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Wang T, Zhang X, Zhang O, Chen G, Pan P, Wang E, Wang J, Wu J, Zhou D, Wang L, Jin R, Chen S, Shen C, Kang Y, Hsieh CY, Hou T. Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop. RESEARCH (WASHINGTON, D.C.) 2024; 7:0408. [PMID: 39055686 PMCID: PMC11268956 DOI: 10.34133/research.0408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 05/22/2024] [Indexed: 07/27/2024]
Abstract
Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSDs of 1.77 and 1.95 Å for the CASP13+14 and CASP15 benchmark datasets, respectively, and manifests at least 2 orders of magnitude speedup in general compared with other methods. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.
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Affiliation(s)
- Tianyue Wang
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | | | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ercheng Wang
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Donghao Zhou
- Shenzhen Institute of Advanced Technology,
Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Langcheng Wang
- Department of Pathology,
New York University Medical Center, New York, NY 10016, USA
| | - Ruofan Jin
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- College of Life Sciences,
Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Shicheng Chen
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine ofZhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Liang S, Zhang C, Zhu M. Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling. J Chem Inf Model 2023; 63:7568-7577. [PMID: 38018130 DOI: 10.1021/acs.jcim.3c01051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.
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Affiliation(s)
- Shide Liang
- Department of Computational Biology, 20n Bio Limited, Hangzhou 310018, P. R. China
- Department of Research and Development, Bio-Thera Solutions, Guangzhou 510530, P. R. China
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, Nebraska 68588, United States
| | - Mingfu Zhu
- Department of Computational Biology, 20n Bio Limited, Hangzhou 310018, P. R. China
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3
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Wang T, Wang L, Zhang X, Shen C, Zhang O, Wang J, Wu J, Jin R, Zhou D, Chen S, Liu L, Wang X, Hsieh CY, Chen G, Pan P, Kang Y, Hou T. Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency. Brief Bioinform 2023; 25:bbad486. [PMID: 38171930 PMCID: PMC10764206 DOI: 10.1093/bib/bbad486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.
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Affiliation(s)
- Tianyue Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Langcheng Wang
- Department of Pathology, New York University Medical Center, 550 First Avenue, New York, NY 10016, USA
| | - Xujun Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chao Shen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Odin Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jialu Wu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Ruofan Jin
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Donghao Zhou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Shicheng Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen 518129, Guangdong, China
| | - Xiaorui Wang
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao, China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Guangyong Chen
- Zhejiang Lab, Zhejiang University, Hangzhou 311121, Zhejiang, China
| | - Peichen Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Shirai T, Nakai A, Ando E, Fujimoto J, Leach S, Arimori T, Higo D, van Eerden FJ, Tulyeu J, Liu YC, Okuzaki D, Murayama MA, Miyata H, Nunomura K, Lin B, Tani A, Kumanogoh A, Ikawa M, Wing JB, Standley DM, Takagi J, Suzuki K. Celastrol suppresses humoral immune responses and autoimmunity by targeting the COMMD3/8 complex. Sci Immunol 2023; 8:eadc9324. [PMID: 37000855 DOI: 10.1126/sciimmunol.adc9324] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Celastrol, a bioactive molecule extracted from the
Tripterygium wilfordii
plant, has been shown to exhibit anti-inflammatory properties. However, its mechanism of action has not been fully elucidated. Here, we show that celastrol suppresses humoral immune responses and autoimmunity by disabling a protein complex consisting of copper metabolism MURR1 domain–containing (COMMD) 3 and COMMD8 (COMMD3/8 complex), a signaling adaptor for chemoattractant receptors. Having demonstrated the involvement of the COMMD3/8 complex in a mouse model of rheumatoid arthritis, we identified celastrol as a compound that covalently bound to and dissociated the COMMD3/8 complex. Celastrol inhibited B cell migration, reduced antibody responses, and blocked arthritis progression, recapitulating deficiency of the COMMD3/8 complex. These effects of celastrol were abolished in mice expressing a celastrol-resistant mutant of the COMMD3/8 complex. These findings establish that celastrol exerts immunosuppressive activity by targeting the COMMD3/8 complex. Our study suggests that the COMMD3/8 complex is a potentially druggable target in autoimmune diseases and points to celastrol as a lead pharmacologic candidate in this capacity.
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Affiliation(s)
- Taiichiro Shirai
- Laboratory of Immune Response Dynamics, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Akiko Nakai
- Laboratory of Immune Response Dynamics, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Department of Immune Response Dynamics, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - Emiko Ando
- Laboratory of Immune Response Dynamics, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
| | - Jun Fujimoto
- Laboratory of Immune Response Dynamics, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Sarah Leach
- Laboratory of Immune Response Dynamics, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
| | - Takao Arimori
- Laboratory for Protein Synthesis and Expression, Institute for Protein Research, Osaka University, Suita, Osaka, Japan
| | - Daisuke Higo
- Thermo Fisher Scientific K.K., Yokohama, Kanagawa, Japan
| | - Floris J. van Eerden
- Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - Janyerkye Tulyeu
- Laboratory of Human Single Cell Immunology, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
| | - Yu-Chen Liu
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
| | - Daisuke Okuzaki
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
- Laboratory of Human Immunology (Single Cell Genomics), WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - Masanori A. Murayama
- Department of Animal Models for Human Diseases, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka, Japan
| | - Haruhiko Miyata
- Department of Experimental Genome Research, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - Kazuto Nunomura
- Center for Supporting Drug Discovery and Life Science Research, Graduate School of Pharmaceutical Science, Osaka University, Suita, Osaka, Japan
| | - Bangzhong Lin
- Center for Supporting Drug Discovery and Life Science Research, Graduate School of Pharmaceutical Science, Osaka University, Suita, Osaka, Japan
| | - Akiyoshi Tani
- Center for Supporting Drug Discovery and Life Science Research, Graduate School of Pharmaceutical Science, Osaka University, Suita, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
- Laboratory of Immunopathology, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka, Japan
| | - Masahito Ikawa
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
- Department of Experimental Genome Research, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
| | - James B. Wing
- Laboratory of Human Single Cell Immunology, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
| | - Daron M. Standley
- Laboratory of Systems Immunology, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
| | - Junichi Takagi
- Laboratory for Protein Synthesis and Expression, Institute for Protein Research, Osaka University, Suita, Osaka, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka, Japan
| | - Kazuhiro Suzuki
- Laboratory of Immune Response Dynamics, WPI Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
- Department of Immune Response Dynamics, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
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5
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Gou P, Zhang W, Giraudier S. Insights into the Potential Mechanisms of JAK2V617F Somatic Mutation Contributing Distinct Phenotypes in Myeloproliferative Neoplasms. Int J Mol Sci 2022; 23:ijms23031013. [PMID: 35162937 PMCID: PMC8835324 DOI: 10.3390/ijms23031013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/09/2022] [Accepted: 01/13/2022] [Indexed: 12/19/2022] Open
Abstract
Myeloproliferative neoplasms (MPN) are a group of blood cancers in which the bone marrow (BM) produces an overabundance of erythrocyte, white blood cells, or platelets. Philadelphia chromosome-negative MPN has three subtypes, including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The over proliferation of blood cells is often associated with somatic mutations, such as JAK2, CALR, and MPL. JAK2V617F is present in 95% of PV and 50–60% of ET and PMF. Based on current molecular dynamics simulations of full JAK2 and the crystal structure of individual domains, it suggests that JAK2 maintains basal activity through self-inhibition, whereas other domains and linkers directly/indirectly enhance this self-inhibited state. Nevertheless, the JAK2V617F mutation is not the only determinant of MPN phenotype, as many normal individuals carry the JAK2V617F mutation without a disease phenotype. Here we review the major MPN phenotypes, JAK-STAT pathways, and mechanisms of development based on structural biology, while also describing the impact of other contributing factors such as gene mutation allele burden, JAK-STAT-related signaling pathways, epigenetic modifications, immune responses, and lifestyle on different MPN phenotypes. The cross-linking of these elements constitutes a complex network of interactions and generates differences in individual and cellular contexts that determine the phenotypic development of MPN.
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Affiliation(s)
- Panhong Gou
- Laboratoire UMRS-1131, Ecole doctorale 561, Université de Paris, 75010 Paris, France
- INSERM UMR-S1131, Hôpital Saint-Louis, 75010 Paris, France
- Correspondence: (P.G.); (S.G.)
| | - Wenchao Zhang
- BFA, UMR 8251, CNRS, Université de Paris, 75013 Paris, France;
| | - Stephane Giraudier
- Laboratoire UMRS-1131, Ecole doctorale 561, Université de Paris, 75010 Paris, France
- INSERM UMR-S1131, Hôpital Saint-Louis, 75010 Paris, France
- Service de Biologie Cellulaire, Hôpital Saint-Louis, AP-HP, 75010 Paris, France
- Correspondence: (P.G.); (S.G.)
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6
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Liang S, Li Z, Zhan J, Zhou Y. De novo protein design by an energy function based on series expansion in distance and orientation dependence. Bioinformatics 2021; 38:86-93. [PMID: 34406339 DOI: 10.1093/bioinformatics/btab598] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 08/11/2021] [Accepted: 08/16/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing. RESULTS The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design. AVAILABILITY AND IMPLEMENTATION The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.
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Affiliation(s)
- Shide Liang
- Department of R & D, Bio-Thera Solutions, Guangzhou 510530, China
| | - Zhixiu Li
- Institute of Health and Biomedical Innovation, Queensland University of Technology at Translational Research Institute, Woolloongabba, QLD 3001, Australia
| | - Jian Zhan
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Gold Coast Campus, Southport, QLD 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Yaoqi Zhou
- Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.,Peking University Shenzhen Graduate School, Shenzhen 518055, China
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7
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Karami Y, Rey J, Postic G, Murail S, Tufféry P, de Vries SJ. DaReUS-Loop: a web server to model multiple loops in homology models. Nucleic Acids Res 2020; 47:W423-W428. [PMID: 31114872 PMCID: PMC6602439 DOI: 10.1093/nar/gkz403] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/20/2019] [Accepted: 05/06/2019] [Indexed: 02/07/2023] Open
Abstract
Loop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.
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Affiliation(s)
- Yasaman Karami
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Julien Rey
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Guillaume Postic
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France.,Institut Français de Bioinformatique (IFB), UMS 3601-CNRS, Université Paris-Saclay, Orsay, France
| | - Samuel Murail
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France
| | - Pierre Tufféry
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
| | - Sjoerd J de Vries
- Sorbonne Paris Cité, Université Paris Diderot, CNRS UMR 8251, INSERM ERL U1133, Paris, France.,Ressource Parisienne en Bioinformatique Structurale (RPBS), Paris, France
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8
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Abstract
The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.
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Affiliation(s)
- Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
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9
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Bansal N, Zheng Z, Song LF, Pei J, Merz KM. The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation. J Am Chem Soc 2018; 140:5434-5446. [PMID: 29607642 DOI: 10.1021/jacs.8b00743] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MTFlex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MTFlex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.
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Affiliation(s)
- Nupur Bansal
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Zheng Zheng
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Lin Frank Song
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Jun Pei
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology , Michigan State University , 578 South Shaw Lane , East Lansing , Michigan 48824 , United States.,Institute for Cyber Enabled Research , Michigan State University , 567 Wilson Road , East Lansing , Michigan 48824 , United States
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10
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Wong SWK, Liu JS, Kou SC. Fast de novo discovery of low-energy protein loop conformations. Proteins 2017; 85:1402-1412. [PMID: 28378911 DOI: 10.1002/prot.25300] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Revised: 03/19/2017] [Accepted: 03/27/2017] [Indexed: 12/25/2022]
Abstract
In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All-atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side-chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near-native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402-1412. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Samuel W K Wong
- Department of Statistics, University of Florida, Gainesville, Florida, 32611
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, Massachusetts, 02138
| | - S C Kou
- Department of Statistics, Harvard University, Cambridge, Massachusetts, 02138
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11
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Park H, Lee GR, Heo L, Seok C. Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments. PLoS One 2014; 9:e113811. [PMID: 25419655 PMCID: PMC4242723 DOI: 10.1371/journal.pone.0113811] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2014] [Accepted: 10/30/2014] [Indexed: 11/19/2022] Open
Abstract
Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.
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Affiliation(s)
- Hahnbeom Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
- * E-mail:
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12
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Shan Y, Gnanasambandan K, Ungureanu D, Kim ET, Hammarén H, Yamashita K, Silvennoinen O, Shaw DE, Hubbard SR. Molecular basis for pseudokinase-dependent autoinhibition of JAK2 tyrosine kinase. Nat Struct Mol Biol 2014; 21:579-84. [PMID: 24918548 DOI: 10.1038/nsmb.2849] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2014] [Accepted: 06/04/2014] [Indexed: 12/31/2022]
Abstract
Janus kinase-2 (JAK2) mediates signaling by various cytokines, including erythropoietin and growth hormone. JAK2 possesses tandem pseudokinase and tyrosine-kinase domains. Mutations in the pseudokinase domain are causally linked to myeloproliferative neoplasms (MPNs) in humans. The structure of the JAK2 tandem kinase domains is unknown, and therefore the molecular bases for pseudokinase-mediated autoinhibition and pathogenic activation remain obscure. Using molecular dynamics simulations of protein-protein docking, we produced a structural model for the autoinhibitory interaction between the JAK2 pseudokinase and kinase domains. A striking feature of our model, which is supported by mutagenesis experiments, is that nearly all of the disease mutations map to the domain interface. The simulations indicate that the kinase domain is stabilized in an inactive state by the pseudokinase domain, and they offer a molecular rationale for the hyperactivity of V617F, the predominant JAK2 MPN mutation.
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Affiliation(s)
- Yibing Shan
- 1] D. E. Shaw Research, New York, New York, USA. [2]
| | - Kavitha Gnanasambandan
- 1] Kimmel Center for Biology and Medicine of the Skirball Institute, Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York, USA. [2]
| | - Daniela Ungureanu
- School of Medicine, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Eric T Kim
- D. E. Shaw Research, New York, New York, USA
| | - Henrik Hammarén
- School of Medicine, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - Kazuo Yamashita
- Systems Immunology Laboratory, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Olli Silvennoinen
- School of Medicine, University of Tampere and Tampere University Hospital, Tampere, Finland
| | - David E Shaw
- 1] D. E. Shaw Research, New York, New York, USA. [2] Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, USA
| | - Stevan R Hubbard
- Kimmel Center for Biology and Medicine of the Skirball Institute, Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, New York, USA
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13
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High-resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations. Proteins 2014; 82:1624-35. [DOI: 10.1002/prot.24591] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Revised: 03/26/2014] [Accepted: 04/12/2014] [Indexed: 02/03/2023]
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14
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Liang S, Zhang C, Zhou Y. LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains. J Comput Chem 2014; 35:335-41. [PMID: 24327406 PMCID: PMC4125323 DOI: 10.1002/jcc.23509] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Revised: 10/06/2013] [Accepted: 11/24/2013] [Indexed: 11/11/2022]
Abstract
Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template-based modeling as well as ab initio protein-structure prediction. Here, we developed a coarse-grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side-chain conformations and ranked by all-atom energy scoring functions. The final integrated technique called loop prediction by energy-assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12-residue test loops and 2.0 Å RMSD for 45 12-residue loops from critical assessment of structure-prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side-chain conformations in protein cores were predicted in the absence of the target loop, loop-prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12-residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks-lab.org.
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Affiliation(s)
- Shide Liang
- Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Chi Zhang
- School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, 68588, USA
| | - Yaoqi Zhou
- School of Informatics, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Drive, Southport Qld 4222, Australia
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15
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Chys P, Chacón P. Random Coordinate Descent with Spinor-matrices and Geometric Filters for Efficient Loop Closure. J Chem Theory Comput 2013; 9:1821-9. [PMID: 26587638 DOI: 10.1021/ct300977f] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein loop closure constitutes a critical step in loop and protein modeling whereby geometrically feasible loops must be found between two given anchor residues. Here, a new analytic/iterative algorithm denoted random coordinate descent (RCD) to perform protein loop closure is described. The algorithm solves loop closure through minimization as in cyclic coordinate descent but selects bonds for optimization randomly, updates loop conformations by spinor-matrices, performs loop closure in both chain directions, and uses a set of geometric filters to yield efficient conformational sampling. Geometric filters allow one to detect clashes and constrain dihedral angles on the fly. The RCD algorithm is at least comparable to state of the art loop closure algorithms due to an excellent balance between efficiency and intrinsic sampling capability. Furthermore, its efficiency allows one to improve conformational sampling by increasing the sampling number without much penalty. Overall, RCD turns out to be accurate, fast, robust, and applicable over a wide range of loop lengths. Because of the versatility of RCD, it is a solid alternative for integration with current loop modeling strategies.
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Affiliation(s)
- Pieter Chys
- Structural Bioinformatics Group, Biological Chemical Physics Department, Institute of Physical Chemistry Rocasolano (IQFR), Consejo Superior de Investigaciones Cientı́ficas (CSIC), Calle de Serrano 119, Madrid 28006, Spain
| | - Pablo Chacón
- Structural Bioinformatics Group, Biological Chemical Physics Department, Institute of Physical Chemistry Rocasolano (IQFR), Consejo Superior de Investigaciones Cientı́ficas (CSIC), Calle de Serrano 119, Madrid 28006, Spain
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16
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Li Y. Conformational sampling in template-free protein loop structure modeling: an overview. Comput Struct Biotechnol J 2013; 5:e201302003. [PMID: 24688696 PMCID: PMC3962101 DOI: 10.5936/csbj.201302003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2012] [Revised: 01/23/2013] [Accepted: 01/28/2013] [Indexed: 01/04/2023] Open
Abstract
Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.
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Affiliation(s)
- Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
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