1
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Zhao N, Wu T, Wang W, Zhang L, Gong X. Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure. Interdiscip Sci 2024; 16:261-288. [PMID: 38955920 DOI: 10.1007/s12539-024-00626-x] [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: 10/26/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/04/2024]
Abstract
Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.
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Affiliation(s)
- Nan Zhao
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Tong Wu
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Wenda Wang
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Lunchuan Zhang
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
| | - Xinqi Gong
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China.
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
- Beijing Academy of Artificial Intelligence, Beijing, 100084, China.
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2
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Krupa MA, Krupa P. Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking. Methods Mol Biol 2024; 2780:27-41. [PMID: 38987462 DOI: 10.1007/978-1-0716-3985-6_3] [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] [Indexed: 07/12/2024]
Abstract
Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.
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Affiliation(s)
- Magdalena A Krupa
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland.
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3
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Meng Q, Guo F, Wang E, Tang J. ComDock: A novel approach for protein-protein docking with an efficient fusing strategy. Comput Biol Med 2023; 167:107660. [PMID: 37944303 DOI: 10.1016/j.compbiomed.2023.107660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/08/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the template-based method utilizes the structural information of known homologous 3D complexes as available and reliable templates to achieve high accuracy and low computational complexity. However, the performance of the template-based method depends on the quality and quantity of templates. When insufficient or even no templates, the ab initio docking method is necessary and largely enriches the docking conformations. Therefore, it's a feasible strategy to fuse the effectivity of the template-based model and the universality of ab initio model to improve the docking performance. In this study, we construct a new, diverse, comprehensive template library derived from PDB, containing 77,685 complexes. We propose a template-based method (named TemDock), which retrieves the evolutionary relationship between the target sequence and samples in the template library and transfers similar structural information. Then, the target structure is built by superposing on the homologous template complex with TM-align. Moreover, we develop a consensus-based method (named ComDock) to integrate our TemDock and an existing ab initio method (ZDOCK). On 105 targets with templates from Benchmark 5.0, the TemDock and ComDock achieve a success rate of 68.57 % and 71.43 % in the top 10 conformations, respectively. Compared with the HDOCK, ComDock obtains better I-RMSD of hit configurations on 9 targets and more hit models in the top 100 conformations. As an efficient method for protein-protein docking, the ComDock is expected to study protein-protein recognition and reveal the various biological passways that are critical for developing drug discovery. The final results are stored at https://github.com/guofei-tju/mqz_ComDock_docking.
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Affiliation(s)
- Qiaozhen Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Ercheng Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Zhejiang Laboratory, Hangzhou, Zhejiang, China.
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.
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4
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Liu X, Duan Y, Hong X, Xie J, Liu S. Challenges in structural modeling of RNA-protein interactions. Curr Opin Struct Biol 2023; 81:102623. [PMID: 37301066 DOI: 10.1016/j.sbi.2023.102623] [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: 01/31/2023] [Revised: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023]
Abstract
In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein-protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.
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Affiliation(s)
- Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Yingtian Duan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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5
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Marsan ES, Dreab A, Bayse CA. In silico insights into the dimer structure and deiodinase activity of type III iodothyronine deiodinase from bioinformatics, molecular dynamics simulations, and QM/MM calculations. J Biomol Struct Dyn 2023; 41:4819-4829. [PMID: 35579922 PMCID: PMC9878935 DOI: 10.1080/07391102.2022.2073271] [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: 01/18/2022] [Accepted: 04/27/2022] [Indexed: 01/28/2023]
Abstract
The homodimeric family of iodothyronine deiodinases (Dios) regioselectively remove iodine from thyroid hormones. Currently, structural data has only been reported for the monomer of the mus type III thioredoxin (Trx) fold catalytic domain (Dio3Trx), but the mode of dimerization has not yet been determined. Various groups have proposed dimer structures that are similar to the A-type and B-type dimerization modes of peroxiredoxins. Computational methods are used to compare the sequence of Dio3Trx to related proteins known to form A-type and B-type dimers. Sequence analysis and in silico protein-protein docking methods suggest that Dio3Trx is more consistent with proteins that adopt B-type dimerization. Molecular dynamics (MD) simulations of the refined Dio3Trx dimer constructed using the SymmDock and GalaxyRefineComplex databases indicate stable dimer formation along the β4α3 interface consistent with other Trx fold B-type dimers. Free energy calculations show that the dimer is stabilized by interdimer interactions between the β-sheets and α-helices. A comparison of MD simulations of the apo and thyroxine-bound dimers suggests that the active site binding pocket is not affected by dimerization. Determination of the transition state for deiodination of thyroxine from the monomer structure using QM/MM methods provides an activation barrier consistent with previous small model DFT studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Eric S Marsan
- Department of Chemistry and Biochemistry, Old Dominion University, Norfolk, VA
| | - Ana Dreab
- Department of Chemistry and Biochemistry, Old Dominion University, Norfolk, VA
| | - Craig A Bayse
- Department of Chemistry and Biochemistry, Old Dominion University, Norfolk, VA
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Rui H, Ashton KS, Min J, Wang C, Potts PR. Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction. RSC Chem Biol 2023; 4:192-215. [PMID: 36908699 PMCID: PMC9994104 DOI: 10.1039/d2cb00207h] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/02/2023] [Indexed: 01/04/2023] Open
Abstract
Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.
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Affiliation(s)
- Huan Rui
- Center for Research Acceleration by Digital Innovation, Amgen Research Thousand Oaks CA 91320 USA
| | - Kate S Ashton
- Medicinal Chemistry, Amgen Research Thousand Oaks CA 91320 USA
| | - Jaeki Min
- Induced Proximity Platform, Amgen Research Thousand Oaks CA 91320 USA
| | - Connie Wang
- Digital, Technology & Innovation, Amgen Thousand Oaks CA 91320 USA
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7
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Si Y, Yan C. Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and AlphaFold2. Brief Bioinform 2022; 23:6596987. [PMID: 35649388 DOI: 10.1093/bib/bbac208] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/17/2022] [Accepted: 05/05/2022] [Indexed: 12/19/2022] Open
Abstract
AlphaFold2 can predict protein complex structures as long as a multiple sequence alignment (MSA) of the interologs of the target protein-protein interaction (PPI) can be provided. In this study, a simplified phylogeny-based approach was applied to generate the MSA of interologs, which was then used as the input to AlphaFold2 for protein complex structure prediction. In this extensively benchmarked protocol on nonredundant PPI dataset, including 107 bacterial PPIs and 442 eukaryotic PPIs, we show complex structures of 79.5% of the bacterial PPIs and 49.8% of the eukaryotic PPIs can be successfully predicted, which yielded significantly better performance than the application of MSA of interologs prepared by two existing approaches. Considering PPIs may not be conserved in species with long evolutionary distances, we further restricted interologs in the MSA to different taxonomic ranks of the species of the target PPI in protein complex structure prediction. We found that the success rates can be increased to 87.9% for the bacterial PPIs and 56.3% for the eukaryotic PPIs if interologs in the MSA are restricted to a specific taxonomic rank of the species of each target PPI. Finally, we show that the optimal taxonomic ranks for protein complex structure prediction can be selected with the application of the predicted template modeling (TM) scores of the output models.
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Affiliation(s)
- Yunda Si
- School of Physics, Huazhong University of Science and Technology, China
| | - Chengfei Yan
- School of Physics, Huazhong University of Science and Technology, China
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8
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Yang P, Ning K. How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives. IMETA 2022; 1:e9. [PMID: 38867727 PMCID: PMC10989767 DOI: 10.1002/imt2.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2024]
Abstract
It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.
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Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
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9
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Xie J, Zheng J, Hong X, Tong X, Liu X, Song Q, Liu S, Liu S. Protein-DNA complex structure modeling based on structural template. Biochem Biophys Res Commun 2021; 577:152-157. [PMID: 34517213 DOI: 10.1016/j.bbrc.2021.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/05/2021] [Accepted: 09/06/2021] [Indexed: 10/20/2022]
Abstract
DNA-binding is an important feature of proteins, and protein-DNA interaction involves in many life processes. Various computational methods have been developed to predict protein-DNA complex structures due to the difficulty of experimentally obtaining protein-DNA complex structures. However, prediction of protein-DNA complex is still a challenging problem compared with prediction of protein-RNA complex, this may be due to the large conformational changes between bound and unbound structure in both protein and DNA. We extend PRIME 2.0 to PRIME 2.0.1 to model protein-DNA complex structures. By comparing sequence and structure alignment methods, we found that structure-based methods can find more templates than sequence-based methods. The results of all-to-all structure alignments showed that DNA structure plays an important role in prediction of protein-DNA complex structure. By exploring the relationship of sequence and structure, we found that in protein-DNA interaction, numerous structures with dissimilar sequences have similar 3D structures and perform the similar function.
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Affiliation(s)
- Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education), Hubei University of Technology, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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10
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Mayer-Bacon C, Agboha N, Muscalli M, Freeland S. Evolution as a Guide to Designing xeno Amino Acid Alphabets. Int J Mol Sci 2021; 22:ijms22062787. [PMID: 33801827 PMCID: PMC8000707 DOI: 10.3390/ijms22062787] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 02/02/2023] Open
Abstract
Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis.
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Affiliation(s)
- Christopher Mayer-Bacon
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; (C.M.-B.); (N.A.)
| | - Neyiasuo Agboha
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; (C.M.-B.); (N.A.)
| | - Mickey Muscalli
- Individualized Study Program, University of Maryland, Baltimore County, Baltimore, MD 21250, USA;
| | - Stephen Freeland
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; (C.M.-B.); (N.A.)
- Individualized Study Program, University of Maryland, Baltimore County, Baltimore, MD 21250, USA;
- Correspondence:
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11
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Vangaveti S, Vreven T, Zhang Y, Weng Z. Integrating ab initio and template-based algorithms for protein-protein complex structure prediction. Bioinformatics 2020; 36:751-757. [PMID: 31393558 DOI: 10.1093/bioinformatics/btz623] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 07/03/2019] [Accepted: 08/06/2019] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Template-based and template-free methods have both been widely used in predicting the structures of protein-protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein-protein complex structure prediction. RESULTS Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein-protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. AVAILABILITY AND IMPLEMENTATION ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sweta Vangaveti
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
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12
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Zheng J, Hong X, Xie J, Tong X, Liu S. P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking. Bioinformatics 2020; 36:96-103. [PMID: 31173056 DOI: 10.1093/bioinformatics/btz478] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 05/24/2019] [Accepted: 06/04/2019] [Indexed: 01/02/2023] Open
Abstract
MOTIVATION The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. RESULTS In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. AVAILABILITY AND IMPLEMENTATION www.rnabinding.com/P3DOCK/P3DOCK.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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13
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Weng G, Wang E, Wang Z, Liu H, Zhu F, Li D, Hou T. HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res 2020; 47:W322-W330. [PMID: 31106357 PMCID: PMC6602443 DOI: 10.1093/nar/gkz397] [Citation(s) in RCA: 307] [Impact Index Per Article: 76.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 04/23/2019] [Accepted: 05/01/2019] [Indexed: 02/07/2023] Open
Abstract
Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server, the ATTRACT docking algorithm, the HawkRank scoring function developed in our group and the MM/GBSA free energy decomposition analysis were seamlessly integrated into a multi-functional platform. The structures of PPIs were predicted by combining the ATTRACT docking and the HawkRank re-scoring, and the key residues for PPIs were highlighted by the MM/GBSA free energy decomposition. The molecular visualization was supported by 3Dmol.js. For the structural modeling of PPIs, HawkDock could achieve a better performance than ZDOCK 3.0.2 in the benchmark testing. For the prediction of key residues, the important residues that play an essential role in PPIs could be identified in the top 10 residues for ∼81.4% predicted models and ∼95.4% crystal structures in the benchmark dataset. To sum up, the HawkDock server is a powerful tool to predict the binding structures and identify the key residues of PPIs. The HawkDock server is accessible free of charge at http://cadd.zju.edu.cn/hawkdock/.
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Affiliation(s)
- Gaoqi Weng
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Feng Zhu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Dan Li
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China
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14
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Abstract
Protein-protein and protein-DNA/RNA interactions are involved in many cellular processes. Therefore, determining their complex structures at the atomic level is valuable to gain insights into these interactions. Because of the technical difficulties and high cost in experimental methods, computational approaches like molecular docking have been developed to predict the structures of macromolecular complexes in the last decades. To automatically integrate the available binding information from the PDB, we have developed HDOCK, a protein-protein/nucleic acid docking web server by combining template-based and free docking. In this chapter, we first briefly introduce our HDOCK server and then give a step-by-step description of docking bovine chymotrypsinogen A against its inhibitor (PDB ID: 1CGI). Two case studies of realistic examples are also discussed. The HDOCK server is freely available at http://hdock.phys.hust.edu.cn/ .
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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15
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Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
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Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
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16
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Abstract
Motivation A highly efficient template-based protein–protein docking algorithm, nicknamed SnapDock, is presented. It employs a Geometric Hashing-based structural alignment scheme to align the target proteins to the interfaces of non-redundant protein–protein interface libraries. Docking of a pair of proteins utilizing the 22 600 interface PIFACE library is performed in < 2 min on the average. A flexible version of the algorithm allowing hinge motion in one of the proteins is presented as well. Results To evaluate the performance of the algorithm a blind re-modelling of 3547 PDB complexes, which have been uploaded after the PIFACE publication has been performed with success ratio of about 35%. Interestingly, a similar experiment with the template free PatchDock docking algorithm yielded a success rate of about 23% with roughly 1/3 of the solutions different from those of SnapDock. Consequently, the combination of the two methods gave a 42% success ratio. Availability and implementation A web server of the application is under development.
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Affiliation(s)
- Michael Estrin
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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17
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Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer. J Mol Biol 2017; 429:3925-3941. [PMID: 29106933 PMCID: PMC7906438 DOI: 10.1016/j.jmb.2017.10.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/16/2017] [Accepted: 10/16/2017] [Indexed: 02/07/2023]
Abstract
There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
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Affiliation(s)
- Emine Guven-Maiorov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Chung-Jung Tsai
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Buyong Ma
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA; Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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18
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Maheshwari S, Brylinski M. Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics 2017; 18:257. [PMID: 28499419 PMCID: PMC5427563 DOI: 10.1186/s12859-017-1675-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 05/03/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved. RESULTS In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway. CONCLUSIONS Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.
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Affiliation(s)
- Surabhi Maheshwari
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. .,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA.
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19
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Zhang J, Kurgan L. Review and comparative assessment of sequence-based predictors of protein-binding residues. Brief Bioinform 2017; 19:821-837. [DOI: 10.1093/bib/bbx022] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Indexed: 12/31/2022] Open
Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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20
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Yan Y, Wen Z, Wang X, Huang SY. Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking. Proteins 2017; 85:497-512. [PMID: 28026062 DOI: 10.1002/prot.25234] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Revised: 12/15/2016] [Accepted: 12/16/2016] [Indexed: 12/23/2022]
Abstract
Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
| | - Zeyu Wen
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
| | - Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan Hubei, 430074, People's Republic of China
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21
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Zheng J, Kundrotas PJ, Vakser IA, Liu S. Template-Based Modeling of Protein-RNA Interactions. PLoS Comput Biol 2016; 12:e1005120. [PMID: 27662342 PMCID: PMC5035060 DOI: 10.1371/journal.pcbi.1005120] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 08/25/2016] [Indexed: 12/29/2022] Open
Abstract
Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. Structures of protein-RNA complexes are important for characterization of biological processes. The number of experimentally determined protein-RNA complexes is limited. Thus modeling of these complexes is important. Reliable structural predictions of proteins and their complexes are provided by comparative modeling, which takes advantage of similar complexes with experimentally determined structures. Thus, in the case of protein-RNA complexes, it is important to determine if similar proteins and RNAs bind in a similar way. We show that, similarly to the earlier published results on protein-protein complexes, such correlation of the protein-RNA binding mode and the monomers similarity indeed exists, and is stronger when the similarity is determined by structure rather than sequence alignment. The data shows clear transition from random to similar binding mode with the increase of the structural similarity of the monomers. On the basis of the results we designed and implemented a predictive tool, which should be useful for the biological community interested in modeling of protein-RNA interactions.
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Affiliation(s)
- Jinfang Zheng
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Petras J. Kundrotas
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
| | - Ilya A. Vakser
- Center for Computational Biology and Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (SL)
| | - Shiyong Liu
- School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail: (IAV); (SL)
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22
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Yu J, Vavrusa M, Andreani J, Rey J, Tufféry P, Guerois R. InterEvDock: a docking server to predict the structure of protein-protein interactions using evolutionary information. Nucleic Acids Res 2016; 44:W542-9. [PMID: 27131368 PMCID: PMC4987904 DOI: 10.1093/nar/gkw340] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Accepted: 04/17/2016] [Indexed: 12/02/2022] Open
Abstract
The structural modeling of protein–protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/.
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Affiliation(s)
- Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
| | - Marek Vavrusa
- INSERM UMR-S 973 Molécules Thérapeutiques in Silico, INSERM UMR-S 973, RPBS, Université Paris Diderot, 35 rue H. Brion, case 7113, Sorbone Paris Cité, 75205 Paris cedex 13, France
| | - Jessica Andreani
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
| | - Julien Rey
- INSERM UMR-S 973 Molécules Thérapeutiques in Silico, INSERM UMR-S 973, RPBS, Université Paris Diderot, 35 rue H. Brion, case 7113, Sorbone Paris Cité, 75205 Paris cedex 13, France
| | - Pierre Tufféry
- INSERM UMR-S 973 Molécules Thérapeutiques in Silico, INSERM UMR-S 973, RPBS, Université Paris Diderot, 35 rue H. Brion, case 7113, Sorbone Paris Cité, 75205 Paris cedex 13, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, 91198 Gif-sur-Yvette cedex, France
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23
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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24
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Rigid-Docking Approaches to Explore Protein-Protein Interaction Space. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:33-55. [PMID: 27830312 DOI: 10.1007/10_2016_41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.
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25
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Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
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26
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Muratcioglu S, Guven-Maiorov E, Keskin Ö, Gursoy A. Advances in template-based protein docking by utilizing interfaces towards completing structural interactome. Curr Opin Struct Biol 2015; 35:87-92. [PMID: 26539658 DOI: 10.1016/j.sbi.2015.10.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Revised: 10/09/2015] [Accepted: 10/13/2015] [Indexed: 11/27/2022]
Abstract
The increase in the number of structurally determined protein complexes strengthens template-based docking (TBD) methods for modelling protein-protein interactions (PPIs). These methods utilize the known structures of protein complexes as templates to predict the quaternary structure of the target proteins. The templates may be partial or complete structures. Interface based (partial) methods have recently gained interest due in part to the observation that the interface regions are reusable. We describe how available template interfaces can be used to obtain the structural models of protein interactions. Despite the agreement that a majority of the protein complexes can be modelled using the available Protein Data Bank (PDB) structures, a handful of studies argue that we need more template proteins to increase the structural coverage of PPIs. We also discuss the performance of the interface TBD methods at large scale, and the significance of capturing multiple conformations for improving accuracy.
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Affiliation(s)
- Serena Muratcioglu
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Emine Guven-Maiorov
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Özlem Keskin
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, 34450 Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, 34450 Istanbul, Turkey.
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27
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Vreven T, Moal IH, Vangone A, Pierce BG, Kastritis PL, Torchala M, Chaleil R, Jiménez-García B, Bates PA, Fernandez-Recio J, Bonvin AMJJ, Weng Z. Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J Mol Biol 2015; 427:3031-41. [PMID: 26231283 PMCID: PMC4677049 DOI: 10.1016/j.jmb.2015.07.016] [Citation(s) in RCA: 260] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 07/17/2015] [Accepted: 07/17/2015] [Indexed: 01/31/2023]
Abstract
We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom
| | - Raphael Chaleil
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, Lincoln's Inn Fields Laboratory, London WC2A 3LY, United Kingdom.
| | - Juan Fernandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Life Sciences Department, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain.
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584CH Utrecht, The Netherlands.
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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28
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Muratcioglu S, Presman DM, Pooley JR, Grøntved L, Hager GL, Nussinov R, Keskin O, Gursoy A. Structural Modeling of GR Interactions with the SWI/SNF Chromatin Remodeling Complex and C/EBP. Biophys J 2015; 109:1227-39. [PMID: 26278180 PMCID: PMC4576152 DOI: 10.1016/j.bpj.2015.06.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 06/23/2015] [Accepted: 06/23/2015] [Indexed: 11/23/2022] Open
Abstract
The glucocorticoid receptor (GR) is a steroid-hormone-activated transcription factor that modulates gene expression. Transcriptional regulation by the GR requires dynamic receptor binding to specific target sites located across the genome. This binding remodels the chromatin structure to allow interaction with other transcription factors. Thus, chromatin remodeling is an essential component of GR-mediated transcriptional regulation, and understanding the interactions between these molecules at the structural level provides insights into the mechanisms of how GR and chromatin remodeling cooperate to regulate gene expression. This study suggests models for the assembly of the SWI/SNF-A (SWItch/Sucrose-NonFermentable) complex and its interaction with the GR. We used the PRISM algorithm (PRotein Interactions by Structural Matching) to predict the three-dimensional complex structures of the target proteins. The structural models indicate that BAF57 and/or BAF250 mediate the interaction between the GR and the SWI/SNF-A complex, corroborating experimental data. They further suggest that a BAF60a/BAF155 and/or BAF60a/BAF170 interaction is critical for association between the core and variant subunits. Further, we model the interaction between GR and CCAAT-enhancer-binding proteins (C/EBPs), since the GR can regulate gene expression indirectly by interacting with other transcription factors like C/EBPs. We observe that GR can bind to bZip domains of the C/EBPα homodimer as both a monomer and dimer of the DNA-binding domain. In silico mutagenesis of the predicted interface residues confirm the importance of these residues in binding. In vivo analysis of the computationally suggested mutations reveals that double mutations of the leucine residues (L317D+L335D) may disrupt the interaction between GR and C/EBPα. Determination of the complex structures of the GR is of fundamental relevance to understanding its interactions and functions, since the function of a protein or a complex is dictated by its structure. In addition, it may help us estimate the effects of mutations on GR interactions and signaling.
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Affiliation(s)
- Serena Muratcioglu
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, Istanbul, Turkey
| | - Diego M Presman
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - John R Pooley
- Henry Wellcome Laboratories for Integrated Neuroscience and Endocrinology, University of Bristol, Bristol, United Kingdom
| | - Lars Grøntved
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark
| | - Gordon L Hager
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ruth Nussinov
- Cancer and Inflammation Program, Basic Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, Istanbul, Turkey.
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics, Koc University, Istanbul, Turkey; Department of Computer Engineering, Koc University, Istanbul, Turkey.
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29
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Petrey D, Chen TS, Deng L, Garzon JI, Hwang H, Lasso G, Lee H, Silkov A, Honig B. Template-based prediction of protein function. Curr Opin Struct Biol 2015; 32:33-8. [PMID: 25678152 DOI: 10.1016/j.sbi.2015.01.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 01/13/2015] [Accepted: 01/19/2015] [Indexed: 12/11/2022]
Abstract
We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.
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Affiliation(s)
- Donald Petrey
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States.
| | - T Scott Chen
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Lei Deng
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Jose Ignacio Garzon
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Howook Hwang
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Gorka Lasso
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Hunjoong Lee
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Antonina Silkov
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
| | - Barry Honig
- Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Department of Systems Biology, Center for Computational Biology and Bioinformatics, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, United States
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30
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Rodrigues JPGLM, Bonvin AMJJ. Integrative computational modeling of protein interactions. FEBS J 2014; 281:1988-2003. [DOI: 10.1111/febs.12771] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Revised: 01/03/2014] [Accepted: 02/19/2014] [Indexed: 01/09/2023]
Affiliation(s)
- João P. G. L. M. Rodrigues
- Computational Structural Biology Group; Bijvoet Center for Biomolecular Research; Utrecht University; the Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group; Bijvoet Center for Biomolecular Research; Utrecht University; the Netherlands
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31
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Template-based structure modeling of protein-protein interactions. Curr Opin Struct Biol 2013; 24:10-23. [PMID: 24721449 DOI: 10.1016/j.sbi.2013.11.005] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2013] [Revised: 10/29/2013] [Accepted: 11/21/2013] [Indexed: 01/21/2023]
Abstract
The structure of protein-protein complexes can be constructed by using the known structure of other protein complexes as a template. The complex structure templates are generally detected either by homology-based sequence alignments or, given the structure of monomer components, by structure-based comparisons. Critical improvements have been made in recent years by utilizing interface recognition and by recombining monomer and complex template libraries. Encouraging progress has also been witnessed in genome-wide applications of template-based modeling, with modeling accuracy comparable to high-throughput experimental data. Nevertheless, bottlenecks exist due to the incompleteness of the protein-protein complex structure library and the lack of methods for distant homologous template identification and full-length complex structure refinement.
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32
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Minhas FUAA, Geiss BJ, Ben-Hur A. PAIRpred: partner-specific prediction of interacting residues from sequence and structure. Proteins 2013; 82:1142-55. [PMID: 24243399 DOI: 10.1002/prot.24479] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Revised: 11/04/2013] [Accepted: 11/09/2013] [Indexed: 11/10/2022]
Abstract
We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/.
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