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Sowlati-Hashjin S, Gandhi A, Garton M. Dawn of a New Era for Membrane Protein Design. BIODESIGN RESEARCH 2022; 2022:9791435. [PMID: 37850134 PMCID: PMC10521746 DOI: 10.34133/2022/9791435] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/20/2022] [Indexed: 10/19/2023] Open
Abstract
A major advancement has recently occurred in the ability to predict protein secondary structure from sequence using artificial neural networks. This new accessibility to high-quality predicted structures provides a big opportunity for the protein design community. It is particularly welcome for membrane protein design, where the scarcity of solved structures has been a major limitation of the field for decades. Here, we review the work done to date on the membrane protein design and set out established and emerging tools that can be used to most effectively exploit this new access to structures.
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Affiliation(s)
- Shahin Sowlati-Hashjin
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2
| | - Aanshi Gandhi
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2
| | - Michael Garton
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2
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2
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Huang K, Luo S, Cong Y, Zhong S, Zhang JZH, Duan L. An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity. NANOSCALE 2020; 12:10737-10750. [PMID: 32388542 DOI: 10.1039/c9nr10638c] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔGPBSA_IE method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔGPBSA_IE method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.
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Affiliation(s)
- Kaifang Huang
- School of Physics and Electronics, Shandong Normal University, Jinan, 250014, China.
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3
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Wang E, Liu H, Wang J, Weng G, Sun H, Wang Z, Kang Y, Hou T. Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein–Ligand Binding Affinities. J Chem Inf Model 2020; 60:5353-5365. [DOI: 10.1021/acs.jcim.0c00024] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Yu Kang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou Zhejiang 310058, China
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4
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Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem Rev 2019; 119:9478-9508. [DOI: 10.1021/acs.chemrev.9b00055] [Citation(s) in RCA: 578] [Impact Index Per Article: 115.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU−ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200122, China
- Department of Chemistry, New York University, New York, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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5
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Garton M, Corbi-Verge C, Hu Y, Nim S, Tarasova N, Sherborne B, Kim PM. Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration. Proteins 2019; 87:236-244. [PMID: 30520126 DOI: 10.1002/prot.25644] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 10/30/2018] [Accepted: 11/29/2018] [Indexed: 11/07/2022]
Abstract
Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.
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Affiliation(s)
- Michael Garton
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Yuan Hu
- Merck & Co., Inc., Kenilworth, New Jersey.,Alkermes Inc., Waltham, Massachusetts
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Nadya Tarasova
- Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute-Frederick, Frederick, Maryland
| | | | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada.,Department of Computer Science, University of Toronto, Toronto, Canada
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6
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A computational approach for designing D-proteins with non-canonical amino acid optimised binding affinity. PLoS One 2017; 12:e0187524. [PMID: 29108013 PMCID: PMC5673230 DOI: 10.1371/journal.pone.0187524] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Accepted: 10/21/2017] [Indexed: 01/08/2023] Open
Abstract
Redesigning protein surface topology to improve target binding holds great promise in the search for highly selective therapeutics. While significant binding improvements can be achieved using natural amino acids, the introduction of non-canonical residues vastly increases sequence space and thus the chance to significantly out-compete native partners. The potency of protein inhibitors can be further enhanced by synthesising mirror image, D-amino versions. This renders them non-immunogenic and makes them highly resistant to proteolytic degradation. Current experimental design methods often preclude the use of D-amino acids and non-canonical amino acids for a variety of reasons. To address this, we build an in silico pipeline for D-protein designs featuring non-canonical amino acids. For a test scaffold we use an existing D-protein inhibitor of VEGF: D-RFX001. We benchmark the approach by recapitulating previous experimental optimisation with canonical amino acids. Subsequent incorporation of non-canonical amino acids allows designs that are predicted to improve binding affinity by up to -7.18 kcal/mol.
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Bhati AP, Wan S, Wright DW, Coveney PV. Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration. J Chem Theory Comput 2016; 13:210-222. [PMID: 27997169 DOI: 10.1021/acs.jctc.6b00979] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The accurate prediction of the binding affinities of ligands to proteins is a major goal in drug discovery and personalized medicine. The time taken to make such predictions is of similar importance to their accuracy, precision, and reliability. In the past few years, an ensemble based molecular dynamics approach has been proposed that provides a route to reliable predictions of free energies based on the molecular mechanics Poisson-Boltzmann surface area method which meets the requirements of speed, accuracy, precision, and reliability. Here, we describe an equivalent methodology based on thermodynamic integration to substantially improve the speed, accuracy, precision, and reliability of calculated relative binding free energies. We report the performance of the method when applied to a diverse set of protein targets and ligands. The results are in very good agreement with experimental data (90% of calculations agree to within 1 kcal/mol), while the method is reproducible by construction. Statistical uncertainties of the order of 0.5 kcal/mol or less are achieved. We present a systematic account of how the uncertainty in the predictions may be estimated.
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Affiliation(s)
- Agastya P Bhati
- Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom
| | - Shunzhou Wan
- Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom
| | - David W Wright
- Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom
| | - Peter V Coveney
- Centre for Computational Science, Department of Chemistry, University College London , 20 Gordon Street, London WC1H 0AJ, United Kingdom
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Maurer M, de Beer SBA, Oostenbrink C. Calculation of Relative Binding Free Energy in the Water-Filled Active Site of Oligopeptide-Binding Protein A. Molecules 2016; 21:499. [PMID: 27092480 PMCID: PMC5881882 DOI: 10.3390/molecules21040499] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 03/30/2016] [Accepted: 04/11/2016] [Indexed: 11/19/2022] Open
Abstract
The periplasmic oligopeptide binding protein A (OppA) represents a well-known example of water-mediated protein-ligand interactions. Here, we perform free-energy calculations for three different ligands binding to OppA, using a thermodynamic integration approach. The tripeptide ligands share a high structural similarity (all have the sequence KXK), but their experimentally-determined binding free energies differ remarkably. Thermodynamic cycles were constructed for the ligands, and simulations conducted in the bound and (freely solvated) unbound states. In the unbound state, it was observed that the difference in conformational freedom between alanine and glycine leads to a surprisingly slow convergence, despite their chemical similarity. This could be overcome by increasing the softness parameter during alchemical transformations. Discrepancies remained in the bound state however, when comparing independent simulations of the three ligands. These difficulties could be traced to a slow relaxation of the water network within the active site. Fluctuations in the number of water molecules residing in the binding cavity occur mostly on a timescale larger than the simulation time along the alchemical path. After extensive simulations, relative binding free energies that were converged to within thermal noise could be obtained, which agree well with available experimental data.
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Affiliation(s)
- Manuela Maurer
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, A-1190 Vienna, Austria.
| | - Stephanie B A de Beer
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, A-1190 Vienna, Austria.
| | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, A-1190 Vienna, Austria.
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