascends the replica ladder. We call this new method replica exchange with solute tempering and bond softening (REST/BOS). We demonstrate the superior sampling efficiency of REST/BOS over other commonly used enhanced sampling methods, including temperature replica exchange method and REST. The conformational free energy landscape of four biologically important monosaccharides, namely, α-glucose, β-glucose, β-mannose, and β-xylose, is studied using REST/BOS, and results are compared with previous experimental and theoretical studies.
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Affiliation(s)
- Lingle Wang
- Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, USA
| | - B J Berne
- Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, USA
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21
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Lapelosa M, Burrone O, Rocchia W. Specific Residue Interactions Regulate the Binding of Dengue Antigens to Broadly Neutralizing EDE Antibodies. ChemistryOpen 2018; 7:604-610. [PMID: 30151331 PMCID: PMC6099161 DOI: 10.1002/open.201800121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Indexed: 11/06/2022] Open
Abstract
Antibodies binding to antigens present on the dengue virus (DENV) represent the main defense mechanism of the host organism against the pathogen. Among the antibodies elicited by DENV and that bind to DII of protein E, EDE1-C8 can bind all DENV serotypes. Our analysis reveals the key residues in this interaction as well as structurally conserved hydrogen bonds located at the binding interface. They stabilize the dengue antigen-antibody complex among the EDE1 group of antibodies (Abs). Combining structural alignments with molecular dynamics simulations in the EDE1 Abs, we identified the critical elements that provide a major energetic contribution to the association of antigens from protein E with Abs. We discuss possible molecular insights into the binding mechanism by using a surrogate molecular entity resembling the protein E that forms native salt bridges and hydrogen bonds, including inferences on the light of high-resolution crystal structures of dengue Fab complexes. Finally, the molecular determinants, the free energy profile, and the binding mechanism provide inspiration for potential strategies in protein engineering to design novel immunogens of protein E against DENV.
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Affiliation(s)
- Mauro Lapelosa
- CONCEPT LabDepartment of Drug Discovery and DevelopmentItalian Institute of TechnologyGenovaviaMorego 6016163Italy
| | - Oscar Burrone
- Department of Molecular ImmunologyICGEB-International Center for Genetic Engineering and BiotechnologyTriesteVia Padriciano 9934149Italy
| | - Walter Rocchia
- CONCEPT LabDepartment of Drug Discovery and DevelopmentItalian Institute of TechnologyGenovaviaMorego 6016163Italy
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22
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Różycki B, Cazade PA, O'Mahony S, Thompson D, Cieplak M. The length but not the sequence of peptide linker modules exerts the primary influence on the conformations of protein domains in cellulosome multi-enzyme complexes. Phys Chem Chem Phys 2018; 19:21414-21425. [PMID: 28758665 DOI: 10.1039/c7cp04114d] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Cellulosomes are large multi-protein catalysts produced by various anaerobic microorganisms to efficiently degrade plant cell-wall polysaccharides down into simple sugars. X-ray and physicochemical structural characterisations show that cellulosomes are composed of numerous protein domains that are connected by unstructured polypeptide segments, yet the properties and possible roles of these 'linker' peptides are largely unknown. We have performed coarse-grained and all-atom molecular dynamics computer simulations of a number of cellulosomal linkers of different lengths and compositions. Our data demonstrates that the effective stiffness of the linker peptides, as quantified by the equilibrium fluctuations in the end-to-end distances, depends primarily on the length of the linker and less so on the specific amino acid sequence. The presence of excluded volume - provided by the domains that are connected - dampens the motion of the linker residues and reduces the effective stiffness of the linkers. Simultaneously, the presence of the linkers alters the conformations of the protein domains that are connected. We demonstrate that short, stiff linkers induce significant rearrangements in the folded domains of the mini-cellulosome composed of endoglucanase Cel8A in complex with scaffoldin ScafT (Cel8A-ScafT) of Clostridium thermocellum as well as in a two-cohesin system derived from the scaffoldin ScaB of Acetivibrio cellulolyticus. We give experimentally testable predictions on structural changes in protein domains that depend on the length of linkers.
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Affiliation(s)
- Bartosz Różycki
- Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, 02-668 Warsaw, Poland.
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23
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Xie Y, Li Z, Zhou J. Hamiltonian replica exchange simulations of glucose oxidase adsorption on charged surfaces. Phys Chem Chem Phys 2018; 20:14587-14596. [PMID: 29766166 DOI: 10.1039/c8cp00530c] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Hamiltonian replica exchange Monte Carlo simulations efficiently identify the lowest-energy orientations of proteins on charged surfaces at variable ionic strengths.
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Affiliation(s)
- Yun Xie
- Huizhou University
- Huizhou
- P. R. China
- School of Chemistry and Chemical Engineering
- Guangdong Provincial Key Lab for Green Chemical Product Technology
| | - Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University
- Guangzhou
- P. R. China
| | - Jian Zhou
- School of Chemistry and Chemical Engineering
- Guangdong Provincial Key Lab for Green Chemical Product Technology
- South China University of Technology
- Guangzhou
- P. R. China
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24
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Arefi HH, Yamamoto T. Communication: Self-assembly of a model supramolecular polymer studied by replica exchange with solute tempering. J Chem Phys 2017; 147:211102. [PMID: 29221407 DOI: 10.1063/1.5008275] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Conventional molecular-dynamics (cMD) simulation has a well-known limitation in accessible time and length scales, and thus various enhanced sampling techniques have been proposed to alleviate the problem. In this paper, we explore the utility of replica exchange with solute tempering (REST) (i.e., a variant of Hamiltonian replica exchange methods) to simulate the self-assembly of a supramolecular polymer in explicit solvent and compare the performance with temperature-based replica exchange MD (T-REMD) as well as cMD. As a test system, we consider a relatively simple all-atom model of supramolecular polymerization (namely, benzene-1,3,5-tricarboxamides in methylcyclohexane solvent). Our results show that both REST and T-REMD are able to predict highly ordered polymer structures with helical H-bonding patterns, in contrast to cMD which completely fails to obtain such a structure for the present model. At the same time, we have also experienced some technical challenge (i.e., aggregation-dispersion transition and the resulting bottleneck for replica traversal), which is illustrated numerically. Since the computational cost of REST scales more moderately than T-REMD, we expect that REST will be useful for studying the self-assembly of larger systems in solution with enhanced rearrangement of monomers.
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Affiliation(s)
- Hadi H Arefi
- Department of Chemistry, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
| | - Takeshi Yamamoto
- Department of Chemistry, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
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25
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Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges. J Comput Aided Mol Des 2017; 32:287-297. [PMID: 28918599 DOI: 10.1007/s10822-017-0065-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 09/08/2017] [Indexed: 10/18/2022]
Abstract
The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ ~ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ ~ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.
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26
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Cole DJ, Janecek M, Stokes JE, Rossmann M, Faver JC, McKenzie GJ, Venkitaraman AR, Hyvönen M, Spring DR, Huggins DJ, Jorgensen WL. Computationally-guided optimization of small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction. Chem Commun (Camb) 2017; 53:9372-9375. [PMID: 28787041 PMCID: PMC5591577 DOI: 10.1039/c7cc05379g] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Free energy perturbation theory, in combination with enhanced sampling of protein-ligand binding modes, is evaluated in the context of fragment-based drug design, and used to design two new small-molecule inhibitors of the Aurora A kinase-TPX2 protein-protein interaction.
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Affiliation(s)
- Daniel J. Cole
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , USA , School of Chemistry , Newcastle University , Newcastle upon Tyne NE1 7RU , UK .
| | - Matej Janecek
- MRC Cancer Unit , University of Cambridge , Hills Road , Cambridge CB2 0XZ , UK , Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , UK
| | - Jamie E. Stokes
- Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , UK
| | - Maxim Rossmann
- Department of Biochemistry , University of Cambridge , 80 Tennis Court Road , Old Addenbrooke's Site , Cambridge CB2 1GA , UK
| | - John C. Faver
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , USA
| | - Grahame J. McKenzie
- MRC Cancer Unit , University of Cambridge , Hills Road , Cambridge CB2 0XZ , UK
| | | | - Marko Hyvönen
- Department of Biochemistry , University of Cambridge , 80 Tennis Court Road , Old Addenbrooke's Site , Cambridge CB2 1GA , UK
| | - David R. Spring
- Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , UK
| | - David J. Huggins
- MRC Cancer Unit , University of Cambridge , Hills Road , Cambridge CB2 0XZ , UK , Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , UK , Theory of Condensed Matter Group , Cavendish Laboratory , 19 JJ Thomson Avenue , Cambridge CB3 0HE , UK
| | - William L. Jorgensen
- Department of Chemistry , Yale University , New Haven , Connecticut 06520-8107 , USA
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27
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Abel R, Wang L, Harder ED, Berne BJ, Friesner RA. Advancing Drug Discovery through Enhanced Free Energy Calculations. Acc Chem Res 2017; 50:1625-1632. [PMID: 28677954 DOI: 10.1021/acs.accounts.7b00083] [Citation(s) in RCA: 189] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.
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Affiliation(s)
- Robert Abel
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Lingle Wang
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Edward D. Harder
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - B. J. Berne
- Department
of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
| | - Richard A. Friesner
- Department
of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
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28
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Abel R, Mondal S, Masse C, Greenwood J, Harriman G, Ashwell MA, Bhat S, Wester R, Frye L, Kapeller R, Friesner RA. Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 2017; 43:38-44. [DOI: 10.1016/j.sbi.2016.10.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 10/07/2016] [Indexed: 01/08/2023]
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29
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Mohamed NA, Bradshaw RT, Essex JW. Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field. J Comput Chem 2016; 37:2749-2758. [PMID: 27757978 PMCID: PMC5111595 DOI: 10.1002/jcc.24500] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/05/2016] [Accepted: 09/07/2016] [Indexed: 01/24/2023]
Abstract
The effects of electronic polarization in biomolecular interactions will differ depending on the local dielectric constant of the environment, such as in solvent, DNA, proteins, and membranes. Here the performance of the AMOEBA polarizable force field is evaluated under nonaqueous conditions by calculating the solvation free energies of small molecules in four common organic solvents. Results are compared with experimental data and equivalent simulations performed with the GAFF pairwise-additive force field. Although AMOEBA results give mean errors close to "chemical accuracy," GAFF performs surprisingly well, with statistically significantly more accurate results than AMOEBA in some solvents. However, for both models, free energies calculated in chloroform show worst agreement to experiment and individual solutes are consistently poor performers, suggesting non-potential-specific errors also contribute to inaccuracy. Scope for the improvement of both potentials remains limited by the lack of high quality experimental data across multiple solvents, particularly those of high dielectric constant. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Noor Asidah Mohamed
- Computational Systems Chemistry, School of ChemistryUniversity of SouthamptonHighfieldSouthamptonSO17 1BJUK
| | - Richard T. Bradshaw
- Computational Systems Chemistry, School of ChemistryUniversity of SouthamptonHighfieldSouthamptonSO17 1BJUK
| | - Jonathan W. Essex
- Computational Systems Chemistry, School of ChemistryUniversity of SouthamptonHighfieldSouthamptonSO17 1BJUK
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30
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Smith AK, Lockhart C, Klimov DK. Does Replica Exchange with Solute Tempering Efficiently Sample Aβ Peptide Conformational Ensembles? J Chem Theory Comput 2016; 12:5201-5214. [DOI: 10.1021/acs.jctc.6b00660] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Amy K. Smith
- School
of Systems Biology
and Computational Materials Science Center, George Mason University, Manassas, Virginia 20110, United States
| | - Christopher Lockhart
- School
of Systems Biology
and Computational Materials Science Center, George Mason University, Manassas, Virginia 20110, United States
| | - Dmitri K. Klimov
- School
of Systems Biology
and Computational Materials Science Center, George Mason University, Manassas, Virginia 20110, United States
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31
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Armacost KA, Goh GB, Brooks CL. Biasing Potential Replica Exchange Multisite λ-Dynamics for Efficient Free Energy Calculations. J Chem Theory Comput 2016; 11:1267-77. [PMID: 26579773 DOI: 10.1021/ct500894k] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Traditional free energy calculation methods are well-known for their drawbacks in scalability and speed in converging results particularly for calculations with large perturbations. In the present work, we report on the development of biasing potential replica exchange multisite λ-dynamics (BP-REX MSλD), which is a free energy method that is capable of performing simultaneous alchemical free energy transformations, including perturbations between flexible moieties. BP-REX MSλD and the original MSλD are applied to a series of symmetrical 2,5-benzoquinone derivatives covering a diverse chemical space and range of conformational flexibility. Improved λ-space sampling is observed for the BP-REX MSλD simulations, yielding a 2-5-fold increase in the number of transitions between substituents compared to traditional MSλD. We also demonstrate the efficacy of varying the value of c, the parameter that controls the ruggedness of the landscape mediating the sampling of λ-states, based on the flexibility of the fragment. Finally, we developed a protocol for maximizing the transition frequency between fragments. This protocol reduces the "kinetic barrier" for alchemically transforming fragments by grouping and ordering based on volume. These findings are applied to a challenging test set involving a series of geldanamycin-based inhibitors of heat shock protein 90 (Hsp90). Even though the perturbations span volume changes by as large as 60 Å(3), the values for the free energy change achieve an average unsigned error (AUE) of 1.5 kcal/mol relative to experimental Kd measurements with a reasonable correlation (R = 0.56). Our results suggest that the BP-REX MSλD algorithm is a highly efficient and scalable free energy method, which when utilized will enable routine calculations on the order of hundreds of compounds using only a few simulations.
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Affiliation(s)
- Kira A Armacost
- Department of Chemistry, University of Michigan , 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Garrett B Goh
- Department of Chemistry, University of Michigan , 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Charles L Brooks
- Department of Chemistry, University of Michigan , 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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Kaus JW, Harder E, Lin T, Abel R, McCammon JA, Wang L. How to deal with multiple binding poses in alchemical relative protein-ligand binding free energy calculations. J Chem Theory Comput 2016; 11:2670-9. [PMID: 26085821 PMCID: PMC4462751 DOI: 10.1021/acs.jctc.5b00214] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Indexed: 11/30/2022]
Abstract
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Recent
advances in improved force fields and sampling methods have made it
possible for the accurate calculation of protein–ligand binding
free energies. Alchemical free energy perturbation (FEP) using an
explicit solvent model is one of the most rigorous methods to calculate
relative binding free energies. However, for cases where there are
high energy barriers separating the relevant conformations that are
important for ligand binding, the calculated free energy may depend
on the initial conformation used in the simulation due to the lack
of complete sampling of all the important regions in phase space.
This is particularly true for ligands with multiple possible binding
modes separated by high energy barriers, making it difficult to sample
all relevant binding modes even with modern enhanced sampling methods.
In this paper, we apply a previously developed method that provides
a corrected binding free energy for ligands with multiple binding
modes by combining the free energy results from multiple alchemical
FEP calculations starting from all enumerated poses, and the results
are compared with Glide docking and MM-GBSA calculations. From these
calculations, the dominant ligand binding mode can also be predicted.
We apply this method to a series of ligands that bind to c-Jun N-terminal
kinase-1 (JNK1) and obtain improved free energy results. The dominant
ligand binding modes predicted by this method agree with the available
crystallography, while both Glide docking and MM-GBSA calculations
incorrectly predict the binding modes for some ligands. The method
also helps separate the force field error from the ligand sampling
error, such that deviations in the predicted binding free energy from
the experimental values likely indicate possible inaccuracies in the
force field. An error in the force field for a subset of the ligands
studied was identified using this method, and improved free energy
results were obtained by correcting the partial charges assigned to
the ligands. This improved the root-mean-square error (RMSE) for the
predicted binding free energy from 1.9 kcal/mol with the original
partial charges to 1.3 kcal/mol with the corrected partial charges.
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Affiliation(s)
- Joseph W Kaus
- Schödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
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Mentes A, Deng NJ, Vijayan RSK, Xia J, Gallicchio E, Levy RM. Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation. J Chem Theory Comput 2016; 12:2459-70. [PMID: 27070865 PMCID: PMC4862910 DOI: 10.1021/acs.jctc.6b00134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics modeling of complex biological systems is limited by finite simulation time. The simulations are often trapped close to local energy minima separated by high energy barriers. Here, we introduce Hamiltonian replica exchange (H-REMD) with torsional flattening in the Binding Energy Distribution Analysis Method (BEDAM), to reduce energy barriers along torsional degrees of freedom and accelerate sampling of intramolecular degrees of freedom relevant to protein-ligand binding. The method is tested on a standard benchmark (T4 Lysozyme/L99A/p-xylene complex) and on a library of HIV-1 integrase complexes derived from the SAMPL4 blind challenge. We applied the torsional flattening strategy to 26 of the 53 known binders to the HIV Integrase LEDGF site found to have a binding energy landscape funneled toward the crystal structure. We show that our approach samples the conformational space more efficiently than the original method without flattening when starting from a poorly docked pose with incorrect ligand dihedral angle conformations. In these unfavorable cases convergence to a binding pose within 2-3 Å from the crystallographic pose is obtained within a few nanoseconds of the Hamiltonian replica exchange simulation. We found that torsional flattening is insufficient in cases where trapping is due to factors other than torsional energy, such as the formation of incorrect intramolecular hydrogen bonds and stacking. Work is in progress to generalize the approach to handle these cases and thereby make it more widely applicable.
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Affiliation(s)
- Ahmet Mentes
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Nan-Jie Deng
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - R. S. K. Vijayan
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Junchao Xia
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, New York 11210, United States
| | - Ronald M. Levy
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
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Zhang BW, Dai W, Gallicchio E, He P, Xia J, Tan Z, Levy RM. Simulating Replica Exchange: Markov State Models, Proposal Schemes, and the Infinite Swapping Limit. J Phys Chem B 2016; 120:8289-301. [PMID: 27079355 DOI: 10.1021/acs.jpcb.6b02015] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Replica exchange molecular dynamics is a multicanonical simulation technique commonly used to enhance the sampling of solvated biomolecules on rugged free energy landscapes. While replica exchange is relatively easy to implement, there are many unanswered questions about how to use this technique most efficiently, especially because it is frequently the case in practice that replica exchange simulations are not fully converged. A replica exchange cycle consists of a series of molecular dynamics steps of a set of replicas moving under different Hamiltonians or at different thermodynamic states followed by one or more replica exchange attempts to swap replicas among the different states. How the replica exchange cycle is constructed affects how rapidly the system equilibrates. We have constructed a Markov state model of replica exchange (MSMRE) using long molecular dynamics simulations of a host-guest binding system as an example, in order to study how different implementations of the replica exchange cycle can affect the sampling efficiency. We analyze how the number of replica exchange attempts per cycle, the number of MD steps per cycle, and the interaction between the two parameters affects the largest implied time scale of the MSMRE simulation. The infinite swapping limit is an important concept in replica exchange. We show how to estimate the infinite swapping limit from the diagonal elements of the exchange transition matrix constructed from MSMRE "simulations of simulations" as well as from relatively short runs of the actual replica exchange simulations.
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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
| | - Wei Dai
- Department of Physics and Astronomy, Rutgers, the State University of New Jersey , Piscataway, New Jersey 08854, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York , Brooklyn, New York 11210, United States
| | - Peng He
- Center for Biophysics and Computational Biology, Department of Chemistry and Institute for Computational Molecular Science, Temple University , Philadelphia, Pennsylvania 19122, United States
| | - Junchao Xia
- 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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Computer Simulation and Modeling Techniques in the Study of Nanoparticle-Membrane Interactions. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/bs.arcc.2016.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
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Kaus JW, McCammon JA. Enhanced ligand sampling for relative protein-ligand binding free energy calculations. J Phys Chem B 2015; 119:6190-7. [PMID: 25906170 PMCID: PMC4442669 DOI: 10.1021/acs.jpcb.5b02348] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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Free
energy calculations are used to study how strongly potential
drug molecules interact with their target receptors. The accuracy
of these calculations depends on the accuracy of the molecular dynamics
(MD) force field as well as proper sampling of the major conformations
of each molecule. However, proper sampling of ligand conformations
can be difficult when there are large barriers separating the major
ligand conformations. An example of this is for ligands with an asymmetrically
substituted phenyl ring, where the presence of protein loops hinders
the proper sampling of the different ring conformations. These ring
conformations become more difficult to sample when the size of the
functional groups attached to the ring increases. The Adaptive Integration
Method (AIM) has been developed, which adaptively changes the alchemical
coupling parameter λ during the MD simulation so that conformations
sampled at one λ can aid sampling at the other λ values.
The Accelerated Adaptive Integration Method (AcclAIM) builds on AIM
by lowering potential barriers for specific degrees of freedom at
intermediate λ values. However, these methods may not work when
there are very large barriers separating the major ligand conformations.
In this work, we describe a modification to AIM that improves sampling
of the different ring conformations, even when there is a very large
barrier between them. This method combines AIM with conformational
Monte Carlo sampling, giving improved convergence of ring populations
and the resulting free energy. This method, called AIM/MC, is applied
to study the relative binding free energy for a pair of ligands that
bind to thrombin and a different pair of ligands that bind to aspartyl
protease β-APP cleaving enzyme 1 (BACE1). These protein–ligand
binding free energy calculations illustrate the improvements in conformational
sampling and the convergence of the free energy compared to both AIM
and AcclAIM.
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Affiliation(s)
- Joseph W Kaus
- †Department of Chemistry and Biochemistry,‡Center for Theoretical Biological Physics,¶Department of Pharmacology, and §Howard Hughes Medical Institute, University of California San Diego, La Jolla, California 92093-0365, United States
| | - J Andrew McCammon
- †Department of Chemistry and Biochemistry,‡Center for Theoretical Biological Physics,¶Department of Pharmacology, and §Howard Hughes Medical Institute, University of California San Diego, La Jolla, California 92093-0365, United States
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Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 2015; 137:2695-703. [PMID: 25625324 DOI: 10.1021/ja512751q] [Citation(s) in RCA: 807] [Impact Index Per Article: 89.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
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Affiliation(s)
- Lingle Wang
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
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Li X, Zhang L, Tian Y, Song Y, Zhan P, Liu X. Novel HIV-1 non-nucleoside reverse transcriptase inhibitors: a patent review (2011 – 2014). Expert Opin Ther Pat 2014; 24:1199-227. [DOI: 10.1517/13543776.2014.964685] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Molecular dynamics and Monte Carlo simulations for protein-ligand binding and inhibitor design. Biochim Biophys Acta Gen Subj 2014; 1850:966-971. [PMID: 25196360 DOI: 10.1016/j.bbagen.2014.08.018] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Revised: 08/26/2014] [Accepted: 08/28/2014] [Indexed: 11/24/2022]
Abstract
BACKGROUND Non-nucleoside inhibitors of HIV reverse transcriptase are an important component of treatment against HIV infection. Novel inhibitors are sought that increase potency against variants that contain the Tyr181Cys mutation. METHODS Molecular dynamics based free energy perturbation simulations have been run to study factors that contribute to protein-ligand binding, and the results are compared with those from previous Monte Carlo based simulations and activity data. RESULTS Predictions of protein-ligand binding modes are very consistent for the two simulation methods; the accord is attributed to the use of an enhanced sampling protocol. The Tyr181Cys binding pocket supports large, hydrophobic substituents, which is in good agreement with experiment. CONCLUSIONS Although some discrepancies exist between the results of the two simulation methods and experiment, free energy perturbation simulations can be used to rapidly test small molecules for gains in binding affinity. GENERAL SIGNIFICANCE Free energy perturbation methods show promise in providing fast, reliable and accurate data that can be used to complement experiment in lead optimization projects. This article is part of a Special Issue entitled "Recent developments of molecular dynamics".
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