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Joo K, Joung I, Cheng Q, Lee SJ, Lee J. Contact-assisted protein structure modeling by global optimization in CASP11. Proteins 2016; 84 Suppl 1:189-99. [DOI: 10.1002/prot.24975] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 11/24/2015] [Accepted: 12/12/2015] [Indexed: 11/09/2022]
Affiliation(s)
- Keehyoung Joo
- Center for in Silico Protein Science, Korea Institute for Advanced Study; Seoul 130-722 Korea
- Center for Advanced Computation, Korea Institute for Advanced Study; Seoul 130-722 Korea
| | - InSuk Joung
- Center for in Silico Protein Science, Korea Institute for Advanced Study; Seoul 130-722 Korea
- School of Computational Sciences, Korea Institute for Advanced Study; Seoul 130-722 Korea
| | - Qianyi Cheng
- Center for in Silico Protein Science, Korea Institute for Advanced Study; Seoul 130-722 Korea
- School of Computational Sciences, Korea Institute for Advanced Study; Seoul 130-722 Korea
| | - Sung Jong Lee
- Center for in Silico Protein Science, Korea Institute for Advanced Study; Seoul 130-722 Korea
- Department of Physics; University of Suwon; Hwaseong-Si Gyeonggi-do 445-743 Korea
| | - Jooyoung Lee
- Center for in Silico Protein Science, Korea Institute for Advanced Study; Seoul 130-722 Korea
- Center for Advanced Computation, Korea Institute for Advanced Study; Seoul 130-722 Korea
- School of Computational Sciences, Korea Institute for Advanced Study; Seoul 130-722 Korea
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Joo K, Joung I, Lee J, Lee J, Lee W, Brooks B, Lee SJ, Lee J. Protein structure determination by conformational space annealing using NMR geometric restraints. Proteins 2015; 83:2251-62. [PMID: 26454251 DOI: 10.1002/prot.24941] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 09/19/2015] [Accepted: 09/22/2015] [Indexed: 11/06/2022]
Abstract
We have carried out numerical experiments to investigate the applicability of the global optimization method of conformational space annealing (CSA) to the enhanced NMR protein structure determination over existing PDB structures. The NMR protein structure determination is driven by the optimization of collective multiple restraints arising from experimental data and the basic stereochemical properties of a protein-like molecule. By rigorous and straightforward application of CSA to the identical NMR experimental data used to generate existing PDB structures, we redetermined 56 recent PDB protein structures starting from fully randomized structures. The quality of CSA-generated structures and existing PDB structures were assessed by multiobjective functions in terms of their consistencies with experimental data and the requirements of protein-like stereochemistry. In 54 out of 56 cases, CSA-generated structures were better than existing PDB structures in the Pareto-dominant manner, while in the remaining two cases, it was a tie with mixed results. As a whole, all structural features tested improved in a statistically meaningful manner. The most improved feature was the Ramachandran favored portion of backbone torsion angles with about 8.6% improvement from 88.9% to 97.5% (P-value <10(-17)). We show that by straightforward application of CSA to the efficient global optimization of an energy function, NMR structures will be of better quality than existing PDB structures.
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Affiliation(s)
- Keehyoung Joo
- Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - InSuk Joung
- Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jinhyuk Lee
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea.,Department of Nanobiotechnology and Bioinformatics, University of Sciences and Technology, Daejeon, 305-350, Korea
| | - Jinwoo Lee
- Department of Mathematics, Kwangwoon University, Nowon-Gu, Seoul, 139-701, Korea
| | - Weontae Lee
- Department of Biochemistry, Yonsei University, Seodaemun-Gu, Seoul, 120-749, Korea
| | - Bernard Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, 20852
| | - Sung Jong Lee
- Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.,Department of Physics, University of Suwon, Hwaseong-Si, Gyeonggi-Do, 445-743, Korea
| | - Jooyoung Lee
- Center for in Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 130-722, Korea
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Park H, Lee H, Seok C. High-resolution protein-protein docking by global optimization: recent advances and future challenges. Curr Opin Struct Biol 2015; 35:24-31. [PMID: 26295792 DOI: 10.1016/j.sbi.2015.08.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 07/13/2015] [Accepted: 08/03/2015] [Indexed: 01/12/2023]
Abstract
A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.
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Affiliation(s)
- Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 151-747, Republic of Korea.
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Shin WH, Heo L, Lee J, Ko J, Seok C, Lee J. LigDockCSA: Protein-ligand docking using conformational space annealing. J Comput Chem 2011; 32:3226-32. [DOI: 10.1002/jcc.21905] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2010] [Revised: 06/17/2011] [Accepted: 07/06/2011] [Indexed: 11/12/2022]
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Kim DS, Kim CM, Won CI, Kim JK, Ryu J, Cho Y, Lee C, Bhak J. BetaDock: Shape-Priority Docking Method Based on Beta-Complex. J Biomol Struct Dyn 2011; 29:219-42. [DOI: 10.1080/07391102.2011.10507384] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Lee J, Lee J, Sasaki TN, Sasai M, Seok C, Lee J. De novo
protein structure prediction by dynamic fragment assembly and conformational space annealing. Proteins 2011; 79:2403-17. [DOI: 10.1002/prot.23059] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2010] [Revised: 03/24/2011] [Accepted: 04/12/2011] [Indexed: 12/25/2022]
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Joo K, Lee J, Seo JH, Lee K, Kim BG, Lee J. All-atom chain-building by optimizing MODELLER energy function using conformational space annealing. Proteins 2009; 75:1010-23. [PMID: 19089941 DOI: 10.1002/prot.22312] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We have investigated the effect of rigorous optimization of the MODELLER energy function for possible improvement in protein all-atom chain-building. For this we applied the global optimization method called conformational space annealing (CSA) to the standard MODELLER procedure to achieve better energy optimization than what MODELLER provides. The method, which we call MODELLERCSA, is tested on two benchmark sets. The first is the 298 proteins taken from the HOMSTRAD multiple alignment set. By simply optimizing the MODELLER energy function, we observe significant improvement in side-chain modeling, where MODELLERCSA provides about 10.7% (14.5%) improvement for chi(1) (chi(1) + chi(2)) accuracy compared to the standard MODELLER modeling. The improvement of backbone accuracy by MODELLERCSA is shown to be less prominent, and a similar improvement can be achieved by simply generating many standard MODELLER models and selecting lowest energy models. However, the level of side-chain modeling accuracy by MODELLERCSA could not be matched either by extensive MODELLER strategies, side-chain remodeling by SCWRL3, or copying unmutated rotamers. The identical procedure was successfully applied to 100 CASP7 template base modeling domains during the prediction season in a blind fashion, and the results are included here for comparison. From this study, we observe a good correlation between the MODELLER energy and the side-chain accuracy. Our findings indicate that, when a good alignment between a target protein and its templates is provided, thorough optimization of the MODELLER energy function leads to accurate all-atom models.
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Affiliation(s)
- Keehyoung Joo
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea
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Computational study for protein-protein docking using global optimization and empirical potentials. Int J Mol Sci 2008; 9:65-77. [PMID: 19325720 PMCID: PMC2635596 DOI: 10.3390/ijms9010065] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2007] [Accepted: 01/15/2008] [Indexed: 11/17/2022] Open
Abstract
Protein-protein interactions are important for biochemical processes in biological systems. The 3D structure of the macromolecular complex resulting from the protein-protein association is a very useful source to understand its specific functions. This work focuses on computational study for protein-protein docking, where the individually crystallized structures of interacting proteins are treated as rigid, and the conformational space generated by the two interacting proteins is explored extensively. The energy function consists of intermolecular electrostatic potential, desolvation free energy represented by empirical contact potential, and simple repulsive energy terms. The conformational space is six dimensional, represented by translational vectors and rotational angles formed between two interacting proteins. The conformational sampling is carried out by the search algorithms such as simulated annealing (SA), conformational space annealing (CSA), and CSA combined with SA simulations (combined CSA/SA). Benchmark tests are performed on a set of 18 protein-protein complexes selected from various protein families to examine feasibility of these search methods coupled with the energy function above for protein docking study.
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Bernauer J, Azé J, Janin J, Poupon A. A new protein-protein docking scoring function based on interface residue properties. Bioinformatics 2007; 23:555-62. [PMID: 17237048 DOI: 10.1093/bioinformatics/btl654] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein-protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low stability and difficulty to produce the proteins and assemble them in native conformation. Thus, docking algorithms have been developed to provide an in silico approach of the problem. A protein-protein docking procedure traditionally consists of two successive tasks: a search algorithm generates a large number of candidate solutions, and then a scoring function is used to rank them. RESULTS To address the second step, we developed a scoring function based on a Voronoï tessellation of the protein three-dimensional structure. We showed that the Voronoï representation may be used to describe in a simplified but useful manner, the geometric and physico-chemical complementarities of two molecular surfaces. We measured a set of parameters on native protein-protein complexes and on decoys, and used them as attributes in several statistical learning procedures: a logistic function, Support Vector Machines (SVM), and a genetic algorithm. For the later, we used ROGER, a genetic algorithm designed to optimize the area under the receiver operating characteristics curve. To further test the scores derived with ROGER, we ranked models generated by two different docking algorithms on targets of a blind prediction experiment, improving in almost all cases the rank of native-like solutions. AVAILABILITY http://genomics.eu.org/spip/-Bioinformatics-tools-
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Affiliation(s)
- J Bernauer
- Yeast Structural Genomics, IBBMC UMR CNRS 8619, Bâtiment 430, Université Paris-Sud, 91405 Orsay, France
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