1
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Lee S, Wang D, Seeliger MA, Tiwary P. Calculating Protein-Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling. J Chem Theory Comput 2024; 20:6341-6349. [PMID: 38991145 DOI: 10.1021/acs.jctc.4c00503] [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: 07/13/2024]
Abstract
Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long time scales. Recent advances in rare event sampling have allowed us to reach these time scales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitude of time scales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anticancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.
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Affiliation(s)
- Suemin Lee
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, United States
| | - Dedi Wang
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, United States
| | - Markus A Seeliger
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, New York 11794-8651, United States
| | - Pratyush Tiwary
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, United States
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park 20742, United States
- University of Maryland Institute for Health Computing, Bethesda, Maryland 20852, United States
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2
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Wang Y, Wu J, Zsolnay V, Pollard TD, Voth GA. Mechanism of Phosphate Release from Actin Filaments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.03.551904. [PMID: 37577500 PMCID: PMC10418243 DOI: 10.1101/2023.08.03.551904] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
After ATP-actin monomers assemble filaments, the ATP's γ-phosphate is hydrolyzed within seconds and dissociates over minutes. We used all-atom molecular dynamics simulations to sample the release of phosphate from filaments and study residues that gate release. Dissociation of phosphate from Mg2+ is rate limiting and associated with an energy barrier of 20 kcal/mol, consistent with experimental rates of phosphate release. Phosphate then diffuses in an internal cavity toward a gate formed by R177 suggested in prior computational studies and cryo-EM structures. The gate is closed when R177 hydrogen bonds with N111 and is open when R177 forms a salt bridge with D179. Most of the time interactions of R177 with other residues occludes the phosphate release pathway. Machine learning analysis reveals that the occluding interactions fluctuate rapidly, underscoring the secondary role of backdoor gate opening in Pi release, in contrast with the previous hypothesis that gate opening is the primary event.
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Affiliation(s)
- Yihang Wang
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics and James Frank Institute, University of Chicago, Chicago, IL
| | - Jiangbo Wu
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics and James Frank Institute, University of Chicago, Chicago, IL
| | - Vilmos Zsolnay
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, IL
| | - Thomas D. Pollard
- Department of Molecular Cellular and Developmental Biology, Yale University, New Haven, CT
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT
- Department of Cell Biology, Yale University, New Haven, CT
| | - Gregory A. Voth
- Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics and James Frank Institute, University of Chicago, Chicago, IL
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3
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Lee S, Wang D, Seeliger MA, Tiwary P. Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589710. [PMID: 38659748 PMCID: PMC11042289 DOI: 10.1101/2024.04.16.589710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long timescales. Recent advances in rare event sampling have allowed us to reach these timescales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitudes of timescales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anti-cancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.
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Affiliation(s)
- Suemin Lee
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
| | - Dedi Wang
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
| | - Markus A. Seeliger
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794-8651, USA
| | - Pratyush Tiwary
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
- University of Maryland Institute for Health Computing, Rockville, United States
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4
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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5
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Ruzmetov T, Montes R, Sun J, Chen SH, Tang Z, Chang CEA. Binding Kinetics Toolkit for Analyzing Transient Molecular Conformations and Computing Free Energy Landscapes. J Phys Chem A 2022; 126:8761-8770. [DOI: 10.1021/acs.jpca.2c05499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Talant Ruzmetov
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Ruben Montes
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Jianan Sun
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Si-Han Chen
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Zhiye Tang
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
| | - Chia-en A. Chang
- Department of Chemistry, University of California at Riverside, Riverside, California92521, United States
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6
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Baudel M, Guyader A, Lelièvre T. On the Hill relation and the mean reaction time for metastable processes. Stoch Process Their Appl 2022. [DOI: 10.1016/j.spa.2022.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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7
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Gohda K. Conformational Analysis of the Loop-to-Helix Transition of the α-Helix3 Plastic Region in the N-Terminal Domain of Human Hsp90α by a Computational Biochemistry Approach. J Chem Inf Model 2022; 62:5699-5714. [PMID: 36278922 DOI: 10.1021/acs.jcim.2c00984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Hsp90 is a chaperone protein aiding in correct protein folding and attractive for drug discovery. The structure of human Hsp90α N-terminal domain (NTD) is intriguing since the α-helix3 region of the ATP-binding site in the NTD plastically changes its conformation, i.e., loop-out, loop-in, and helical conformations, according to the bound inhibitor type. The plastic region structure is known to influence the mode of inhibition-inhibitors bound to a helix have a longer residence time in the complex, which is a factor of in vivo-active drugs, compared with loop binders. In this study, we analyzed the loop-to-helix transition of the plastic region through binding of a helix binder by a computational biochemistry approach. To generate the helical transition from the loop, the resorcinol inhibitor C1 complexed with a loop-in structure was alchemically transformed to the C10 inhibitor, which is known as a helix binder. The loop in the C1 complex possesses Leu107 tightly binding to the hydrophobic subpocket, considered as a key residue for the plasticity. From 10 × 1 μs simulations after the alchemical transformation, the helical transition was observed with a 29% success rate. Conformational analysis of the simulations identified residues possibly associated with the helical transition. The implementation of additional simulations (dihedral-constrained and in silico mutant simulations) led to a statistically significant increase in the transition success rate to 78%, as observed in Asn105 psi-constrained simulation. Therefore, we concluded that the Asn105 psi dihedral angle is most likely involved in the helical transition by a change of the dihedral angle to gauche-negative.
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Affiliation(s)
- Keigo Gohda
- Computer-aided Molecular Modeling Research Center, Kansai (CAMM-Kansai), 3-32-302, Tsuto-Otsuka, Nishinomiya 663-8241, Japan
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8
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Palacio-Rodriguez K, Vroylandt H, Stelzl LS, Pietrucci F, Hummer G, Cossio P. Transition Rates and Efficiency of Collective Variables from Time-Dependent Biased Simulations. J Phys Chem Lett 2022; 13:7490-7496. [PMID: 35939819 DOI: 10.1021/acs.jpclett.2c01807] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Simulations with adaptive time-dependent bias enable an efficient exploration of the conformational space of a system. However, the dynamic information is altered by the bias. Infrequent metadynamics recovers the transition rate of crossing a barrier, if the collective variables are ideal and there is no bias deposition near the transition state. Unfortunately, these conditions are not always fulfilled. To overcome these limitations, and inspired by single-molecule force spectroscopy, we use Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system. We assess the efficiency of collective variables parameter by measuring how efficiently the bias accelerates the transitions. We present approximate analytical expressions of the survival probability, reproducing the barrier-crossing time statistics and enabling the extraction of the unbiased transition rate even for challenging cases. We explore the limits of our method and provide convergence criteria to assess its validity.
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Affiliation(s)
- Karen Palacio-Rodriguez
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d'Histoire Naturelle, CNRS UMR 7590, 75005 Paris, France
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia, 050010 Medellín, Colombia
| | - Hadrien Vroylandt
- Institut des sciences du calcul et des données, Sorbonne Université, 75005 Paris, France
| | - Lukas S Stelzl
- Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
- KOMET 1, Institute of Physics, Johannes Gutenberg University Mainz, 55099 Mainz, Germany
- Institute of Molecular Biology, 55128 Mainz, Germany
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
| | - Fabio Pietrucci
- Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, Muséum National d'Histoire Naturelle, CNRS UMR 7590, 75005 Paris, France
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
- Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Pilar Cossio
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia, 050010 Medellín, Colombia
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany
- Center for Computational Mathematics, Flatiron Institute, 10010 New York, United States
- Center for Computational Biology, Flatiron Institute, 10010 New York, United States
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9
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Pramanik D, Pawar AB, Roy S, Singh JK. Mechanistic insights of key host proteins and potential repurposed inhibitors regulating SARS-CoV-2 pathway. J Comput Chem 2022; 43:1237-1250. [PMID: 35535951 PMCID: PMC9348233 DOI: 10.1002/jcc.26888] [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: 02/09/2022] [Revised: 04/03/2022] [Accepted: 04/22/2022] [Indexed: 12/16/2022]
Abstract
The emergence of pandemic situations originated from severe acute respiratory syndrome (SARS)‐CoV‐2 and its new variants created worldwide medical emergencies. Due to the non‐availability of efficient drugs and vaccines at these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS‐CoV‐2. Finding a suitable repurposing drug with inhibitory efficacy to a host‐protein is challenging. A detailed mechanistic understanding of the kinetics, (dis)association pathways, key protein residues facilitating the entry–exit of the drugs with targets are fundamental in selecting these repurposed drugs. Keeping this target as the goal of the paper, the potential repurposing drugs, Nafamostat, Camostat, Silmitasertib, Valproic acid, and Zotatifin with host‐proteins HDAC2, CSK22, eIF4E2 are studied to elucidate energetics, kinetics, and dissociation pathways. From an ensemble of independent simulations, we observed the presence of single or multiple dissociation pathways with varying host‐proteins‐drug systems and quantitatively estimated the probability of unbinding through these specific pathways. We also explored the crucial gateway residues facilitating these dissociation mechanisms. Interestingly, the residues we obtained for HDAC2 and CSK22 are also involved in the catalytic activity. Our results demonstrate how these potential drugs interact with the host machinery and the specific target residues, showing involvement in the mechanism. Most of these drugs are in the preclinical phase, and some are already being used to treat severe COVID‐19 patients. Hence, the mechanistic insight presented in this study is envisaged to support further findings of clinical studies and eventually develop efficient inhibitors to treat SARS‐CoV‐2.
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Affiliation(s)
- Debabrata Pramanik
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | | | - Sudip Roy
- Prescience Insilico Private Limited, Bangalore, India
| | - Jayant Kumar Singh
- Department of Chemical Engineering, Indian Institute of Technology Kanpur, Kanpur, India.,Prescience Insilico Private Limited, Bangalore, India
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10
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Nguyen HL, Thai NQ, Li MS. Determination of Multidirectional Pathways for Ligand Release from the Receptor: A New Approach Based on Differential Evolution. J Chem Theory Comput 2022; 18:3860-3872. [PMID: 35512104 PMCID: PMC9202309 DOI: 10.1021/acs.jctc.1c01158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
![]()
Steered molecular
dynamics (SMD) simulation is a powerful method
in computer-aided drug design as it can be used to access the relative
binding affinity with high precision but with low computational cost.
The success of SMD depends on the choice of the direction along which
the ligand is pulled from the receptor-binding site. In most simulations,
the unidirectional pathway was used, but in some cases, this choice
resulted in the ligand colliding with the complex surface of the exit
tunnel. To overcome this difficulty, several variants of SMD with
multidirectional pulling have been proposed, but they are not completely
devoid of disadvantages. Here, we have proposed to determine the direction
of pulling with a simple scoring function that minimizes the receptor–ligand
interaction, and an optimization algorithm called differential evolution
is used for energy minimization. The effectiveness of our protocol
was demonstrated by finding expulsion pathways of Huperzine A and
camphor from the binding site of Torpedo California acetylcholinesterase
and P450cam proteins, respectively, and comparing them with the previous
results obtained using memetic sampling and random acceleration molecular
dynamics. In addition, by applying this protocol to a set of ligands
bound with LSD1 (lysine specific demethylase 1), we obtained a much
higher correlation between the work of pulling force and experimental
data on the inhibition constant IC50 compared to that obtained using
the unidirectional approach based on minimal steric hindrance.
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Affiliation(s)
- Hoang Linh Nguyen
- Life Science Lab, Institute for Computational Science and Technology, QuangTrung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City 729110, Vietnam.,Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City 740500, Vietnam.,Vietnam National University, Ho Chi Minh City 71300, Vietnam
| | - Nguyen Quoc Thai
- Life Science Lab, Institute for Computational Science and Technology, QuangTrung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City 729110, Vietnam.,Dong Thap University, 783 Pham Huu Lau Street, Ward 6, Cao Lanh City, Dong Thap 81100, Vietnam
| | - Mai Suan Li
- Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, Warsaw 02-668, Poland
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11
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Wang J, Miao Y. Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics. J Chem Theory Comput 2022; 18:1275-1285. [PMID: 35099970 DOI: 10.1021/acs.jctc.1c00974] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.
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Affiliation(s)
- Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States
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12
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Pantsar T, Kaiser PD, Kudolo M, Forster M, Rothbauer U, Laufer SA. Decisive role of water and protein dynamics in residence time of p38α MAP kinase inhibitors. Nat Commun 2022; 13:569. [PMID: 35091547 PMCID: PMC8799644 DOI: 10.1038/s41467-022-28164-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 01/06/2022] [Indexed: 12/31/2022] Open
Abstract
Target residence time plays a crucial role in the pharmacological activity of small molecule inhibitors. Little is known, however, about the underlying causes of inhibitor residence time at the molecular level, which complicates drug optimization processes. Here, we employ all-atom molecular dynamics simulations (~400 μs in total) to gain insight into the binding modes of two structurally similar p38α MAPK inhibitors (type I and type I½) with short and long residence times that otherwise show nearly identical inhibitory activities in the low nanomolar IC50 range. Our results highlight the importance of protein conformational stability and solvent exposure, buried surface area of the ligand and binding site resolvation energy for residence time. These findings are further confirmed by simulations with a structurally diverse short residence time inhibitor SB203580. In summary, our data provide guidance in compound design when aiming for inhibitors with improved target residence time. The molecular determinants of the residence time of a small molecule inhibitor at its target protein are not well understood. Here, Pantsar et al. show that the target protein’s conformational stability and solvent exposure are key factors governing the target residence time of kinase inhibitors.
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Affiliation(s)
- Tatu Pantsar
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany.,School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland
| | - Philipp D Kaiser
- NMI Natural and Medical Sciences Institute at the University of Tuebingen, Markwiesenstrasse 55, 72770, Reutlingen, Germany
| | - Mark Kudolo
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Michael Forster
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Ulrich Rothbauer
- NMI Natural and Medical Sciences Institute at the University of Tuebingen, Markwiesenstrasse 55, 72770, Reutlingen, Germany.,Pharmaceutical Biotechnology, Eberhard Karls University Tuebingen, Markwiesenstrasse 55, 72770, Reutlingen, Germany.,Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, 72076, Tuebingen, Germany
| | - Stefan A Laufer
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls Universität Tübingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany. .,Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, 72076, Tuebingen, Germany. .,Tuebingen Center for Academic Drug Discovery & Development (TüCAD2), 72076, Tuebingen, Germany.
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13
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Ritonavir and xk263 Binding-Unbinding with HIV-1 Protease: Pathways, Energy and Comparison. LIFE (BASEL, SWITZERLAND) 2022; 12:life12010116. [PMID: 35054509 PMCID: PMC8779838 DOI: 10.3390/life12010116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 01/22/2023]
Abstract
Understanding non-covalent biomolecular recognition, which includes drug-protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand-protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand-protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand-protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand-protein recognition mechanisms and drug discovery.
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14
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Maximova E, Postnikov EB, Lavrova AI, Farafonov V, Nerukh D. Protein-Ligand Dissociation Rate Constant from All-Atom Simulation. J Phys Chem Lett 2021; 12:10631-10636. [PMID: 34704768 DOI: 10.1021/acs.jpclett.1c02952] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Dissociation of a ligand isoniazid from a protein catalase was investigated using all-atom molecular dynamics (MD) simulations. Random acceleration MD (τ-RAMD) was used, in which a random artificial force applied to the ligand facilitates its dissociation. We have suggested a novel approach to extrapolate such obtained dissociation times to the zero-force limit assuming never before attempted universal exponential dependence of the bond strength on the applied force, allowing direct comparison with experimentally measured values. We have found that our calculated dissociation time was equal to 36.1 s with statistically significant values distributed in the interval of 0.2-72.0 s, which quantitatively matches the experimental value of 50 ± 8 s despite the extrapolation over 9 orders of magnitude in time.
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Affiliation(s)
- Ekaterina Maximova
- Department of Nanobiotechnology, Alferov University, Khlopina Street, 8/3 A, 194021 Saint Petersburg, Russia
- Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Helmholtzstr. 10, 01069 Dresden, Germany
| | - Eugene B Postnikov
- Department of Theoretical Physics, Kursk State University, Radishcheva Street, 33, 305000 Kursk, Russia
| | - Anastasia I Lavrova
- Saint-Petersburg State University, 7/9 Universitetskaya Emb., 199034 Saint Petersburg, Russia
- Saint-Petersburg State Research Institute of Phthisiopulmonology, 2-4 Ligovskiy Avenue, 194064 Saint-Petersburg, Russia
| | - Vladimir Farafonov
- V. N. Karazin Kharkiv National University, 4 Svobody sq., Kharkiv 61022, Ukraine
| | - Dmitry Nerukh
- Department of Mathematics, Aston University, Birmingham B4 7ET, U.K
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15
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Carvalho HF, Ferrario V, Pleiss J. Molecular Mechanism of Methanol Inhibition in CALB-Catalyzed Alcoholysis: Analyzing Molecular Dynamics Simulations by a Markov State Model. J Chem Theory Comput 2021; 17:6570-6582. [PMID: 34494846 DOI: 10.1021/acs.jctc.1c00559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Lipases are widely used enzymes that catalyze hydrolysis and alcoholysis of fatty acid esters. At high concentrations of small alcohols such as methanol or ethanol, many lipases are inhibited by the substrate. The molecular basis of the inhibition of Candida antarctica lipase B (CALB) by methanol was investigated by unbiased molecular dynamics (MD) simulations, and the substrate binding kinetics was analyzed by Markov state models (MSMs). The modeled fluxes of productive methanol binding at concentrations between 50 mM and 5.5 M were in good agreement with the experimental activity profile of CALB, with a peak at 300 mM. The kinetic and structural analysis uncovered the molecular basis of CALB inhibition. Beyond 300 mM, the kinetic bottleneck results from crowding of methanol in the substrate access channel, which is caused by the gradual formation of methanol patches close to Leu140 (helix α5), Leu278, and Ile285 (helix α10) at a distance of 4-5 Å from the active site. Our findings demonstrate the usefulness of unbiased MD simulations to study enzyme-substrate interactions at realistic substrate concentrations and the feasibility of scale-bridging by an MSM analysis to derive kinetic information.
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Affiliation(s)
- Henrique F Carvalho
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Valerio Ferrario
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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16
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Bianciotto M, Gkeka P, Kokh DB, Wade RC, Minoux H. Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics. J Chem Theory Comput 2021; 17:6522-6535. [PMID: 34494849 DOI: 10.1021/acs.jctc.1c00453] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.
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Affiliation(s)
- Marc Bianciotto
- Molecular Design Sciences, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | - Paraskevi Gkeka
- Molecular Design Sciences, Sanofi R&D, 91 385 Chilly-Mazarin, France
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.,Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, 69120 Heidelberg, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany
| | - Hervé Minoux
- Data and Data Science, Sanofi R&D, 91 385 Chilly-Mazarin, France
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17
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Capponi S, Wang S, Navarro EJ, Bianco S. AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2021; 44:123. [PMID: 34613523 PMCID: PMC8493367 DOI: 10.1140/epje/s10189-021-00119-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 08/24/2021] [Indexed: 05/02/2023]
Abstract
We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.
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Affiliation(s)
- Sara Capponi
- IBM Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120, USA
- Center for Cellular Construction, San Francisco, CA, 94158, USA
| | - Shangying Wang
- IBM Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120, USA
- Center for Cellular Construction, San Francisco, CA, 94158, USA
| | - Erik J Navarro
- IBM Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120, USA
- Center for Cellular Construction, San Francisco, CA, 94158, USA
- Graduate Program in Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Simone Bianco
- IBM Almaden Research Center, 650 Harry Rd, San Jose, CA, 95120, USA.
- Center for Cellular Construction, San Francisco, CA, 94158, USA.
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18
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Zlobin A, Diankin I, Pushkarev S, Golovin A. Probing the Suitability of Different Ca 2+ Parameters for Long Simulations of Diisopropyl Fluorophosphatase. Molecules 2021; 26:5839. [PMID: 34641383 PMCID: PMC8510429 DOI: 10.3390/molecules26195839] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of diisopropyl fluorophosphatase (DFPase). Molecular modeling techniques offer a unique opportunity to address this task rationally by providing a physical description of the substrate-binding process. However, DFPase is a metalloenzyme, and correct modeling of metal cations is a challenging task generally coming with a tradeoff between simulation speed and accuracy. Here, we probe several molecular mechanical parameter combinations for their ability to empower long simulations needed to achieve a quantitative description of substrate binding. We demonstrate that a combination of the Amber19sb force field with the recently developed 12-6 Ca2+ models allows us to both correctly model DFPase and obtain new insights into the DFP binding process.
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Affiliation(s)
- Alexander Zlobin
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Igor Diankin
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
- Sirius University of Science and Technology, 354340 Sochi, Russia
| | - Sergey Pushkarev
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
| | - Andrey Golovin
- Faculty of Bioengineering, Lomonosov Moscow State University, 119234 Moscow, Russia; (I.D.); (S.P.)
- Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997 Moscow, Russia
- Sirius University of Science and Technology, 354340 Sochi, Russia
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19
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Buck M. Letting go: Deep computational modeling insights into pH-dependent calcium affinity. J Biol Chem 2021; 297:100974. [PMID: 34280436 PMCID: PMC8350533 DOI: 10.1016/j.jbc.2021.100974] [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] [Indexed: 11/24/2022] Open
Abstract
Calcium and other cofactors can feature as key additions to a molecular interface, to the extent that the cofactor is completely buried in the bound state. How can such an interaction be regulated then? The answer: By facilitating a switch through an allosteric network. Although a number of unbinding mechanisms are being characterized, an extensive computational study by Joswig et al. reveals a detailed model for the pattern recognition receptor langerin.
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Affiliation(s)
- Matthias Buck
- Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, USA.
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20
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Abstract
Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.
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21
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Decherchi S, Cavalli A. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chem Rev 2020; 120:12788-12833. [PMID: 33006893 PMCID: PMC8011912 DOI: 10.1021/acs.chemrev.0c00534] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Indexed: 12/19/2022]
Abstract
Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.
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Affiliation(s)
- Sergio Decherchi
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
| | - Andrea Cavalli
- Computational
and Chemical Biology, Fondazione Istituto
Italiano di Tecnologia, 16163 Genoa, Italy
- Department
of Pharmacy and Biotechnology, University
of Bologna, 40126 Bologna, Italy
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22
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Kokh DB, Doser B, Richter S, Ormersbach F, Cheng X, Wade RC. A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories. J Chem Phys 2020; 153:125102. [DOI: 10.1063/5.0019088] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Affiliation(s)
- Daria B. Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Bernd Doser
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Stefan Richter
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Fabian Ormersbach
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Xingyi Cheng
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
- Molecular Biosciences, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Rebecca C. Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany
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23
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Smith Z, Ravindra P, Wang Y, Cooley R, Tiwary P. Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics. J Phys Chem B 2020; 124:8221-8229. [DOI: 10.1021/acs.jpcb.0c03985] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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24
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Nunes-Alves A, Kokh DB, Wade RC. Recent progress in molecular simulation methods for drug binding kinetics. Curr Opin Struct Biol 2020; 64:126-133. [PMID: 32771530 DOI: 10.1016/j.sbi.2020.06.022] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/23/2020] [Accepted: 06/23/2020] [Indexed: 12/29/2022]
Abstract
Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.
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Affiliation(s)
- Ariane Nunes-Alves
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
| | - Daria B Kokh
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany; Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany.
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25
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Chaturvedi N, Nachliel E, Gutman M. Characterization of Pre‐Dissociative Structures of the E6AP Trimer by All‐atom Unbiased Molecular Dynamics. Isr J Chem 2020. [DOI: 10.1002/ijch.202000016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Navaneet Chaturvedi
- Laser Laboratory for Fast Reactions, Department of Biochemistry and Molecular BiologyTel Aviv University Israel
- Department of Molecular and Cell BiologyLeicester Institute of Structural and Chemical BiologyUniversity of Leicester Leicester LE1 9HN United Kingdom
| | - Esther Nachliel
- Laser Laboratory for Fast Reactions, Department of Biochemistry and Molecular BiologyTel Aviv University Israel
| | - Menachem Gutman
- Laser Laboratory for Fast Reactions, Department of Biochemistry and Molecular BiologyTel Aviv University Israel
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26
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Jagger BR, Kochanek SE, Haldar S, Amaro RE, Mulholland AJ. Multiscale simulation approaches to modeling drug-protein binding. Curr Opin Struct Biol 2020; 61:213-221. [PMID: 32113133 DOI: 10.1016/j.sbi.2020.01.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/16/2020] [Accepted: 01/21/2020] [Indexed: 01/19/2023]
Abstract
Simulations can provide detailed insight into the molecular processes involved in drug action, such as protein-ligand binding, and can therefore be a valuable tool for drug design and development. Processes with a large range of length and timescales may be involved, and understanding these different scales typically requires different types of simulation methodology. Ideally, simulations should be able to connect across scales, to analyze and predict how changes at one scale can influence another. Multiscale simulation methods, which combine different levels of treatment, are an emerging frontier with great potential in this area. Here we review multiscale frameworks of various types, and selected applications to biomolecular systems with a focus on drug-ligand binding.
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Affiliation(s)
- Benjamin R Jagger
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States
| | - Sarah E Kochanek
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States
| | - Susanta Haldar
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK.
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27
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Klyukin K, Alexandrov V. Kinetics of pH-dependent interactions between PD-1 and PD-L1 immune checkpoint proteins from molecular dynamics. Proteins 2020; 88:1162-1168. [PMID: 32105362 DOI: 10.1002/prot.25885] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 02/05/2020] [Accepted: 02/23/2020] [Indexed: 12/17/2022]
Abstract
Immune checkpoint blockade of signaling pathways such as PD-1/PD-L1 has recently opened up a new avenue for highly efficient immunotherapeutic strategies to treat cancer. Since tumor microenvironments are characterized by lower pH (5.5-7.0), pH-dependent protein-ligand interactions can be exploited as efficient means to regulate drug affinity and specificity for a variety of malignancies. In this article, we investigate the mechanism and kinetics of pH-dependent binding and unbinding processes for the PD-1/PD-L1 checkpoint pair employing classical molecular dynamics simulations. Two representative pH levels corresponding to circumneutral physiological conditions of blood (pH 7.4) and acidic tumor microenvironment (pH 5.5) are considered. Our calculations demonstrate that pH plays a key role in protein-ligand interactions with small pH changes leading to several orders of magnitude increase in binding affinity. By identifying the binding pocket in the PD-1/PD-L1 complex, we show a pivotal role of the His68 protonation state of PD-1in the complex stabilization at low pH. The results on the reaction rate constants are in qualitative agreement with available experimental data. The obtained molecular details are important for further engineering of binding/unbinding kinetics to formulate more efficient immune checkpoint blockade strategies.
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Affiliation(s)
- Konstantin Klyukin
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska
| | - Vitaly Alexandrov
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska.,Nebraska Center for Materials and Nanoscience, University of Nebraska-Lincoln, Lincoln, Nebraska
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28
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Ribeiro JML, Filizola M. Insights From Molecular Dynamics Simulations of a Number of G-Protein Coupled Receptor Targets for the Treatment of Pain and Opioid Use Disorders. Front Mol Neurosci 2019; 12:207. [PMID: 31507375 PMCID: PMC6716474 DOI: 10.3389/fnmol.2019.00207] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 08/07/2019] [Indexed: 01/20/2023] Open
Abstract
Effective treatments for pain management remain elusive due to the dangerous side-effects of current gold-standard opioid analgesics, including the respiratory depression that has led to skyrocketing death rates from opioid overdoses over the past decade. In an attempt to address the horrific opioid crisis worldwide, the National Institute on Drug Abuse has recently proposed boosting research on specific pharmacological mechanisms mediated by a number of G protein-coupled receptors (GPCRs). This research is expected to expedite the discovery of medications for opioid overdose and opioid use disorders, leading toward a safer and more effective treatment of pain. Here, we review mechanistic insights from recent all-atom molecular dynamics simulations of a specific subset of GPCRs for which high-resolution experimental structures are available, including opioid, cannabinoid, orexin, metabotropic glutamate, and dopamine receptor subtypes.
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Affiliation(s)
- João Marcelo Lamim Ribeiro
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Marta Filizola
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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29
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Bruce NJ, Ganotra GK, Richter S, Wade RC. KBbox: A Toolbox of Computational Methods for Studying the Kinetics of Molecular Binding. J Chem Inf Model 2019; 59:3630-3634. [DOI: 10.1021/acs.jcim.9b00485] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Neil J. Bruce
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Gaurav K. Ganotra
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
- Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp), Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
| | - Stefan Richter
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
| | - Rebecca C. Wade
- Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Im Neuenheimer Feld 282, 69120 Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
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30
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Rydzewski J, Valsson O. Finding multiple reaction pathways of ligand unbinding. J Chem Phys 2019; 150:221101. [DOI: 10.1063/1.5108638] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Jakub Rydzewski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87–100 Torun, Poland
| | - Omar Valsson
- Max Planck Institute for Polymer Research, Ackermannweg 10, D-55128 Mainz, Germany
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Pramanik D, Smith Z, Kells A, Tiwary P. Can One Trust Kinetic and Thermodynamic Observables from Biased Metadynamics Simulations?: Detailed Quantitative Benchmarks on Millimolar Drug Fragment Dissociation. J Phys Chem B 2019; 123:3672-3678. [DOI: 10.1021/acs.jpcb.9b01813] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Debabrata Pramanik
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
| | - Zachary Smith
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
| | - Adam Kells
- Department of Chemistry, King’s College London, SE1 1DB, London, U.K
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States
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