1
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Woo H, Kim Y, Seok C. Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling. PLoS Comput Biol 2024; 20:e1012239. [PMID: 38913733 PMCID: PMC11226077 DOI: 10.1371/journal.pcbi.1012239] [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: 07/05/2024] [Accepted: 06/07/2024] [Indexed: 06/26/2024] Open
Abstract
As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems.
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Affiliation(s)
- Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yubeen Kim
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
- Galux Inc. Seoul, Republic of Korea
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2
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Hou Y, Bai Y, Lu C, Wang Q, Wang Z, Gao J, Xu H. Applying molecular docking to pesticides. PEST MANAGEMENT SCIENCE 2023; 79:4140-4152. [PMID: 37547967 DOI: 10.1002/ps.7700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/17/2023] [Accepted: 08/05/2023] [Indexed: 08/08/2023]
Abstract
Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yang Hou
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuqian Bai
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Chang Lu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Qiuchan Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Zishi Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Jinsheng Gao
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Hongliang Xu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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3
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Kwon S, Seok C. CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking. Comput Struct Biotechnol J 2022; 21:1-10. [PMID: 36514334 PMCID: PMC9719078 DOI: 10.1016/j.csbj.2022.11.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 11/27/2022] Open
Abstract
Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.
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Affiliation(s)
- Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
- Galux Inc, Seoul 08738, South Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
- Galux Inc, Seoul 08738, South Korea
- Corresponding author.
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4
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In silico Structural and Functional Characterization of a Hypothetical Protein from Stenotrophomonas maltophilia SRM01. JOURNAL OF PURE AND APPLIED MICROBIOLOGY 2022. [DOI: 10.22207/jpam.16.2.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Stenotrophomonas maltophilia is a low-virulence opportunistic pathogen that causes human infections, especially in profound ill patients. Even if the bacterial genomes seem understood, the activities of many proteins are unknown. The purpose of our current research is to unravel the functional characteristics i.e. functional domain search and valuable regions of a hypothetical protein that would aid in the identification of potential drug targets in Stenotrophomonas maltophilia. The hypothetical protein of S.maltophilia was located and annotated using different in silico techniques. Our target protein was predicted to be Transcrip Reg superfamily YebC/PmpR based on motif and domain analysis by functional annotation tools. The regulator proteins of the YebC family are part of a vast collection of widely conserved hypothetical proteins with unclear functions. Examining and reviewing the function of YebC family protein, they repress Quorum sensing by directly binding to the promoter region of QS master regulator pqrS. It has also been reported that T3SS expression is regulated by YebC, to activate the virulence expression direct interaction with one of the T3SS promoters is needed.
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5
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Heggadadevanakote Kendaganna P, Shivamallu C, Shruthi G, Nagabushan Chitagudigi M, Pradeep S, Karunakar P, Raghavendra AG, Patil SS, Syed A, Elgorban AM, Bahkali AH, Veerapur R, Prasad Kollur S. In silico screening and validation of KPHS_00890 protein of Klebsiella pneumoniae proteome: An application to bacterial resistance and pathogenesis. JOURNAL OF KING SAUD UNIVERSITY - SCIENCE 2021; 33:101537. [DOI: 10.1016/j.jksus.2021.101537] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
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6
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Fluorescence Spectroscopic Analysis of ppGpp Binding to cAMP Receptor Protein and Histone-Like Nucleoid Structuring Protein. Int J Mol Sci 2021; 22:ijms22157871. [PMID: 34360641 PMCID: PMC8346002 DOI: 10.3390/ijms22157871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/02/2021] [Accepted: 07/20/2021] [Indexed: 11/17/2022] Open
Abstract
The cyclic AMP receptor protein (CRP) is one of the best-known transcription factors, regulating about 400 genes. The histone-like nucleoid structuring protein (H-NS) is one of the nucleoid-forming proteins and is responsible for DNA packaging and gene repression in prokaryotes. In this study, the binding of ppGpp to CRP and H-NS was determined by fluorescence spectroscopy. CRP from Escherichia coli exhibited intrinsic fluorescence at 341 nm when excited at 280 nm. The fluorescence intensity decreased in the presence of ppGpp. The dissociation constant of 35 ± 3 µM suggests that ppGpp binds to CRP with a similar affinity to cAMP. H-NS also shows intrinsic fluorescence at 329 nm. The fluorescence intensity was decreased by various ligands and the calculated dissociation constant for ppGpp was 80 ± 11 µM, which suggests that the binding site was occupied fully by ppGpp under starvation conditions. This study suggests the modulatory effects of ppGpp in gene expression regulated by CRP and H-NS. The method described here may be applicable to many other proteins.
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7
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Zhang W, Meng Q, Tang J, Guo F. Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset. Brief Bioinform 2021; 22:6265196. [PMID: 33959764 DOI: 10.1093/bib/bbab150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/27/2021] [Indexed: 12/27/2022] Open
Abstract
Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein-protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained $19$ protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein-protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein-protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of $6.144$ minutes, average Fnat of best pose of $0.953$ and average rank of best pose of $4.158$. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods.
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Affiliation(s)
- Wei Zhang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Qiaozhen Meng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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8
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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9
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Hoang HN, Tran TT, Jung C. The Activation of Glycerol Dehydrogenase fromEscherichia coliby ppGpp. B KOREAN CHEM SOC 2019. [DOI: 10.1002/bkcs.11932] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Huyen Nga Hoang
- Department of Molecular MedicineChonnam National University, Graduate school Gwangju South Korea
| | - Thanh Tuyen Tran
- Department of Molecular MedicineChonnam National University, Graduate school Gwangju South Korea
| | - Che‐Hun Jung
- Department of Molecular MedicineChonnam National University, Graduate school Gwangju South Korea
- Department of ChemistryChonnam National University, Graduate school Gwangju South Korea
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10
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Comparison of the umbrella sampling and the double decoupling method in binding free energy predictions for SAMPL6 octa-acid host-guest challenges. J Comput Aided Mol Des 2018; 32:1075-1086. [PMID: 30324304 DOI: 10.1007/s10822-018-0166-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 09/21/2018] [Indexed: 12/14/2022]
Abstract
We calculate the absolute binding free energies of tetra-methylated octa-acids host-guest systems as a part of the SAMPL6 blind challenge (receipt ID vq30p). We employed two different free energy simulation methods, i.e., the umbrella sampling (US) and double decoupling method (DDM). The US method was used with the weighted histogram analysis method (WHAM) (US-WHAM scheme). In the DDM scheme, Hamiltonian replica-exchange method (HREM) was combined with the Bennett acceptance ratio (BAR) (HREM-BAR scheme). We obtained initial binding poses via molecular docking using GalaxyDock-HG program, which is developed for the SAMPL challenge. The root mean square deviation (RMSD) and the mean absolute deviations (MAD) using US-WHAM scheme were 1.33 and 1.02 kcal/mol, respectively. The MAD was the top among all submissions, however the correlation with respect to experiment was unexceptional. While the RMSD and MAD via HREM-BAR scheme were greater than US-WHAM scheme, (i.e., 2.09 and 1.76 kcal/mol), their correlations were slightly better than US-WHAM. The correlation between the two methods was high. Further discussion on the DDM method can be found in a companion paper by Han et al. (receipt ID 3z83m) in the same issue.
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11
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Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates. Structure 2018; 26:565-571.e3. [PMID: 29551288 PMCID: PMC5890617 DOI: 10.1016/j.str.2018.02.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 01/26/2018] [Accepted: 02/09/2018] [Indexed: 11/22/2022]
Abstract
There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures. We present PARITY, matching atoms in identical topology to gauge ligand similarity Bound-cognate ligand similarity is a useful metric for ranking PDB structures Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7 We provide rank-ordered lists of PDBs with the most biologically relevant ligands
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12
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Inverse Resolution Limit of Partition Density and Detecting Overlapping Communities by Link-Surprise. Sci Rep 2017; 7:12399. [PMID: 28963540 PMCID: PMC5622083 DOI: 10.1038/s41598-017-12432-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 09/04/2017] [Indexed: 11/16/2022] Open
Abstract
Finding overlapping communities of complex networks remains a challenge in network science. To address this challenge, one of the widely used approaches is finding the communities of links by optimizing the objective function, partition density. In this study, we show that partition density suffers from inverse resolution limit; it has a strong preference to triangles. This resolution limit makes partition density an improper objective function for global optimization. The conditions where partition density prefers triangles to larger link community structures are analytically derived and confirmed with global optimization calculations using synthetic and real-world networks. To overcome this limitation of partition density, we suggest an alternative measure, Link Surprise, to find link communities, which is suitable for global optimization. Benchmark studies demonstrate that global optimization of Link Surprise yields meaningful and more accurate link community structures than partition density optimization.
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13
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GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking. J Comput Aided Mol Des 2017. [DOI: 10.1007/s10822-017-0030-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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14
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Heo S, Lee J, Joo K, Shin HC, Lee J. Protein Loop Structure Prediction Using Conformational Space Annealing. J Chem Inf Model 2017; 57:1068-1078. [DOI: 10.1021/acs.jcim.6b00742] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Seungryong Heo
- School
of Systems Biomedical Science, Soongsil University, Seoul 06978, Korea
| | - Juyong Lee
- Laboratory
of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | | | - Hang-Cheol Shin
- School
of Systems Biomedical Science, Soongsil University, Seoul 06978, Korea
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15
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Das A, Bhattacharya S. Different Types of Molecular Docking Based on Variations of Interacting Molecules. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.
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16
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Tofoleanu F, Lee J, Pickard Iv FC, König G, Huang J, Baek M, Seok C, Brooks BR. Absolute binding free energies for octa-acids and guests in SAMPL5 : Evaluating binding free energies for octa-acid and guest complexes in the SAMPL5 blind challenge. J Comput Aided Mol Des 2017; 31:107-118. [PMID: 27696242 PMCID: PMC6472255 DOI: 10.1007/s10822-016-9965-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 09/02/2016] [Indexed: 02/07/2023]
Abstract
As part of the SAMPL5 blind prediction challenge, we calculate the absolute binding free energies of six guest molecules to an octa-acid (OAH) and to a methylated octa-acid (OAMe). We use the double decoupling method via thermodynamic integration (TI) or Hamiltonian replica exchange in connection with the Bennett acceptance ratio (HREM-BAR). We produce the binding poses either through manual docking or by using GalaxyDock-HG, a docking software developed specifically for this study. The root mean square deviations for our most accurate predictions are 1.4 kcal mol-1 for OAH with TI and 1.9 kcal mol-1 for OAMe with HREM-BAR. Our best results for OAMe were obtained for systems with ionic concentrations corresponding to the ionic strength of the experimental solution. The most problematic system contains a halogenated guest. Our attempt to model the σ-hole of the bromine using a constrained off-site point charge, does not improve results. We use results from molecular dynamics simulations to argue that the distinct binding affinities of this guest to OAH and OAMe are due to a difference in the flexibility of the host. We believe that the results of this extensive analysis of host-guest complexes will help improve the protocol used in predicting binding affinities for larger systems, such as protein-substrate compounds.
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Affiliation(s)
- Florentina Tofoleanu
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung, and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA.
| | - Juyong Lee
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung, and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA
| | - Frank C Pickard Iv
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung, and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA
| | - Gerhard König
- Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr, Germany
| | - Jing Huang
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung, and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA
- Department of Pharmaceutical Science, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Institutes of Health - National Heart, Lung, and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA
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17
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Skariyachan S. Exploring the Potential of Herbal Ligands Toward Multidrug-Resistant Bacterial Pathogens by Computational Drug Discovery. TRANSLATIONAL BIOINFORMATICS AND ITS APPLICATION 2017. [DOI: 10.1007/978-94-024-1045-7_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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18
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Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge. J Comput Aided Mol Des 2016; 31:71-85. [PMID: 27677749 DOI: 10.1007/s10822-016-9968-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/08/2016] [Indexed: 12/11/2022]
Abstract
Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett's acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.
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19
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Grebner C, Iegre J, Ulander J, Edman K, Hogner A, Tyrchan C. Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design. J Chem Inf Model 2016; 56:774-87. [DOI: 10.1021/acs.jcim.5b00744] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Christoph Grebner
- CVMD Innovative Medicine, ‡RIA Innovative Medicine, and §Discovery Science, AstraZeneca R&D, 43283 Mölndal, Sweden
| | - Jessica Iegre
- CVMD Innovative Medicine, ‡RIA Innovative Medicine, and §Discovery Science, AstraZeneca R&D, 43283 Mölndal, Sweden
| | - Johan Ulander
- CVMD Innovative Medicine, ‡RIA Innovative Medicine, and §Discovery Science, AstraZeneca R&D, 43283 Mölndal, Sweden
| | - Karl Edman
- CVMD Innovative Medicine, ‡RIA Innovative Medicine, and §Discovery Science, AstraZeneca R&D, 43283 Mölndal, Sweden
| | - Anders Hogner
- CVMD Innovative Medicine, ‡RIA Innovative Medicine, and §Discovery Science, AstraZeneca R&D, 43283 Mölndal, Sweden
| | - Christian Tyrchan
- CVMD Innovative Medicine, ‡RIA Innovative Medicine, and §Discovery Science, AstraZeneca R&D, 43283 Mölndal, Sweden
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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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21
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Das A, Bhattacharya S. Different Types of Molecular Docking Based on Variations of Interacting Molecules. METHODS AND ALGORITHMS FOR MOLECULAR DOCKING-BASED DRUG DESIGN AND DISCOVERY 2016. [DOI: 10.4018/978-1-5225-0115-2.ch006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.
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22
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Shin WH, Lee GR, Seok C. Evaluation of GalaxyDock Based on the Community Structure-Activity Resource 2013 and 2014 Benchmark Studies. J Chem Inf Model 2015; 56:988-95. [PMID: 26583962 DOI: 10.1021/acs.jcim.5b00309] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We analyze the results of the GalaxyDock protein-ligand docking program in the two recent experiments of Community Structure-Activity Resource (CSAR), CSAR 2013 and 2014. GalaxyDock performs global optimization of a modified AutoDock3 energy function by employing the conformational space annealing method. The energy function of GalaxyDock is quite sensitive to atomic clashes. Such energy functions can be effective for sampling physically correct conformations but may not be effective for scoring when conformations are not fully optimized. In phase 1 of CSAR 2013, we successfully selected all four true binders of digoxigenin along with three false positives. However, the energy values were rather high due to insufficient optimization of the conformations docked to homology models. A posteriori relaxation of the model complex structures by GalaxyRefine improved the docking energy values and differentiated the true binders from the false positives better. In the scoring test of CSAR 2013 phase 2, we selected the best poses for each of the two targets. The results of CSAR 2013 phase 3 suggested that an improved method for generating initial conformations for GalaxyDock is necessary for targets involving bulky ligands. Finally, combining existing binding information with GalaxyDock energy-based optimization may be needed for more accurate binding affinity prediction.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry, Seoul National University , Seoul 151-747, Republic of Korea
| | - Gyu Rie 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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23
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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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24
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Abstract
Enzymes are one of the most important groups of drug targets, and identifying possible ligand-enzyme interactions is of major importance in many drug discovery processes. Novel computational methods have been developed that can apply the information from the increasing number of resolved and available ligand-enzyme complexes to model new unknown interactions and therefore contribute to answer open questions in the field of drug discovery like the identification of unknown protein functions, off-target binding, ligand 3D homology modeling and induced-fit simulations.
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25
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A comparison of various optimization algorithms of protein–ligand docking programs by fitness accuracy. J Mol Model 2014; 20:2251. [DOI: 10.1007/s00894-014-2251-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2014] [Accepted: 04/10/2014] [Indexed: 10/25/2022]
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26
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Heo L, Shin WH, Lee MS, Seok C. GalaxySite: ligand-binding-site prediction by using molecular docking. Nucleic Acids Res 2014; 42:W210-4. [PMID: 24753427 PMCID: PMC4086128 DOI: 10.1093/nar/gku321] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Knowledge of ligand-binding sites of proteins provides invaluable information for
functional studies, drug design and protein design. Recent progress in
ligand-binding-site prediction methods has demonstrated that using information
from similar proteins of known structures can improve predictions. The
GalaxySite web server, freely accessible at http://galaxy.seoklab.org/site, combines such information with
molecular docking for more precise binding-site prediction for non-metal
ligands. According to the recent critical assessments of structure prediction
methods held in 2010 and 2012, this server was found to be superior or
comparable to other state-of-the-art programs in the category of
ligand-binding-site prediction. A strong merit of the GalaxySite program is that
it provides additional predictions on binding ligands and their binding poses in
terms of the optimized 3D coordinates of the protein–ligand complexes,
whereas other methods predict only identities of binding-site residues or copy
binding geometry from similar proteins. The additional information on the
specific binding geometry would be very useful for applications in functional
studies and computer-aided drug discovery.
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Affiliation(s)
- Lim Heo
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Woong-Hee Shin
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Myeong Sup Lee
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul 138-736, Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
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27
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Shin WH, Kim JK, Kim DS, Seok C. GalaxyDock2: Protein-ligand docking using beta-complex and global optimization. J Comput Chem 2013; 34:2647-56. [DOI: 10.1002/jcc.23438] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Revised: 07/20/2013] [Accepted: 08/18/2013] [Indexed: 11/10/2022]
Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry; Seoul National University; Seoul 151-747 Republic of Korea
| | - Jae-Kwan Kim
- Department of Industrial Engineering; Hanyang University; Seoul 133-791 Republic of Korea
| | - Deok-Soo Kim
- Department of Industrial Engineering; Hanyang University; Seoul 133-791 Republic of Korea
| | - Chaok Seok
- Department of Chemistry; Seoul National University; Seoul 151-747 Republic of Korea
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28
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Kim MS, Lee H, Heo L, Lim A, Seok C, Shin DH. New molecular interaction of IIANtr
and HPr from Burkholderia pseudomallei
identified by X-ray crystallography and docking studies. Proteins 2013; 81:1499-508. [DOI: 10.1002/prot.24275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Revised: 01/24/2013] [Accepted: 02/18/2013] [Indexed: 11/11/2022]
Affiliation(s)
- Mi-Sun Kim
- Division of Life & Pharmaceutical Sciences; The Center for Cell Signaling & Drug Discovery Research; College of Pharmacy, Ewha Womans University; Seoul 120-750 Republic of Korea
| | - Hasup Lee
- Department of Chemistry; College of Natural Sciences; Seoul National University; Seoul 151-747 Republic of Korea
| | - Lim Heo
- Department of Chemistry; College of Natural Sciences; Seoul National University; Seoul 151-747 Republic of Korea
| | - Areum Lim
- Division of Life & Pharmaceutical Sciences; The Center for Cell Signaling & Drug Discovery Research; College of Pharmacy, Ewha Womans University; Seoul 120-750 Republic of Korea
| | - Chaok Seok
- Department of Chemistry; College of Natural Sciences; Seoul National University; Seoul 151-747 Republic of Korea
| | - Dong Hae Shin
- Division of Life & Pharmaceutical Sciences; The Center for Cell Signaling & Drug Discovery Research; College of Pharmacy, Ewha Womans University; Seoul 120-750 Republic of Korea
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29
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Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit 2013; 26:215-39. [PMID: 23526775 DOI: 10.1002/jmr.2266] [Citation(s) in RCA: 193] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2012] [Revised: 01/16/2013] [Accepted: 01/19/2013] [Indexed: 12/28/2022]
Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences; Monash University; Parkville; VIC; 3052; Australia
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Abstract
An important issue in developing protein-ligand docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain docking. This method was tested on 3 sets of protein-ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible docking methods tested on the same sets, increasing the binding conformation prediction accuracy by 10%-60% compared to rigid-receptor docking. This encouraging result suggests that this powerful global energy optimization method may be further extended to incorporate larger magnitudes of receptor flexibility in the future. The program is available at http://galaxy.seoklab.org/softwares/galaxydock.html .
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry, Seoul National University, Seoul 151-747, Republic of Korea
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31
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Ko J, Park H, Seok C. GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions. BMC Bioinformatics 2012; 13:198. [PMID: 22883815 PMCID: PMC3462707 DOI: 10.1186/1471-2105-13-198] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2012] [Accepted: 08/07/2012] [Indexed: 01/05/2023] Open
Abstract
Background Protein structures can be reliably predicted by template-based modeling (TBM) when experimental structures of homologous proteins are available. However, it is challenging to obtain structures more accurate than the single best templates by either combining information from multiple templates or by modeling regions that vary among templates or are not covered by any templates. Results We introduce GalaxyTBM, a new TBM method in which the more reliable core region is modeled first from multiple templates and less reliable, variable local regions, such as loops or termini, are then detected and re-modeled by an ab initio method. This TBM method is based on “Seok-server,” which was tested in CASP9 and assessed to be amongst the top TBM servers. The accuracy of the initial core modeling is enhanced by focusing on more conserved regions in the multiple-template selection and multiple sequence alignment stages. Additional improvement is achieved by ab initio modeling of up to 3 unreliable local regions in the fixed framework of the core structure. Overall, GalaxyTBM reproduced the performance of Seok-server, with GalaxyTBM and Seok-server resulting in average GDT-TS of 68.1 and 68.4, respectively, when tested on 68 single-domain CASP9 TBM targets. For application to multi-domain proteins, GalaxyTBM must be combined with domain-splitting methods. Conclusion Application of GalaxyTBM to CASP9 targets demonstrates that accurate protein structure prediction is possible by use of a multiple-template-based approach, and ab initio modeling of variable regions can further enhance the model quality.
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Affiliation(s)
- Junsu Ko
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea.
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32
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Kantardjiev AA. Quantum.Ligand.Dock: protein-ligand docking with quantum entanglement refinement on a GPU system. Nucleic Acids Res 2012; 40:W415-22. [PMID: 22669908 PMCID: PMC3394274 DOI: 10.1093/nar/gks515] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Quantum.Ligand.Dock (protein-ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein-ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments-the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein-ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.
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Affiliation(s)
- Alexander A Kantardjiev
- Biophysical Chemistry Group, Institute of Organic Chemistry, Bulgarian Academy of Sciences, Sofia 1113, Bulgaria.
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