1
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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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2
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Cárdenas-Guerra RE, Montes-Flores O, Nava-Pintor EE, Reséndiz-Cardiel G, Flores-Pucheta CI, Rodríguez-Gavaldón YI, Arroyo R, Bottazzi ME, Hotez PJ, Ortega-López J. Chagasin from Trypanosoma cruzi as a molecular scaffold to express epitopes of TSA-1 as soluble recombinant chimeras. Protein Expr Purif 2024; 218:106458. [PMID: 38423156 DOI: 10.1016/j.pep.2024.106458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024]
Abstract
Trypanosoma cruzi is the causative agent of Chagas disease, a global public health problem. New therapeutic drugs and biologics are needed. The TSA-1 recombinant protein of T. cruzi is one such promising antigen for developing a therapeutic vaccine. However, it is overexpressed in E. coli as inclusion bodies, requiring an additional refolding step. As an alternative, in this study, we propose the endogenous cysteine protease inhibitor chagasin as a molecular scaffold to generate chimeric proteins. These proteins will contain combinations of two of the five conserved epitopes (E1 to E5) of TSA-1 in the L4 and L6 chagasin loops. Twenty chimeras (Q1-Q20) were designed, and their solubility was predicted using bioinformatics tools. Nine chimeras with different degrees of solubility were selected and expressed in E. coli BL21 (DE3). Western blot assays with anti-6x-His and anti-chagasin antibodies confirmed the expression of soluble recombinant chimeras. Both theoretically and experimentally, the Q12 (E5-E3) chimera was the most soluble, and the Q20 (E4-E5) the most insoluble protein. Q4 (E5-E1) and Q8 (E5-E2) chimeras were classified as proteins with medium solubility that exhibited the highest yield in the soluble fraction. Notably, Q4 has a yield of 239 mg/L, well above the yield of recombinant chagasin (16.5 mg/L) expressed in a soluble form. The expression of the Q4 chimera was scaled up to a 7 L fermenter obtaining a yield of 490 mg/L. These data show that chagasin can serve as a molecular scaffold for the expression of TSA-1 epitopes in the form of soluble chimeras.
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Affiliation(s)
- Rosa Elena Cárdenas-Guerra
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Octavio Montes-Flores
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Edgar Ezequiel Nava-Pintor
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Gerardo Reséndiz-Cardiel
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Claudia Ivonne Flores-Pucheta
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Yasmín Irene Rodríguez-Gavaldón
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Rossana Arroyo
- Departamento de Infectómica y Patogénesis Molecular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico
| | - Maria Elena Bottazzi
- Texas Children's Hospital Center for Vaccine Development, Department of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Peter J Hotez
- Texas Children's Hospital Center for Vaccine Development, Department of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Jaime Ortega-López
- Departamento de Biotecnología y Bioingeniería, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Av. IPN # 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, CP 07360, Mexico City, Mexico.
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3
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Hamadalnil Y, Altayb HN. In silico molecular study of hepatitis B virus X protein as a therapeutic target. J Biomol Struct Dyn 2024; 42:4002-4015. [PMID: 37254310 DOI: 10.1080/07391102.2023.2217920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/18/2023] [Indexed: 06/01/2023]
Abstract
The Hepatitis B virus is a leading cause of liver cirrhosis and hepatocellular carcinoma. HBx viral protein is considered a contributor to pathogenesis and hepatocarcinogenesis. This study aimed to screen the effect of some antiviral compounds to target HBx protein for inhibition of its function. Here, molecular docking, molcular dynsmic simulation, MM/GBSA and T-SNE methods were applied to study the complex stability and to cluster the conformations that generated in the simulation. Among the 179 compounds screened in this study, three antiviral agents (SC75741, Punicalagin, and Ledipasvir) exhibited the lowest docking energy and best interaction. Among these compounds, SC75741 was identified as a potent inhibitor of HBx that showed the best and most stable interaction during molecular dynamic simulation, and blocking a region near to HBx helix resides (aa 88-100) that is associated with cell invasion. The analysis of relative binding free energy through MM/GBSA for molecular dynamic simulation results revealed binding energy -9.9 kcal/mol for SC75741, -11 kcal/mol for Punicalagin, and -10.1 kcal/mol for Ledipasvir. These results elucidate the possible use of these compounds in the research for targeting HBx.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yassir Hamadalnil
- Faculty of Medicine, Nile University, Khartoum, Sudan
- Ibra Hospital, Ministry of Health, Ibra, Sultanate of Oman
| | - Hisham N Altayb
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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4
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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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5
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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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6
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Stevens AO, He Y. Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction. Biomolecules 2022; 12:985. [PMID: 35883541 PMCID: PMC9312937 DOI: 10.3390/biom12070985] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/05/2022] [Accepted: 07/12/2022] [Indexed: 01/22/2023] Open
Abstract
The inhibition of protein-protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein-protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2's prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.
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Affiliation(s)
- Amy O. Stevens
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA;
| | - Yi He
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA;
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA
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7
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Barozet A, Chacón P, Cortés J. Current approaches to flexible loop modeling. Curr Res Struct Biol 2021; 3:187-191. [PMID: 34409304 PMCID: PMC8361254 DOI: 10.1016/j.crstbi.2021.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 06/30/2021] [Accepted: 07/25/2021] [Indexed: 01/14/2023] Open
Abstract
Loops are key components of protein structures, involved in many biological functions. Due to their conformational variability, the structural investigation of loops is a difficult topic, requiring a combination of experimental and computational methods. This paper provides a brief overview of current computational approaches to flexible loop modeling, and presents the main ingredients of the most standard protocols. Despite great progress in recent years, accurately modeling the conformational variability of long flexible loops remains a challenging problem. Future advances in this field will likely come from a tight coupling of experimental and computational techniques, which would enable a better understanding of the relationships between loop sequence, structural flexibility, and functional roles. In fine, accurate loop modeling will open the road to loop design problems of interest for applications in biomedicine and biotechnology.
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Affiliation(s)
- Amélie Barozet
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
| | - Pablo Chacón
- Department of Biological Physical Chemistry, Rocasolano Physical Chemistry Institute C.S.I.C., Madrid, Spain
| | - Juan Cortés
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
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8
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Feng JJ, Chen JN, Kang W, Wu YD. Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C. J Chem Theory Comput 2021; 17:4614-4628. [PMID: 34170125 DOI: 10.1021/acs.jctc.1c00341] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein loops, connecting the α-helices and β-strands, are involved in many important biological processes. However, due to their conformational flexibility, it is still challenging to accurately determine three-dimensional (3D) structures of long loops experimentally and computationally. Herein, we present a systematic study of the protein loop structure prediction via a total of ∼850 μs molecular dynamics (MD) simulations. For a set of 15 long (10-16 residues) and solvent-exposed loops, we first evaluated the performance of four state-of-the-art loop modeling algorithms, DaReUS-Loop, Sphinx, Rosetta-NGK, and MODELLER, on each loop, and none of them could accurately predict the structures for most loops. Then, temperature replica exchange molecular dynamics (REMD) simulations were conducted with three recent force fields, RSFF2C with TIP3P water model, CHARMM36m with CHARMM-modified TIP3P, and AMBER ff19SB with OPC. We found that our recently developed residue-specific force field RSFF2C performed the best and successfully predicted 12 out of 15 loops with a root-mean-square deviation (RMSD) < 1.5 Å. As an alternative with lower computational cost, normal MD simulations at high temperatures (380, 500, and 620 K) were investigated. Temperature-dependent performance was observed for each force field, and, for RSFF2C+TIP3P, we found that three independent 100-ns MD simulations at 500 K gave comparable results with REMD simulations. These results suggest that MD simulations, especially with enhanced sampling techniques such as replica exchange, with the RSFF2C force field could be useful for accurate loop structure prediction.
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Affiliation(s)
- Jia-Jie Feng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Jia-Nan Chen
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Wei Kang
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen 518132, China
| | - Yun-Dong Wu
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.,College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.,Shenzhen Bay Laboratory, Shenzhen 518132, China
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9
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Studer G, Tauriello G, Bienert S, Biasini M, Johner N, Schwede T. ProMod3-A versatile homology modelling toolbox. PLoS Comput Biol 2021; 17:e1008667. [PMID: 33507980 PMCID: PMC7872268 DOI: 10.1371/journal.pcbi.1008667] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 02/09/2021] [Accepted: 01/03/2021] [Indexed: 11/18/2022] Open
Abstract
Computational methods for protein structure modelling are routinely used to complement experimental structure determination, thus they help to address a broad spectrum of scientific questions in biomedical research. The most accurate methods today are based on homology modelling, i.e. detecting a homologue to the desired target sequence that can be used as a template for modelling. Here we present a versatile open source homology modelling toolbox as foundation for flexible and computationally efficient modelling workflows. ProMod3 is a fully scriptable software platform that can perform all steps required to generate a protein model by homology. Its modular design aims at fast prototyping of novel algorithms and implementing flexible modelling pipelines. Common modelling tasks, such as loop modelling, sidechain modelling or generating a full protein model by homology, are provided as production ready pipelines, forming the starting point for own developments and enhancements. ProMod3 is the central software component of the widely used SWISS-MODEL web-server.
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Affiliation(s)
- Gabriel Studer
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Marco Biasini
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niklaus Johner
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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10
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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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11
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Mitusińska K, Skalski T, Góra A. Simple Selection Procedure to Distinguish between Static and Flexible Loops. Int J Mol Sci 2020; 21:ijms21072293. [PMID: 32225102 PMCID: PMC7177474 DOI: 10.3390/ijms21072293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/22/2020] [Accepted: 03/24/2020] [Indexed: 12/02/2022] Open
Abstract
Loops are the most variable and unorganized elements of the secondary structure of proteins. Their ability to shift their shape can play a role in the binding of small ligands, enzymatic catalysis, or protein–protein interactions. Due to the loop flexibility, the positions of their residues in solved structures show the largest B-factors, or in a worst-case scenario can be unknown. Based on the loops’ movements’ timeline, they can be divided into slow (static) and fast (flexible). Although most of the loops that are missing in experimental structures belong to the flexible loops group, the computational tools for loop reconstruction use a set of static loop conformations to predict the missing part of the structure and evaluate the model. We believe that these two loop types can adopt different conformations and that using scoring functions appropriate for static loops is not sufficient for flexible loops. We showed that common model evaluation methods, are insufficient in the case of flexible solvent-exposed loops. Instead, we recommend using the potential energy to evaluate such loop models. We provide a novel model selection method based on a set of geometrical parameters to distinguish between flexible and static loops without the use of molecular dynamics simulations. We have also pointed out the importance of water network and interactions with the solvent for the flexible loop modeling.
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Affiliation(s)
- Karolina Mitusińska
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100 Gliwice, Poland;
| | - Tomasz Skalski
- Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100 Gliwice, Poland;
| | - Artur Góra
- Tunneling Group, Biotechnology Centre, Silesian University of Technology, ul. Krzywoustego 8, 44-100 Gliwice, Poland;
- Correspondence: ; Tel.: +48-322371659
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12
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Kundert K, Kortemme T. Computational design of structured loops for new protein functions. Biol Chem 2019; 400:275-288. [PMID: 30676995 PMCID: PMC6530579 DOI: 10.1515/hsz-2018-0348] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 12/18/2018] [Indexed: 12/20/2022]
Abstract
The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.
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Affiliation(s)
- Kale Kundert
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA
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13
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Karami Y, Guyon F, De Vries S, Tufféry P. DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins. Sci Rep 2018; 8:13673. [PMID: 30209260 PMCID: PMC6135855 DOI: 10.1038/s41598-018-32079-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 08/31/2018] [Indexed: 11/08/2022] Open
Abstract
Despite efforts during the past decades, loop modeling remains a difficult part of protein structure modeling. Several approaches have been developed in the framework of crystal structures. However, for homology models, the modeling of loops is still far from being solved. We propose DaReUS-Loop, a data-based approach that identifies loop candidates mining the complete set of experimental structures available in the Protein Data Bank. Candidate filtering relies on local conformation profile-profile comparison, together with physico-chemical scoring. Applied to three different template-based test sets, DaReUS-Loop shows significant increase in the number of high-accuracy loops, and significant enhancement for modeling long loops. A special advantage is that our method proposes a prediction confidence score that correlates well with the expected accuracy of the loops. Strikingly, over 50% of successful loop models are derived from unrelated proteins, indicating that fragments under similar constraints tend to adopt similar structure, beyond mere homology.
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Affiliation(s)
- Yasaman Karami
- Molécules Thérapeutiques in silico, UMR-S973, Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Diderot, Sorbonne Paris Cité, RPBS, 75013, Paris, France
| | - Frédéric Guyon
- Molécules Thérapeutiques in silico, UMR-S973, Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Diderot, Sorbonne Paris Cité, RPBS, 75013, Paris, France
| | - Sjoerd De Vries
- Molécules Thérapeutiques in silico, UMR-S973, Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Diderot, Sorbonne Paris Cité, RPBS, 75013, Paris, France.
| | - Pierre Tufféry
- Molécules Thérapeutiques in silico, UMR-S973, Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris Diderot, Sorbonne Paris Cité, RPBS, 75013, Paris, France.
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14
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Won J, Lee GR, Park H, Seok C. GalaxyGPCRloop: Template-Based and Ab Initio Structure Sampling of the Extracellular Loops of G-Protein-Coupled Receptors. J Chem Inf Model 2018; 58:1234-1243. [DOI: 10.1021/acs.jcim.8b00148] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Jonghun Won
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Hahnbeom Park
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
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15
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Marks C, Nowak J, Klostermann S, Georges G, Dunbar J, Shi J, Kelm S, Deane CM. Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction. Bioinformatics 2018; 33:1346-1353. [PMID: 28453681 PMCID: PMC5408792 DOI: 10.1093/bioinformatics/btw823] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 01/09/2017] [Indexed: 01/31/2023] Open
Abstract
Motivation Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Results We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Availability and Implementation Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Claire Marks
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jaroslaw Nowak
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Guy Georges
- Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany
| | - James Dunbar
- Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany
| | - Jiye Shi
- Department of Informatics, UCB Pharma, Slough, UK
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16
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Alam N, Goldstein O, Xia B, Porter KA, Kozakov D, Schueler-Furman O. High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLoS Comput Biol 2017; 13:e1005905. [PMID: 29281622 PMCID: PMC5760072 DOI: 10.1371/journal.pcbi.1005905] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 01/09/2018] [Accepted: 11/29/2017] [Indexed: 11/24/2022] Open
Abstract
Peptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy, as validated on a small but representative set of peptide-protein complex structures well resolved by X-ray crystallography. Our approach opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il. Peptide-protein interactions are crucial components of various important biological processes in living cells. High-resolution structural information of such interactions provides insight about the underlying biophysical principles governing the interactions, and a starting point for their targeted manipulations. Accurate docking algorithms can help fill the gap between the vast number of these interactions and the small number of experimentally solved structures. However, the accuracies of the existing protocols have been limited, in particular for ab initio docking when no information about the peptide beyond its sequence is available. Here we introduce PIPER-FlexPepDock, a fragment-based global docking protocol for high-resolution modeling of peptide-protein interactions. Integration of accurate and efficient representation of the peptide using fragment ensembles, their fast and exhaustive rigid-body docking, and their subsequent accurate flexible refinement, enables peptide-protein docking of remarkable accuracy. The validation on a representative benchmark set of crystallographically solved high-resolution peptide-protein complexes demonstrates significantly improved performance over all existing docking protocols. This opens up the way to the modeling of many more peptide-protein interactions, and to a more detailed study of peptide-protein association in general.
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Affiliation(s)
- Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Oriel Goldstein
- School of Computer Sciences and Engineering, The Hebrew University, Jerusalem, Israel
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Kathryn A. Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, United States of America
- Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, New York, United States of America
- * E-mail: (OSF); (DK)
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
- * E-mail: (OSF); (DK)
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17
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Marks C, Deane C. Antibody H3 Structure Prediction. Comput Struct Biotechnol J 2017; 15:222-231. [PMID: 28228926 PMCID: PMC5312500 DOI: 10.1016/j.csbj.2017.01.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 01/24/2017] [Accepted: 01/27/2017] [Indexed: 01/20/2023] Open
Abstract
Antibodies are proteins of the immune system that are able to bind to a huge variety of different substances, making them attractive candidates for therapeutic applications. Antibody structures have the potential to be useful during drug development, allowing the implementation of rational design procedures. The most challenging part of the antibody structure to experimentally determine or model is the H3 loop, which in addition is often the most important region in an antibody's binding site. This review summarises the approaches used so far in the pursuit of accurate computational H3 structure prediction.
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Affiliation(s)
- C. Marks
- Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, United Kingdom
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18
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Childers MC, Towse CL, Daggett V. The effect of chirality and steric hindrance on intrinsic backbone conformational propensities: tools for protein design. Protein Eng Des Sel 2016; 29:271-80. [PMID: 27284086 DOI: 10.1093/protein/gzw023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 05/11/2016] [Indexed: 01/30/2023] Open
Abstract
The conformational propensities of amino acids are an amalgamation of sequence effects, environmental effects and underlying intrinsic behavior. Many have attempted to investigate neighboring residue effects to aid in our understanding of protein folding and improve structure prediction efforts, especially with respect to difficult to characterize states, such as disordered or unfolded states. Host-guest peptide series are a useful tool in examining the propensities of the amino acids free from the surrounding protein structure. Here, we compare the distributions of the backbone dihedral angles (φ/ψ) of the 20 proteogenic amino acids in two different sequence contexts using the AAXAA and GGXGG host-guest pentapeptide series. We further examine their intrinsic behaviors across three environmental contexts: water at 298 K, water at 498 K, and 8 M urea at 298 K. The GGXGG systems provide the intrinsic amino acid propensities devoid of any conformational context. The alanine residues in the AAXAA series enforce backbone chirality, thereby providing a model of the intrinsic behavior of amino acids in a protein chain. Our results show modest differences in φ/ψ distributions due to the steric constraints of the Ala side chains, the magnitudes of which are dependent on the denaturing conditions. One of the strongest factors modulating φ/ψ distributions was the protonation of titratable side chains, and the largest differences observed were in the amino acid propensities for the rarely sampled αL region.
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Affiliation(s)
| | - Clare-Louise Towse
- Department of Bioengineering, University of Washington, Seattle, WA 98195-5013, USA
| | - Valerie Daggett
- Department of Bioengineering, University of Washington, Seattle, WA 98195-5013, USA
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19
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Ismer J, Rose AS, Tiemann JKS, Goede A, Preissner R, Hildebrand PW. SL2: an interactive webtool for modeling of missing segments in proteins. Nucleic Acids Res 2016; 44:W390-4. [PMID: 27105847 PMCID: PMC4987885 DOI: 10.1093/nar/gkw297] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 04/11/2016] [Indexed: 11/22/2022] Open
Abstract
SuperLooper2 (SL2) (http://proteinformatics.charite.de/sl2) is the updated version of our previous web-server SuperLooper, a fragment based tool for the prediction and interactive placement of loop structures into globular and helical membrane proteins. In comparison to our previous version, SL2 benefits from both a considerably enlarged database of fragments derived from high-resolution 3D protein structures of globular and helical membrane proteins, and the integration of a new protein viewer. The database, now with double the content, significantly improved the coverage of fragment conformations and prediction quality. The employment of the NGL viewer for visualization of the protein under investigation and interactive selection of appropriate loops makes SL2 independent of third-party plug-ins and additional installations.
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Affiliation(s)
- Jochen Ismer
- Institute of Medical Physics and Biophysics, University Medicine, Berlin, 10117 Berlin, Germany
| | - Alexander S Rose
- Institute of Medical Physics and Biophysics, University Medicine, Berlin, 10117 Berlin, Germany
| | - Johanna K S Tiemann
- Institute of Medical Physics and Biophysics, University Medicine, Berlin, 10117 Berlin, Germany
| | - Andrean Goede
- Institute of Physiology & Experimental Clinical Research Center, University Medicine, Berlin, 13125, Germany
| | - Robert Preissner
- Institute of Physiology & Experimental Clinical Research Center, University Medicine, Berlin, 13125, Germany
| | - Peter W Hildebrand
- Institute of Medical Physics and Biophysics, University Medicine, Berlin, 10117 Berlin, Germany
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