1
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Dai B, Chen JN, Zeng Q, Geng H, Wu YD. Accurate Structure Prediction for Cyclic Peptides Containing Proline Residues with High-Temperature Molecular Dynamics. J Phys Chem B 2024; 128:7322-7331. [PMID: 39028892 DOI: 10.1021/acs.jpcb.4c02004] [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: 07/21/2024]
Abstract
Cyclic peptides (CPs) are emerging as promising drug candidates. Numerous natural CPs and their analogs are effective therapeutics against various diseases. Notably, many of them contain peptidyl cis-prolyl bonds. Due to the high rotational barrier of peptide bonds, conventional molecular dynamics simulations struggle to effectively sample the cis/trans-isomerization of peptide bonds. Previous studies have highlighted the high accuracy of the residue-specific force field (RSFF) and the high sampling efficiency of high-temperature molecular dynamics (high-T MD). Herein, we propose a protocol that combines high-T MD with RSFF2C and a recently developed reweighting method based on probability densities for accurate structure prediction of proline-containing CPs. Our method successfully predicted 19 out of 23 CPs with the backbone rmsd < 1.0 Å compared to X-ray structures. Furthermore, we performed high-T MD and density reweighting on the sunflower trypsin inhibitor (SFTI-1)/trypsin complex to demonstrate its applicability in studying CP-complexes containing cis-prolines. Our results show that the conformation of SFTI-1 in aqueous solution is consistent with its bound conformation, potentially facilitating its binding.
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Affiliation(s)
- Botao Dai
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Jia-Nan Chen
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Qing Zeng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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2
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Chen JN, Dai B, Wu YD. Probability Density Reweighting of High-Temperature Molecular Dynamics. J Chem Theory Comput 2024; 20:4977-4985. [PMID: 38758038 DOI: 10.1021/acs.jctc.3c01423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Molecular dynamics (MD) simulation is a popular method for elucidating the structures and functions of biomolecules. However, exploring the conformational space, especially for large systems with slow transitions, often requires enhanced sampling methods. Although conducting MD at high temperatures provides a straightforward approach, resulting conformational ensembles diverge significantly from those at low temperatures. To address this discrepancy, we propose a novel probability density-based reweighting (PDR) method. PDR exhibits robust performance across four distinct systems, including a miniprotein, a cyclic peptide, a protein loop, and a protein-peptide complex. It accurately restores the conformational distributions at high temperatures to those at low temperatures. Additionally, we apply PDR to reweight previously studied high-T MD simulations of 12 protein-peptide complexes, enabling a comprehensive investigation of the conformational space of protein-peptide complexes.
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Affiliation(s)
- Jia-Nan Chen
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Botao Dai
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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3
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Miao J, Ghosh AP, Ho MN, Li C, Huang X, Pentelute BL, Baleja JD, Lin YS. Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides. J Phys Chem B 2024; 128:5281-5292. [PMID: 38785765 PMCID: PMC11163431 DOI: 10.1021/acs.jpcb.4c00157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024]
Abstract
Molecular dynamics simulation is a powerful tool for characterizing the solution structural ensembles of cyclic peptides. However, the ability of simulation to recapitulate experimental results and make accurate predictions largely depends on the force fields used. In our work here, we evaluate the performance of seven state-of-the-art force fields in recapitulating the experimental NMR results in water of 12 benchmark cyclic peptides, consisting of 6 cyclic pentapeptides, 4 cyclic hexapeptides, and 2 cyclic heptapeptides. The results show that RSFF2+TIP3P, RSFF2C+TIP3P, and Amber14SB+TIP3P exhibit similar and the best performance, all recapitulating the NMR-derived structure information on 10 cyclic peptides. Amber19SB+OPC successfully recapitulates the NMR-derived structure information on 8 cyclic peptides. In contrast, OPLS-AA/M+TIP4P, Amber03+TIP3P, and Amber14SBonlysc+GB-neck2 could only recapitulate the NMR-derived structure information on 5 cyclic peptides, the majority of which are not well-structured.
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Affiliation(s)
- Jiayuan Miao
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Arghya Pratim Ghosh
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Minh Ngoc Ho
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Chengxi Li
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
- College
of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang 310030, China
- Engineering
Research Center of Functional Materials Intelligent Manufacturing
of Zhejiang Province, ZJU-Hangzhou Global
Scientific and Technological Innovation Center, Hangzhou, Zhejiang 311215, China
| | - Xuejian Huang
- Graduate
Program in Pharmacology and Experimental Therapeutics, Graduate School
of Biomedical Sciences, Tufts University, Boston, Massachusetts 02111, United States
| | - Bradley L. Pentelute
- Department
of Chemistry, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139, United States
| | - James D. Baleja
- Graduate
Program in Pharmacology and Experimental Therapeutics, Graduate School
of Biomedical Sciences, Tufts University, Boston, Massachusetts 02111, United States
| | - Yu-Shan Lin
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
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4
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Zou JL, Chen KX, Wang XJ, Lu ZC, Wu XH, Wu YD. Structure-Based Rational and General Strategy for Stabilizing Single-Chain T-Cell Receptors to Enhance Affinity. J Med Chem 2024. [PMID: 38661304 DOI: 10.1021/acs.jmedchem.4c00503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
The T-cell receptor (TCR) is a crucial molecule in cellular immunity. The single-chain T-cell receptor (scTCR) is a potential format in TCR therapeutics because it eliminates the possibility of αβ-TCR mispairing. However, its poor stability and solubility impede the in vitro study and manufacturing of therapeutic applications. In this study, some conserved structural motifs are identified in variable domains regardless of germlines and species. Theoretical analysis helps to identify those unfavored factors and leads to a general strategy for stabilizing scTCRs by substituting residues at exact IMGT positions with beneficial propensities on the consensus sequence of germlines. Several representative scTCRs are displayed to achieve stability optimization and retain comparable binding affinities with the corresponding αβ-TCRs in the range of μM to pM. These results demonstrate that our strategies for scTCR engineering are capable of providing the affinity-enhanced and specificity-retained format, which are of great value in facilitating the development of TCR-related therapeutics.
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Affiliation(s)
- Jia-Ling Zou
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | | | | | | | - Xian-Hui Wu
- Tianmu Institute of Health, Changzhou 213399, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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5
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Abstract
A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.
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Affiliation(s)
- Ke-Wei Chen
- Lab of Computional Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Tian-Yu Sun
- Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Lab of Computional Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Shenzhen Bay Laboratory, Shenzhen 518132, China
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6
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Zhang P, Yang W. Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein. J Chem Phys 2023; 159:024118. [PMID: 37431910 PMCID: PMC10481389 DOI: 10.1063/5.0142280] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/22/2023] [Indexed: 07/12/2023] Open
Abstract
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.
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Affiliation(s)
- Pan Zhang
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Weitao Yang
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
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7
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Moore SJ, Deplazes E, Mancera RL. Influence of force field choice on the conformational landscape of rat and human islet amyloid polypeptide. Proteins 2023; 91:338-353. [PMID: 36163697 PMCID: PMC10092333 DOI: 10.1002/prot.26432] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 02/04/2023]
Abstract
Human islet amyloid polypeptide (hIAPP) is a naturally occurring, intrinsically disordered protein (IDP) whose abnormal aggregation into toxic soluble oligomers and insoluble amyloid fibrils is a pathological feature in type-2 diabetes. Rat IAPP (rIAPP) differs from hIAPP by only six amino acids yet has a reduced tendency to aggregate or form fibrils. The structures of the monomeric forms of IAPP are difficult to characterize due to their intrinsically disordered nature. Molecular dynamics simulations can provide a detailed characterization of the monomeric forms of rIAPP and hIAPP in near-physiological conditions. In this work, the conformational landscapes of rIAPP and hIAPP as a function of secondary structure content were predicted using well-tempered bias exchange metadynamics simulations. Several combinations of commonly used biomolecular force fields and water models were tested. The predicted conformational preferences of both rIAPP and hIAPP are typical of IDPs, exhibiting dominant random coil structures but showing a low propensity for transient α-helical conformations. Predicted nuclear magnetic resonance Cα chemical shifts reveal different preferences with each force field towards certain conformations, with AMBERff99SBnmr2/TIP4Pd showing the best agreement with the experiment. Comparisons of secondary structure content demonstrate residue-specific differences between hIAPP and rIAPP that may reflect their different aggregation propensities.
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Affiliation(s)
- Sandra J Moore
- Curtin Medical School, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, Western Australia, Australia
| | - Evelyne Deplazes
- Curtin Medical School, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, Western Australia, Australia.,School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia
| | - Ricardo L Mancera
- Curtin Medical School, Curtin Health Innovation Research Institute, Curtin Institute for Computation, Curtin University, Perth, Western Australia, Australia
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8
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Cui X, Liu H, Chen HF. Polarizable Force Field of Intrinsically Disordered Proteins with CMAP and Reweighting Optimization. J Chem Inf Model 2022; 62:4970-4982. [PMID: 36178373 DOI: 10.1021/acs.jcim.2c00835] [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
Intrinsically disordered proteins (IDPs) are highly structurally heterogeneous without a specific tertiary structure under physiology conditions and play key roles in the development of human diseases. Due to the characteristics of diverse conformations, as one of the important methods, molecular dynamics simulation can complement information for experimental methods. Because of the enrichment for charged amino acids for IDPs, polarizable force fields should be a good choice for the simulation of IDPs. However, current polarizable force fields are limited in sampling conformer features of IDPs. Therefore, a polarizable force field was released and named Drude2019IDP based on Drude2019 with reweighting and grid-based potential energy correction map optimization. In order to evaluate the performance of Drude2019IDP, 16 dipeptides, 18 short peptides, 3 representative IDPs, and 5 structural proteins were simulated. The results show that the NMR observables driven by Drude2019IDP are in better agreement with the experiment data than those by Drude2019 on short peptides and IDPs. Drude2019IDP can sample more diverse conformations than Drude2019. Furthermore, the performances of the two force fields are similar to the sample ordered proteins. These results confirm that the developed Drude2019IDP can improve the reproduction of conformers for intrinsically disordered proteins and can be used to gain insight into the paradigm of sequence-disorder for IDPs.
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Affiliation(s)
- Xiaochen Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai200240, China
| | - Hao Liu
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai200240, China
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai200240, China.,Shanghai Center for Bioinformation Technology, Shanghai200235, China
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9
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Chen JN, Jiang F, Wu YD. Accurate Prediction for Protein-Peptide Binding Based on High-Temperature Molecular Dynamics Simulations. J Chem Theory Comput 2022; 18:6386-6395. [PMID: 36149394 DOI: 10.1021/acs.jctc.2c00743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The structural characterization of protein-peptide interactions is fundamental to elucidating biological processes and designing peptide drugs. Molecular dynamics (MD) simulations are extensively used to study biomolecular systems. However, simulating the protein-peptide binding process is usually quite expensive. Based on our previous studies, herein, we propose a simple and effective method to predict the binding site and pose of the peptide simultaneously using high-temperature (high-T) MD simulations with the RSFF2C force field. Thousands of binding events (nonspecific or specific) can be sampled during microseconds of high-T MD. From density-based clustering analysis, the structures of all of the 12 complexes (nine with linear peptides and three with cyclic peptides) can be successfully predicted with root-mean-square deviation (RMSD) < 2.5 Å. By directly simulating the process of the ligand binding onto the receptor, our method approaches experimental precision for the first time, significantly surpassing previous protein-peptide docking methods in terms of accuracy.
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Affiliation(s)
- Jia-Nan Chen
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,Shenzhen Bay Laboratory, Shenzhen 518132, China.,College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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10
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Yu L, Brüschweiler R. Quantitative prediction of ensemble dynamics, shapes and contact propensities of intrinsically disordered proteins. PLoS Comput Biol 2022; 18:e1010036. [PMID: 36084124 PMCID: PMC9491582 DOI: 10.1371/journal.pcbi.1010036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/21/2022] [Accepted: 08/03/2022] [Indexed: 12/29/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup, which are two IDPs that fold upon binding to their targets, how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including 15N spin relaxation, without the need for reweighting. Further inspection revealed that 10-20% of the individual MD trajectories display the formation of secondary structures not observed in the experimental NMR data. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function.
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Affiliation(s)
- Lei Yu
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, United States of America
| | - Rafael Brüschweiler
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, United States of America
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail:
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11
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Feng JJ, Chen JN, Kang W, Wu YD. Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C. J Chem Theory Comput 2021; 17:4614-4628. [PMID: 34170125 DOI: 10.1021/acs.jctc.1c00341] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein loops, connecting the α-helices and β-strands, are involved in many important biological processes. However, due to their conformational flexibility, it is still challenging to accurately determine three-dimensional (3D) structures of long loops experimentally and computationally. Herein, we present a systematic study of the protein loop structure prediction via a total of ∼850 μs molecular dynamics (MD) simulations. For a set of 15 long (10-16 residues) and solvent-exposed loops, we first evaluated the performance of four state-of-the-art loop modeling algorithms, DaReUS-Loop, Sphinx, Rosetta-NGK, and MODELLER, on each loop, and none of them could accurately predict the structures for most loops. Then, temperature replica exchange molecular dynamics (REMD) simulations were conducted with three recent force fields, RSFF2C with TIP3P water model, CHARMM36m with CHARMM-modified TIP3P, and AMBER ff19SB with OPC. We found that our recently developed residue-specific force field RSFF2C performed the best and successfully predicted 12 out of 15 loops with a root-mean-square deviation (RMSD) < 1.5 Å. As an alternative with lower computational cost, normal MD simulations at high temperatures (380, 500, and 620 K) were investigated. Temperature-dependent performance was observed for each force field, and, for RSFF2C+TIP3P, we found that three independent 100-ns MD simulations at 500 K gave comparable results with REMD simulations. These results suggest that MD simulations, especially with enhanced sampling techniques such as replica exchange, with the RSFF2C force field could be useful for accurate loop structure prediction.
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Affiliation(s)
- Jia-Jie Feng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Jia-Nan Chen
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Wei Kang
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.,Shenzhen Bay Laboratory, Shenzhen 518132, China
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12
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An K, Zhou JB, Xiong Y, Han W, Wang T, Ye ZQ, Wu YD. Computational Studies of the Structural Basis of Human RPS19 Mutations Associated With Diamond-Blackfan Anemia. Front Genet 2021; 12:650897. [PMID: 34108988 PMCID: PMC8181406 DOI: 10.3389/fgene.2021.650897] [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: 01/12/2021] [Accepted: 04/28/2021] [Indexed: 11/13/2022] Open
Abstract
Diamond-Blackfan Anemia (DBA) is an inherited rare disease characterized with severe pure red cell aplasia, and it is caused by the defective ribosome biogenesis stemming from the impairment of ribosomal proteins. Among all DBA-associated ribosomal proteins, RPS19 affects most patients and carries most DBA mutations. Revealing how these mutations lead to the impairment of RPS19 is highly demanded for understanding the pathogenesis of DBA, but a systematic study is currently lacking. In this work, based on the complex structure of human ribosome, we comprehensively studied the structural basis of DBA mutations of RPS19 by using computational methods. Main structure elements and five conserved surface patches involved in RPS19-18S rRNA interaction were identified. We further revealed that DBA mutations would destabilize RPS19 through disrupting the hydrophobic core or breaking the helix, or perturb the RPS19-18S rRNA interaction through destroying hydrogen bonds, introducing steric hindrance effect, or altering surface electrostatic property at the interface. Moreover, we trained a machine-learning model to predict the pathogenicity of all possible RPS19 mutations. Our work has laid a foundation for revealing the pathogenesis of DBA from the structural perspective.
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Affiliation(s)
- Ke An
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Jing-Bo Zhou
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Yao Xiong
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Wei Han
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Tao Wang
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Zhi-Qiang Ye
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Yun-Dong Wu
- State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China
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13
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Mu J, Liu H, Zhang J, Luo R, Chen HF. Recent Force Field Strategies for Intrinsically Disordered Proteins. J Chem Inf Model 2021; 61:1037-1047. [PMID: 33591749 PMCID: PMC8256680 DOI: 10.1021/acs.jcim.0c01175] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.
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Affiliation(s)
- Junxi Mu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hao Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, School of Medicine, Shanghai Jiao Tong University, Shanghai 20025, China
| | - Ray Luo
- Departments of Molecular Biology and Biochemistry, Chemical and Molecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, California 92697-3900, United States
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
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14
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Wang W. Recent advances in atomic molecular dynamics simulation of intrinsically disordered proteins. Phys Chem Chem Phys 2021; 23:777-784. [PMID: 33355572 DOI: 10.1039/d0cp05818a] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Intrinsically disordered proteins (IDPs) play important roles in cellular functions. The inherent structural heterogeneity of IDPs makes the high-resolution experimental characterization of IDPs extremely difficult. Molecular dynamics (MD) simulation could provide the atomic-level description of the structural and dynamic properties of IDPs. This perspective reviews the recent progress in atomic MD simulation studies of IDPs, including the development of force fields and sampling methods, as well as applications in IDP-involved protein-protein interactions. The employment of large-scale simulations and advanced sampling techniques allows more accurate estimation of the thermodynamics and kinetics of IDP-mediated protein interactions, and the holistic landscape of the binding process of IDPs is emerging.
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Affiliation(s)
- Wenning Wang
- Department of Chemistry, Multiscale Research Institute of Complex Systems and Institute of Biomedical Sciences, Fudan University, Shanghai 200438, China.
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15
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Yu L, Li DW, Brüschweiler R. Systematic Differences between Current Molecular Dynamics Force Fields To Represent Local Properties of Intrinsically Disordered Proteins. J Phys Chem B 2021; 125:798-804. [PMID: 33444020 DOI: 10.1021/acs.jpcb.0c10078] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The prevalence of intrinsically disordered proteins (IDPs) and protein regions in structural biology has prompted the recent development of molecular dynamics (MD) force fields for the more realistic representations of such systems. Using experimental nuclear magnetic resonance backbone scalar 3J-coupling constants of the intrinsically disordered proteins α-synuclein and amyloid-β in their native aqueous environment as a metric, we compare the performance of four recent MD force fields, namely, AMBER ff14SB, CHARMM C36m, AMBER ff99SB-disp, and AMBER ff99SBnmr2, by partitioning the polypeptides into an overlapping series of heptapeptides for which a cumulative total of 276 μs MD simulations were performed. The results show substantial differences between the different force fields at the individual residue level. Except for ff99SBnmr2, the force fields systematically underestimate the scalar 3J(HN,Hα)-couplings due to an underrepresentation of β-conformations and an overrepresentation of either α- or PPII conformations. The study demonstrates that the incorporation of coil library information in modern MD force fields, as shown here for ff99SBnmr2, provides substantially improved performance and more realistic sampling of the local backbone dihedral angles of IDPs as reflected by the good accuracy of the computed scalar 3J(HN,Hα)-couplings with less than 0.5 Hz error. Such force fields will enable a better understanding of how structural dynamics and thermodynamics influence the IDP function. Although the methodology based on heptapeptides used here does not allow the assessment of potential intramolecular long-range interactions, its computational affordability permits well-converged simulations that can be easily parallelized. This should make the quantitative validation of intrinsic disorder observed in MD simulations of polypeptides with experimental scalar J-couplings widely applicable.
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Affiliation(s)
- Lei Yu
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States
| | - Da-Wei Li
- Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States
| | - Rafael Brüschweiler
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.,Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, United States.,Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, United States
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16
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Zhang YN, Zhang XC, Zhu R, Yao WC, Xu JW, Wang M, Ren JY, Xu CZ, Huang ZR, Zhang XW, Yu W, Liao HX, Yuan XH, Wu XM. Computational and Experimental Approaches to Decipher the Binding Mechanism of General Odorant-Binding Protein 2 from Athetis lepigone to Chlorpyrifos and Phoxim. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:88-100. [PMID: 33356208 DOI: 10.1021/acs.jafc.0c05389] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Insect resistance to insecticides is an increasingly serious problem, and the resistant mechanisms are complicated. The resistance research based on the chemosensory pathway is one of the hot problems at present, but the specific binding mechanism of chemosensory genes and insecticides remains elusive. The binding mechanism of AlepGOBP2 (belong to insect chemosensory gene) with two insecticides was investigated by computational and experimental approaches. Our calculation results indicated that four key residues (Phe12, Ile52, Ile94, and Phe118) could steadily interact with these two insecticides and be assigned as hotspot sites responsible for their binding affinities. The significant alkyl-π and hydrophobic interactions involved by these four hotspot residues were found to be the driving forces for their binding affinities, especially for two residues (Phe12 and Ile94) that significantly contribute to the binding of chlorpyrifos, which were also validated by our binding assay results. Furthermore, we also found that the AlepGOBP2-chlorpyrifos/phoxim complexes can be more efficiently converged in the residue-specific force field-(RSFF2C) and its higher accuracy and repeatability in protein dynamics simulation, per-residue free energy decomposition, and computational alanine scanning calculations have also been achieved in this paper. These findings provided useful insights for efficient and reliable calculation of the binding mechanism of relevant AlepGOBPs with other insecticides, facilitating to develop new and efficient insecticides targeting the key sites of AlepGOBP2.
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Affiliation(s)
- Ya-Nan Zhang
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Xiao-Chun Zhang
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Rui Zhu
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Wei-Chen Yao
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Ji-Wei Xu
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Meng Wang
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Jia-Yi Ren
- Zhuhai College of Jilin University, Zhuhai 519041, China
- Institute of Biomedicine, Jinan University, Guangzhou 510632, P. R. China
| | - Cheng-Zhen Xu
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Zhuo-Ran Huang
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Xing-Wang Zhang
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
| | - Wei Yu
- Zhuhai College of Jilin University, Zhuhai 519041, China
- Institute of Biomedicine, Jinan University, Guangzhou 510632, P. R. China
| | - Hua-Xin Liao
- Institute of Biomedicine, Jinan University, Guangzhou 510632, P. R. China
| | - Xiao-Hui Yuan
- Institute of Biomedicine, Jinan University, Guangzhou 510632, P. R. China
- Zhuhai Trinomab Biotechnology Co., Ltd., Zhuhai 519040, China
| | - Xiao-Min Wu
- Anhui Province Key Laboratory of Pollutant Sensitive Materials and Environmental Remediation, College of Life Sciences, Huaibei Normal University, Huaibei 235000, China
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17
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Fatafta H, Samantray S, Sayyed-Ahmad A, Coskuner-Weber O, Strodel B. Molecular simulations of IDPs: From ensemble generation to IDP interactions leading to disorder-to-order transitions. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2021; 183:135-185. [PMID: 34656328 DOI: 10.1016/bs.pmbts.2021.06.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Abstract
Intrinsically disordered proteins (IDPs) lack a well-defined three-dimensional structure but do exhibit some dynamical and structural ordering. The structural plasticity of IDPs indicates that entropy-driven motions are crucial for their function. Many IDPs undergo function-related disorder-to-order transitions upon by their interaction with specific binding partners. Approaches that are based on both experimental and theoretical tools enable the biophysical characterization of IDPs. Molecular simulations provide insights into IDP structural ensembles and disorder-to-order transition mechanisms. However, such studies depend strongly on the chosen force field parameters and simulation techniques. In this chapter, we provide an overview of IDP characteristics, review all-atom force fields recently developed for IDPs, and present molecular dynamics-based simulation methods that allow IDP ensemble generation as well as the characterization of disorder-to-order transitions. In particular, we introduce metadynamics, replica exchange molecular dynamics simulations, and also kinetic models resulting from Markov State modeling, and provide various examples for the successful application of these simulation methods to IDPs.
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Affiliation(s)
- Hebah Fatafta
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Suman Samantray
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany; AICES Graduate School, RWTH Aachen University, Aachen, Germany
| | | | - Orkid Coskuner-Weber
- Molecular Biotechnology, Turkish-German University, Sahinkaya Caddesi, Istanbul, Turkey
| | - Birgit Strodel
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany; Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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18
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Development of Charge-Augmented Three-Point Water Model (CAIPi3P) for Accurate Simulations of Intrinsically Disordered Proteins. Int J Mol Sci 2020; 21:ijms21176166. [PMID: 32859072 PMCID: PMC7504337 DOI: 10.3390/ijms21176166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/17/2020] [Accepted: 08/21/2020] [Indexed: 01/15/2023] Open
Abstract
Intrinsically disordered proteins (IDPs) are molecules without a fixed tertiary structure, exerting crucial roles in cellular signalling, growth and molecular recognition events. Due to their high plasticity, IDPs are very challenging in experimental and computational structural studies. To provide detailed atomic insight in IDPs' dynamics governing their functional mechanisms, all-atom molecular dynamics (MD) simulations are widely employed. However, the current generalist force fields and solvent models are unable to generate satisfactory ensembles for IDPs when compared to existing experimental data. In this work, we present a new solvation model, denoted as the Charge-Augmented Three-Point Water Model for Intrinsically Disordered Proteins (CAIPi3P). CAIPi3P has been generated by performing a systematic scan of atomic partial charges assigned to the widely popular molecular scaffold of the three-point TIP3P water model. We found that explicit solvent MD simulations employing CAIPi3P solvation considerably improved the small-angle X-ray scattering (SAXS) scattering profiles for three different IDPs. Not surprisingly, this improvement was further enhanced by using CAIPi3P water in combination with the protein force field parametrized for IDPs. We also demonstrated the applicability of CAIPi3P to molecular systems containing structured as well as intrinsically disordered regions/domains. Our results highlight the crucial importance of solvent effects for generating molecular ensembles of IDPs which reproduce the experimental data available. Hence, we conclude that our newly developed CAIPi3P solvation model is a valuable tool for molecular simulations of intrinsically disordered proteins and assessing their molecular dynamics.
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19
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Song D, Liu H, Luo R, Chen HF. Environment-Specific Force Field for Intrinsically Disordered and Ordered Proteins. J Chem Inf Model 2020; 60:2257-2267. [PMID: 32227937 PMCID: PMC10449432 DOI: 10.1021/acs.jcim.0c00059] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The need for accurate and efficient force fields for modeling 3D structures of macrobiomolecules and in particular intrinsically disordered proteins (IDPs) has increased with recent findings to associate IDPs and human diseases. However, most conventional protein force fields and recent IDP-specific force fields are limited in reproducing accurate structural features of IDPs. Here, we present an environmental specific precise force field (ESFF1) based on CMAP corrections of 71 different sequence environments to improve the accuracy and efficiency of MD simulation for both IDPs and folded proteins. MD simulations of 84 different short peptides, IDPs, and structured proteins show that ESFF1 can accurately reproduce spectroscopic properties for different peptides and proteins whether they are disordered or ordered. The successful ab initio folding of five fast-folding proteins further supports the reliability of ESFF1. The extensive analysis documented here shows that ESFF1 is able to achieve a reasonable balance between ordered and disordered states in protein simulations.
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Affiliation(s)
- Dong Song
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hao Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ray Luo
- Departments of Molecular Biology and Biochemistry, Chemical and Molecular Engineering, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, California 92697-3900, United States
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Center for Bioinformation Technology, Shanghai 200235, China
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20
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Kang W, Jiang F, Wu YD, Wales DJ. Multifunnel Energy Landscapes for Phosphorylated Translation Repressor 4E-BP2 and Its Mutants. J Chem Theory Comput 2019; 16:800-810. [PMID: 31774674 PMCID: PMC7462351 DOI: 10.1021/acs.jctc.9b01042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Upon phosphorylation of specific sites, eukaryotic translation initiation factor 4E (eIF4E) binding protein 2 (4E-BP2) undergoes a fundamental structural transformation from a disordered state to a four-stranded β-sheet, leading to decreased binding affinity for its partner. This change reflects the significant effects of phosphate groups on the underlying energy landscapes of proteins. In this study, we combine high-temperature molecular dynamics simulations and discrete path sampling to construct energy landscapes for a doubly phosphorylated 4E-BP218-62 and two mutants (a single site mutant D33K and a double mutant Y54A/L59A). The potential and free energy landscapes for these three systems are multifunneled with the folded state and several alternative states lying close in energy, suggesting perhaps a multifunneled and multifunctional protein. Hydrogen bonds between phosphate groups and other residues not only stabilize these low-lying conformations to different extents but also play an important role in interstate transitions. From the energy landscape perspective, our results explain some interesting experimental observations, including the low stability of doubly phosphorylated 4E-BP2 and its moderate binding to eIF4E and the inability of phosphorylated Y54A/L59A to fold.
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Affiliation(s)
- Wei Kang
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics , Peking University Shenzhen Graduate School , Shenzhen 518055 , China.,College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China.,Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , U.K
| | - Fan Jiang
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics , Peking University Shenzhen Graduate School , Shenzhen 518055 , China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics , Peking University Shenzhen Graduate School , Shenzhen 518055 , China.,College of Chemistry and Molecular Engineering , Peking University , Beijing 100871 , China
| | - David J Wales
- Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , U.K
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21
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Tian C, Kasavajhala K, Belfon KAA, Raguette L, Huang H, Migues AN, Bickel J, Wang Y, Pincay J, Wu Q, Simmerling C. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J Chem Theory Comput 2019; 16:528-552. [PMID: 31714766 DOI: 10.1021/acs.jctc.9b00591] [Citation(s) in RCA: 848] [Impact Index Per Article: 169.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Molecular dynamics (MD) simulations have become increasingly popular in studying the motions and functions of biomolecules. The accuracy of the simulation, however, is highly determined by the molecular mechanics (MM) force field (FF), a set of functions with adjustable parameters to compute the potential energies from atomic positions. However, the overall quality of the FF, such as our previously published ff99SB and ff14SB, can be limited by assumptions that were made years ago. In the updated model presented here (ff19SB), we have significantly improved the backbone profiles for all 20 amino acids. We fit coupled φ/ψ parameters using 2D φ/ψ conformational scans for multiple amino acids, using as reference data the entire 2D quantum mechanics (QM) energy surface. We address the polarization inconsistency during dihedral parameter fitting by using both QM and MM in aqueous solution. Finally, we examine possible dependency of the backbone fitting on side chain rotamer. To extensively validate ff19SB parameters, and to compare to results using other Amber models, we have performed a total of ∼5 ms MD simulations in explicit solvent. Our results show that after amino-acid-specific training against QM data with solvent polarization, ff19SB not only reproduces the differences in amino-acid-specific Protein Data Bank (PDB) Ramachandran maps better but also shows significantly improved capability to differentiate amino-acid-dependent properties such as helical propensities. We also conclude that an inherent underestimation of helicity is present in ff14SB, which is (inexactly) compensated for by an increase in helical content driven by the TIP3P bias toward overly compact structures. In summary, ff19SB, when combined with a more accurate water model such as OPC, should have better predictive power for modeling sequence-specific behavior, protein mutations, and also rational protein design. Of the explicit water models tested here, we recommend use of OPC with ff19SB.
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Affiliation(s)
- Chuan Tian
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Koushik Kasavajhala
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Kellon A A Belfon
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Lauren Raguette
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - He Huang
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Angela N Migues
- Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - John Bickel
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Yuzhang Wang
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Jorge Pincay
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States
| | - Qin Wu
- Center for Functional Nanomaterials , Brookhaven National Laboratory , Upton , New York 11973 , United States
| | - Carlos Simmerling
- Department of Chemistry , Stony Brook University , Stony Brook , New York 11794 , United States.,Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 11794 , United States
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22
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Malliavin TE, Mucherino A, Lavor C, Liberti L. Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach. J Chem Inf Model 2019; 59:4486-4503. [PMID: 31442036 DOI: 10.1021/acs.jcim.9b00215] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The optimization approaches classically used during the determination of protein structure encounter various difficulties, especially when the size of the conformational space is large. Indeed, in such a case, algorithmic convergence criteria are more difficult to set up. Moreover, the size of the search space makes it difficult to achieve a complete exploration. The interval branch-and-prune (iBP) approach, based on the reformulation of the distance geometry problem (DGP) provides a theoretical frame for the generation of protein conformations, by systematically sampling the conformational space. When an appropriate subset of interatomic distances is known exactly, this worst-case exponential-time algorithm is provably complete and fixed-parameter tractable. These guarantees, however, immediately disappear as distance measurement errors are introduced. Here we propose an improvement of this approach: threading-augmented interval branch-and-prune (TAiBP), where the combinatorial explosion of the original iBP approach arising from its exponential complexity is alleviated by partitioning the input instances into consecutive peptide fragments and by using self-organizing maps (SOMs) to obtain clusters of similar solutions. A validation of the TAiBP approach is presented here on a set of proteins of various sizes and structures. The calculation inputs are a uniform covalent geometry extracted from force field covalent terms, the backbone dihedral angles with error intervals, and a few long-range distances. For most of the proteins smaller than 50 residues and interval widths of 20°, the TAiBP approach yielded solutions with RMSD values smaller than 3 Å with respect to the initial protein conformation. The efficiency of the TAiBP approach for proteins larger than 50 residues will require the use of nonuniform covalent geometry and may have benefits from the recent development of residue-specific force-fields.
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Affiliation(s)
- Thérèse E Malliavin
- Unité de Bioinformatique Structurale, UMR 3528, CNRS, and Departement de Bioinformatique, Biostatistique et Biologie Intégrative, USR 3756, CNRS , Institut Pasteur , 75015 Paris , France
| | | | - Carlile Lavor
- Applied Math Department , IMECC-University of Campinas , Campinas , SP 13083-970 , Brazil
| | - Leo Liberti
- LIX CNRS, Ecole Polytechnique , Institut Polytechnique de Paris , Route de Saclay , 91128 Palaiseau , France
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23
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Yang S, Liu H, Zhang Y, Lu H, Chen H. Residue-Specific Force Field Improving the Sample of Intrinsically Disordered Proteins and Folded Proteins. J Chem Inf Model 2019; 59:4793-4805. [DOI: 10.1021/acs.jcim.9b00647] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Sheng Yang
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU−Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Hao Liu
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU−Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Yangpeng Zhang
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU−Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU−Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, 800 Dongchuan Road, Shanghai, 200240, China
| | - Haifeng Chen
- State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, SJTU−Yale Joint Center for Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
- Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai, 200235, China
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24
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Allen AA, Robertson MJ, Payne MC, Cole DJ. Development and Validation of the Quantum Mechanical Bespoke Protein Force Field. ACS OMEGA 2019; 4:14537-14550. [PMID: 31528808 PMCID: PMC6740169 DOI: 10.1021/acsomega.9b01769] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/30/2019] [Indexed: 06/10/2023]
Abstract
Molecular mechanics force field parameters for macromolecules, such as proteins, are traditionally fit to reproduce experimental properties of small molecules, and thus, they neglect system-specific polarization. In this paper, we introduce a complete protein force field that is designed to be compatible with the quantum mechanical bespoke (QUBE) force field by deriving nonbonded parameters directly from the electron density of the specific protein under study. The main backbone and sidechain protein torsional parameters are rederived in this work by fitting to quantum mechanical dihedral scans for compatibility with QUBE nonbonded parameters. Software is provided for the preparation of QUBE input files. The accuracy of the new force field, and the derived torsional parameters, is tested by comparing the conformational preferences of a range of peptides and proteins with experimental measurements. Accurate backbone and sidechain conformations are obtained in molecular dynamics simulations of dipeptides, with NMR J coupling errors comparable to the widely used OPLS force field. In simulations of five folded proteins, the secondary structure is generally retained, and the NMR J coupling errors are similar to standard transferable force fields, although some loss of the experimental structure is observed in certain regions of the proteins. With several avenues for further development, the use of system-specific nonbonded force field parameters is a promising approach for next-generation simulations of biological molecules.
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Affiliation(s)
- Alice
E. A. Allen
- TCM
Group, Cavendish Laboratory, 19 JJ Thomson Ave, Cambridge CB3 0HE, United Kingdom
| | - Michael J. Robertson
- Department of Molecular and Cellular Physiology and Department of Structural Biology Stanford University School of Medicine, 279 Campus Drive, Stanford, California 94305, United States
| | - Michael C. Payne
- TCM
Group, Cavendish Laboratory, 19 JJ Thomson Ave, Cambridge CB3 0HE, United Kingdom
| | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, United
Kingdom
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25
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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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26
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Jiang F, Wu HN, Kang W, Wu YD. Developments and Applications of Coil-Library-Based Residue-Specific Force Fields for Molecular Dynamics Simulations of Peptides and Proteins. J Chem Theory Comput 2019; 15:2761-2773. [DOI: 10.1021/acs.jctc.8b00794] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fan Jiang
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Hao-Nan Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Wei Kang
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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