1
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Xu G, Luo Z, Yan Y, Wang Q, Ma J. OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer. Structure 2024; 32:1001-1010.e2. [PMID: 38657613 DOI: 10.1016/j.str.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/06/2024] [Accepted: 03/28/2024] [Indexed: 04/26/2024]
Abstract
Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25 G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China
| | - Zhenwei Luo
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China
| | - Yaming Yan
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai 200131, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China.
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2
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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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Liu J, Zhang C, Lai L. GeoPacker: A novel deep learning framework for protein side-chain modeling. Protein Sci 2022; 31:e4484. [PMID: 36309961 PMCID: PMC9667900 DOI: 10.1002/pro.4484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/23/2022] [Accepted: 10/26/2022] [Indexed: 12/13/2022]
Abstract
Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in protein sequence design. However, highly efficient and accurate protein side-chain prediction methods that can give detailed atomic interactions are still lacking. In the present study, we developed a deep learning based method, GeoPacker, that uses geometric deep learning coupled ResNet for protein side-chain modeling. GeoPacker explicitly represents atomic interactions with rotational and translational invariance for information extraction of relative locations. GeoPacker outperformed the state-of-the-art energy function-based methods in side-chain structure prediction accuracy and runs about 10 and 700 times faster than the deep learning-based method DLPacker and OPUS-rota4 with comparable prediction accuracy, respectively. The performance of GeoPacker does not depend on the secondary structures that the residues belong to. GeoPacker gives highly accurate predictions for buried residues in the protein core as well as protein-protein interface, making it a useful tool for protein structure modeling, protein, and interaction design.
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Affiliation(s)
- Jiale Liu
- Center for Life Sciences, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
| | - Changsheng Zhang
- BNLMS, College of Chemistry and Molecular EngineeringPeking UniversityBeijingChina
| | - Luhua Lai
- Center for Life Sciences, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- BNLMS, College of Chemistry and Molecular EngineeringPeking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
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4
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Xu G, Wang Y, Wang Q, Ma J. Studying protein-protein interaction through side-chain modeling method OPUS-Mut. Brief Bioinform 2022; 23:6663639. [PMID: 35959990 DOI: 10.1093/bib/bbac330] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 12/12/2022] Open
Abstract
Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China.,Shanghai AI Laboratory, Shanghai 200030, China
| | - Yilin Wang
- Georgetown Preparatory School, North Bethesda, MD 20852, USA
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China.,Shanghai AI Laboratory, Shanghai 200030, China
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5
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Xu G, Wang Q, Ma J. OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors. Brief Bioinform 2022; 23:bbab529. [PMID: 34905769 PMCID: PMC8769891 DOI: 10.1093/bib/bbab529] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/11/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems Fudan University Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center Fudan University Shanghai, 201210, China
- Shanghai AI Laboratory Shanghai, 200030, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology Baylor College of Medicine Houston, Texas 77030, United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems Fudan University Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center Fudan University Shanghai, 201210, China
- Shanghai AI Laboratory Shanghai, 200030, China
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6
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Verburgt J, Kihara D. Benchmarking of structure refinement methods for protein complex models. Proteins 2022; 90:83-95. [PMID: 34309909 PMCID: PMC8671191 DOI: 10.1002/prot.26188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/24/2021] [Accepted: 07/22/2021] [Indexed: 01/03/2023]
Abstract
Protein structure docking is the process in which the quaternary structure of a protein complex is predicted from individual tertiary structures of the protein subunits. Protein docking is typically performed in two main steps. The subunits are first docked while keeping them rigid to form the complex, which is then followed by structure refinement. Structure refinement is crucial for a practical use of computational protein docking models, as it is aimed for correcting conformations of interacting residues and atoms at the interface. Here, we benchmarked the performance of eight existing protein structure refinement methods in refinement of protein complex models. We show that the fraction of native contacts between subunits is by far the most straightforward metric to improve. However, backbone dependent metrics, based on the Root Mean Square Deviation proved more difficult to improve via refinement.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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7
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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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8
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Xu G, Wang Q, Ma J. OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods. J Chem Inf Model 2020; 60:6691-6697. [PMID: 33211480 DOI: 10.1021/acs.jcim.0c00951] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sampling, we first propose a novel side-chain rotamer prediction method based on deep neural networks, named OPUS-RotaNN. Then, on the basis of our previous work OPUS-Rota2, we propose an open-source side-chain modeling framework, OPUS-Rota3, which integrates the results of different methods into its rotamer library as the sampling candidates. By including OPUS-RotaNN into OPUS-Rota3, we conduct our experiments on three native backbone test sets and one non-native backbone test set. On the native backbone test set, CAMEO-Hard61 for example, OPUS-Rota3 successfully predicts 51.14% of all side-chain dihedral angles with a tolerance criterion of 20° and outperforms OSCAR-star (50.87%), SCWRL4 (50.40%), and FASPR (49.85%). On the non-native backbone test set DB379-ITASSER, the accuracy of OPUS-Rota3 is 52.49%, better than OSCAR-star (48.95%), FASPR (48.69%), and SCWRL4 (48.29%). All the source codes including the training codes and the data we used are available at https://github.com/thuxugang/opus_rota3.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States.,Department of Bioengineering, Rice University, Houston, Texas 77005, United States
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9
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Xu G, Wang Q, Ma J. OPUS-Fold: An Open-Source Protein Folding Framework Based on Torsion-Angle Sampling. J Chem Theory Comput 2020; 16:3970-3976. [DOI: 10.1021/acs.jctc.0c00186] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas 77030, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
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10
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Badaczewska-Dawid AE, Kolinski A, Kmiecik S. Computational reconstruction of atomistic protein structures from coarse-grained models. Comput Struct Biotechnol J 2019; 18:162-176. [PMID: 31969975 PMCID: PMC6961067 DOI: 10.1016/j.csbj.2019.12.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 12/10/2019] [Indexed: 01/02/2023] Open
Abstract
Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.
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Affiliation(s)
| | | | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
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11
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Xu G, Ma T, Du J, Wang Q, Ma J. OPUS-Rota2: An Improved Fast and Accurate Side-Chain Modeling Method. J Chem Theory Comput 2019; 15:5154-5160. [PMID: 31412199 DOI: 10.1021/acs.jctc.9b00309] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Side-chain modeling plays a critical role in protein structure prediction. However, in many current methods, balancing the speed and accuracy is still challenging. In this paper, on the basis of our previous work OPUS-Rota (Protein Sci. 2008, 17, 1576-1585), we introduce a new side-chain modeling method, OPUS-Rota2, which is tested on both a 65-protein test set (DB65) in the OPUS-Rota paper and a 379-protein test set (DB379) in the SCWRL4 paper. If the main chain is native, OPUS-Rota2 is more accurate than OPUS-Rota, SCWRL4, and OSCAR-star but slightly less accurate than OSCAR-o. Also, if the main chain is non-native, OPUS-Rota2 is more accurate than any other method. Moreover, OPUS-Rota2 is significantly faster than any other method, in particular, 2 orders of magnitude faster than OSCAR-o. Thus, the combination of higher accuracy and speed of OPUS-Rota2 in modeling side chains on both the native and non-native main chains makes OPUS-Rota2 a very useful tool in protein structure modeling.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems , Fudan University , Shanghai 200433 , China.,School of Life Sciences , Tsinghua University , Beijing 100084 , China
| | | | - Junqing Du
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology , Baylor College of Medicine , One Baylor Plaza, BCM-125 , Houston , Texas 77030 , United States
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology , Baylor College of Medicine , One Baylor Plaza, BCM-125 , Houston , Texas 77030 , United States
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems , Fudan University , Shanghai 200433 , China.,School of Life Sciences , Tsinghua University , Beijing 100084 , China.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology , Baylor College of Medicine , One Baylor Plaza, BCM-125 , Houston , Texas 77030 , United States.,School of Life Sciences , Fudan University , Shanghai 200433 , China
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12
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Yu Z, Yao Y, Deng H, Yi M. ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment. BMC Bioinformatics 2019; 20:299. [PMID: 31159742 PMCID: PMC6547486 DOI: 10.1186/s12859-019-2898-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/13/2019] [Indexed: 01/05/2023] Open
Abstract
Background The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials. Results We developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, “effective atomic interaction” is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination. Conclusions Distance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/. Electronic supplementary material The online version of this article (10.1186/s12859-019-2898-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhongwang Yu
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuangen Yao
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haiyou Deng
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. .,Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Ming Yi
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. .,Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070, China.
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13
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Jumper JM, Faruk NF, Freed KF, Sosnick TR. Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics. PLoS Comput Biol 2018; 14:e1006342. [PMID: 30589846 PMCID: PMC6307715 DOI: 10.1371/journal.pcbi.1006342] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 06/21/2018] [Indexed: 12/02/2022] Open
Abstract
To address the large gap between time scales that can be easily reached by molecular simulations and those required to understand protein dynamics, we present a rapid self-consistent approximation of the side chain free energy at every integration step. In analogy with the adiabatic Born-Oppenheimer approximation for electronic structure, the protein backbone dynamics are simulated as preceding according to the dictates of the free energy of an instantaneously-equilibrated side chain potential. The side chain free energy is computed on the fly, allowing the protein backbone dynamics to traverse a greatly smoothed energetic landscape. This computation results in extremely rapid equilibration and sampling of the Boltzmann distribution. Our method, termed Upside, employs a reduced model involving the three backbone atoms, along with the carbonyl oxygen and amide proton, and a single (oriented) side chain bead having multiple locations reflecting the conformational diversity of the side chain's rotameric states. We also introduce a novel, maximum-likelihood method to parameterize the side chain interactions using protein structures. We demonstrate state-of-the-art accuracy for predicting χ1 rotamer states while consuming only milliseconds of CPU time. Our method enables rapidly equilibrating coarse-grained simulations that can nonetheless contain significant molecular detail. We also show that the resulting free energies of the side chains are sufficiently accurate for de novo folding of some proteins.
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Affiliation(s)
- John M. Jumper
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, United States of America
- Department of Chemistry, and The James Franck Institute, University of Chicago, Chicago, Illinois, United States of America
| | - Nabil F. Faruk
- Graduate Program in Biophysical Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Karl F. Freed
- Department of Chemistry, and The James Franck Institute, University of Chicago, Chicago, Illinois, United States of America
| | - Tobin R. Sosnick
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, United States of America
- Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois, United States of America
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14
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Dauzhenka T, Kundrotas PJ, Vakser IA. Computational Feasibility of an Exhaustive Search of Side-Chain Conformations in Protein-Protein Docking. J Comput Chem 2018; 39:2012-2021. [PMID: 30226647 DOI: 10.1002/jcc.25381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 03/24/2018] [Accepted: 05/26/2018] [Indexed: 11/07/2022]
Abstract
Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Taras Dauzhenka
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
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15
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Leem J, Georges G, Shi J, Deane CM. Antibody side chain conformations are position-dependent. Proteins 2018; 86:383-392. [PMID: 29318667 DOI: 10.1002/prot.25453] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 12/15/2017] [Accepted: 01/05/2018] [Indexed: 11/11/2022]
Abstract
Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position-dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ1 and χ1+2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears.
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Affiliation(s)
- Jinwoo Leem
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, United Kingdom
| | - Guy Georges
- Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Nonnenwald 2, Penzberg, 82377, Germany
| | - Jiye Shi
- Chemistry Department, UCB, 208 Bath Road, Slough, SL1 3WE, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, United Kingdom
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16
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Nishigami H, Kamiya N, Nakamura H. Revisiting antibody modeling assessment for CDR-H3 loop. Protein Eng Des Sel 2016; 29:477-484. [PMID: 27515703 PMCID: PMC5081041 DOI: 10.1093/protein/gzw028] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 06/14/2016] [Accepted: 06/14/2016] [Indexed: 02/05/2023] Open
Abstract
The antigen-binding site of antibodies, also known as complementarity-determining region (CDR), has hypervariable sequence properties. In particular, the third CDR loop of the heavy chain, CDR-H3, has such variability in its sequence, length, and conformation that ordinary modeling techniques cannot build a high-quality structure. At Stage 2 of the Second Antibody Modeling Assessment (AMA-II) held in 2013, the model structures of the CDR-H3 loops were submitted by the seven modelers and were critically assessed. After our participation in AMA-II, we rebuilt one of the long CDR-H3 loops with 13 residues (A52 antibody) by a more precise method, using enhanced conformational sampling with the explicit water model, as compared to our previous method employed at AMA-II. The current stable models obtained from the free energy landscape at 300 K include structures similar to the X-ray crystal structures. Those models were not built in our previous work at AMA-II. The current free energy landscape suggested that the CDR-H3 loop structures in the crystal are not stable in solution, but they are stabilized by the crystal packing effect.
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Affiliation(s)
- Hiroshi Nishigami
- Institute for Protein Research, Osaka University, 3-2, Yamadaoka, Suita, Osaka 565-0871, Japan
- Present address: Graduate School of Life Science, University of Hyogo, 3-2-1, Koto, Kamigori, Akoh, Hyogo 678-1297, Japan
| | - Narutoshi Kamiya
- Institute for Protein Research, Osaka University, 3-2, Yamadaoka, Suita, Osaka 565-0871, Japan
- Advanced Institute for Computational Science, RIKEN, QBiC Building B, 6-2-4, Furuedai, Suita, Osaka 565-0874, Japan
- Present address: Graduate School of Simulation Studies, University of Hyogo, 7-1-28, Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Haruki Nakamura
- Institute for Protein Research, Osaka University, 3-2, Yamadaoka, Suita, Osaka 565-0871, Japan
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17
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Gaillard T, Panel N, Simonson T. Protein side chain conformation predictions with an MMGBSA energy function. Proteins 2016; 84:803-19. [PMID: 26948696 DOI: 10.1002/prot.25030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 02/22/2016] [Accepted: 02/27/2016] [Indexed: 12/17/2022]
Abstract
The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Thomas Gaillard
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France
| | - Nicolas Panel
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France
| | - Thomas Simonson
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France
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18
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Kim H, Kihara D. Protein structure prediction using residue- and fragment-environment potentials in CASP11. Proteins 2015; 84 Suppl 1:105-17. [PMID: 26344195 DOI: 10.1002/prot.24920] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 08/03/2015] [Accepted: 08/31/2015] [Indexed: 11/08/2022]
Abstract
An accurate scoring function that can select near-native structure models from a pool of alternative models is key for successful protein structure prediction. For the critical assessment of techniques for protein structure prediction (CASP) 11, we have built a protocol of protein structure prediction that has novel coarse-grained scoring functions for selecting decoys as the heart of its pipeline. The score named PRESCO (Protein Residue Environment SCOre) developed recently by our group evaluates the native-likeness of local structural environment of residues in a structure decoy considering positions and the depth of side-chains of spatially neighboring residues. We also introduced a helix interaction potential as an additional scoring function for selecting decoys. The best models selected by PRESCO and the helix interaction potential underwent structure refinement, which includes side-chain modeling and relaxation with a short molecular dynamics simulation. Our protocol was successful, achieving the top rank in the free modeling category with a significant margin of the accumulated Z-score to the subsequent groups when the top 1 models were considered. Proteins 2016; 84(Suppl 1):105-117. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Hyungrae Kim
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47906
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47906. .,Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907.
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19
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Peterson LX, Kang X, Kihara D. Assessment of protein side-chain conformation prediction methods in different residue environments. Proteins 2014; 82:1971-84. [PMID: 24619909 PMCID: PMC5007623 DOI: 10.1002/prot.24552] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Revised: 03/02/2014] [Accepted: 03/07/2014] [Indexed: 11/09/2022]
Abstract
Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic-detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette IN, 47907, USA
| | - Xuejiao Kang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette IN, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
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20
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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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21
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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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22
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Abstract
Modeling of side-chain conformations on a fixed protein backbone, also called side-chain packing, plays an important role in protein structure prediction, protein design, molecular docking, and functional analysis. RASP, or RApid Side-chain Predictor, is a recently developed program that can model protein side-chain conformations with both high accuracy and high speed. Moreover, it can generate structures with few atomic clashes. This chapter first provides a brief introduction to the principle and performances of the RASP package. Then details on how to use RASP programs to predict protein side-chain conformations are elaborated. Finally, it describes case studies for structure refinement in homology modeling and residue substitution.
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23
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Petrella RJ. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2013; 12:1341014. [PMID: 25552783 PMCID: PMC4278582 DOI: 10.1142/s0219633613410149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.
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Affiliation(s)
- Robert J. Petrella
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
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24
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Li Z, Yang Y, Zhan J, Dai L, Zhou Y. Energy functions in de novo protein design: current challenges and future prospects. Annu Rev Biophys 2013; 42:315-35. [PMID: 23451890 DOI: 10.1146/annurev-biophys-083012-130315] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.
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Affiliation(s)
- Zhixiu Li
- School of Informatics, Indiana University-Purdue University, Indianapolis, Indiana 46202, USA
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25
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Liang S, Zhang C, Sarmiento J, Standley DM. Protein Loop Modeling with Optimized Backbone Potential Functions. J Chem Theory Comput 2012; 8:1820-7. [PMID: 26593673 DOI: 10.1021/ct300131p] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We represented protein backbone potential as a Fourier series. The parameters of the backbone dihedral potential were initialized to random values and optimized by Monte Carlo simulations so that generated native-like loop decoys had a lower energy than non-native decoys. The low energy regions of the optimized backbone potential were consistent with observed Ramachandran plots derived from crystal structures. The backbone potential was then used for the prediction of loop conformations (OSCAR-loop) combining with the previously described OSCAR force field, which has been shown to be very accurate in side chain modeling. As a result, the accuracy of OSCAR-loop was improved by local energy minimization based on the complete force field. The average accuracies were 0.40, 0.70, 1.10, 2.08, and 3.58 Å for 4, 6, 8, 10, and 12-residue loops, respectively, with each size being represented by 325 to 2809 targets. The accuracy was better than that of other loop modeling algorithms for short loops (<10 residues). For longer loops, the prediction accuracy was improved by concurrently sampling with a fragment-based method, Spanner. OSCAR-loop is available for download at http://sysimm.ifrec.osaka-u.ac.jp/OSCAR/ .
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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, Nebraska 68588, United States
| | - Jamica Sarmiento
- Systems Immunology Lab, Immunology Frontier Research Center, Osaka University , Suita, Osaka, 565-0871, Japan
| | - Daron M Standley
- Systems Immunology Lab, Immunology Frontier Research Center, Osaka University , Suita, Osaka, 565-0871, Japan
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26
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Abstract
MOTIVATION Modeling of side chain conformations constitutes an indispensable effort in protein structure modeling, protein-protein docking and protein design. Thanks to an intensive attention to this field, many of the existing programs can achieve reasonably good and comparable prediction accuracy. Moreover, in our previous work on CIS-RR, we argued that the prediction with few atomic clashes can complement the current existing methods for subsequent analysis and refinement of protein structures. However, these recent efforts to enhance the quality of predicted side chains have been accompanied by a significant increase of computational cost. RESULTS In this study, by mainly focusing on improving the speed of side chain conformation prediction, we present a RApid Side-chain Predictor, called RASP. To achieve a much faster speed with a comparable accuracy to the best existing methods, we not only employ the clash elimination strategy of CIS-RR, but also carefully optimize energy terms and integrate different search algorithms. In comprehensive benchmark testings, RASP is over one order of magnitude faster (~ 40 times over CIS-RR) than the recently developed methods, while achieving comparable or even better accuracy.
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Affiliation(s)
- Zhichao Miao
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
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27
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Liang S, Zheng D, Zhang C, Standley DM. Fast and accurate prediction of protein side-chain conformations. ACTA ACUST UNITED AC 2011; 27:2913-4. [PMID: 21873640 PMCID: PMC3187653 DOI: 10.1093/bioinformatics/btr482] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Summary: We developed a fast and accurate side-chain modeling program [Optimized Side Chain Atomic eneRgy (OSCAR)-star] based on orientation-dependent energy functions and a rigid rotamer model. The average computing time was 18 s per protein for 218 test proteins with higher prediction accuracy (1.1% increase for χ1 and 0.8% increase for χ1+2) than the best performing program developed by other groups. We show that the energy functions, which were calibrated to tolerate the discrete errors of rigid rotamers, are appropriate for protein loop selection, especially for decoys without extensive structural refinement. Availability: OSCAR-star and the 218 test proteins are available for download at http://sysimm.ifrec.osaka-u.ac.jp/OSCAR Contact:standley@ifrec.osaka-u.ac.jp Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shide Liang
- Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan
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28
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Liang S, Zhang C, Standley DM. Protein loop selection using orientation-dependent force fields derived by parameter optimization. Proteins 2011; 79:2260-7. [DOI: 10.1002/prot.23051] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2011] [Revised: 03/21/2011] [Accepted: 03/31/2011] [Indexed: 12/25/2022]
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