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Zhang J, Li H, Zhao X, Wu Q, Huang SY. Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement. J Chem Inf Model 2022; 62:5806-5820. [PMID: 36342197 DOI: 10.1021/acs.jcim.2c00895] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.
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Affiliation(s)
- Jinze Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Xuejun Zhao
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
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2
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Kapla J, Rodríguez-Espigares I, Ballante F, Selent J, Carlsson J. Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models? PLoS Comput Biol 2021; 17:e1008936. [PMID: 33983933 PMCID: PMC8186765 DOI: 10.1371/journal.pcbi.1008936] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 06/08/2021] [Accepted: 04/02/2021] [Indexed: 01/14/2023] Open
Abstract
The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.
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Affiliation(s)
- Jon Kapla
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Ismael Rodríguez-Espigares
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Flavio Ballante
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Jana Selent
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences of Pompeu Fabra University (UPF), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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Guterres H, Lee HS, Im W. Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates. J Chem Theory Comput 2019; 15:6524-6535. [PMID: 31557013 DOI: 10.1021/acs.jctc.9b00751] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.
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Affiliation(s)
- Hugo Guterres
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Hui Sun Lee
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States
| | - Wonpil Im
- Department of Biological Sciences , Lehigh University , Bethlehem , Pennsylvania 18015 , United States.,School of Computational Sciences , Korea Institute for Advanced Study , Seoul 02455 , Republic of Korea
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Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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5
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Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
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Dawid AE, Gront D, Kolinski A. Coarse-Grained Modeling of the Interplay between Secondary Structure Propensities and Protein Fold Assembly. J Chem Theory Comput 2018; 14:2277-2287. [PMID: 29486120 DOI: 10.1021/acs.jctc.7b01242] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We recently developed a new coarse-grained model of protein structure and dynamics [ Dawid et al. J. Chem. Theory Comput. 2017 , 13 ( 11 ), 5766 - 5779 ]. The model assumed a single bead representation of amino acid residues, where positions of such united residues were defined by centers of mass of four amino acid fragments. Replica exchange Monte Carlo sampling of the model chains provided good pictures of modeled structures and their dynamics. In its generic form the statistical knowledge-based force field of the model has been dedicated for single-domain globular proteins. Sequence-specific interactions are defined by three-letter secondary structure data. In the present work we demonstrate that different assignments and/or predictions of secondary structures are usually sufficient for enforcing cooperative formation of native-like folds of SURPASS chains for the majority of single-domain globular proteins. Simulations of native-like structure assembly for a representative set of globular proteins have shown that the accuracy of secondary structure data is usually not crucial for model performance, although some specific errors can strongly distort the obtained three-dimensional structures.
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Affiliation(s)
- Aleksandra E Dawid
- Faculty of Chemistry, Biological and Chemical Research Center , University of Warsaw , Pasteura 1 , 02-093 Warsaw , Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center , University of Warsaw , Pasteura 1 , 02-093 Warsaw , Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center , University of Warsaw , Pasteura 1 , 02-093 Warsaw , Poland
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7
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Hong SH, Joung I, Flores-Canales JC, Manavalan B, Cheng Q, Heo S, Kim JY, Lee SY, Nam M, Joo K, Lee IH, Lee SJ, Lee J. Protein structure modeling and refinement by global optimization in CASP12. Proteins 2017; 86 Suppl 1:122-135. [PMID: 29159837 DOI: 10.1002/prot.25426] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 11/10/2017] [Accepted: 11/16/2017] [Indexed: 11/09/2022]
Abstract
For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded.
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Affiliation(s)
- Seung Hwan Hong
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | - InSuk Joung
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | - Jose C Flores-Canales
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | - Balachandran Manavalan
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | - Qianyi Cheng
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | - Seungryong Heo
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea
| | - Jong Yun Kim
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea
| | - Sun Young Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea
| | - Mikyung Nam
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea
| | - Keehyoung Joo
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, South Korea
| | - In-Ho Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,Korea Research Institute of Standards and Science (KRISS), Daejeon, South Korea
| | - Sung Jong Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,The Research Institute for Basic Sciences, Changwon National University, Changwon-Si, Gyeongsangnam-do, South Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, South Korea
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8
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Hovan L, Oleinikovas V, Yalinca H, Kryshtafovych A, Saladino G, Gervasio FL. Assessment of the model refinement category in CASP12. Proteins 2017; 86 Suppl 1:152-167. [PMID: 29071750 DOI: 10.1002/prot.25409] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/03/2017] [Accepted: 10/24/2017] [Indexed: 01/07/2023]
Abstract
We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field.
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Affiliation(s)
- Ladislav Hovan
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | | | - Havva Yalinca
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | | | - Giorgio Saladino
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, United Kingdom
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