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Monastyrskyy B, D'Andrea D, Fidelis K, Tramontano A, Kryshtafovych A. New encouraging developments in contact prediction: Assessment of the CASP11 results. Proteins 2016; 84 Suppl 1:131-44. [PMID: 26474083 PMCID: PMC4834069 DOI: 10.1002/prot.24943] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 09/15/2015] [Accepted: 10/11/2015] [Indexed: 12/27/2022]
Abstract
This article provides a report on the state-of-the-art in the prediction of intra-molecular residue-residue contacts in proteins based on the assessment of the predictions submitted to the CASP11 experiment. The assessment emphasis is placed on the accuracy in predicting long-range contacts. Twenty-nine groups participated in contact prediction in CASP11. At least eight of them used the recently developed evolutionary coupling techniques, with the top group (CONSIP2) reaching precision of 27% on target proteins that could not be modeled by homology. This result indicates a breakthrough in the development of methods based on the correlated mutation approach. Successful prediction of contacts was shown to be practically helpful in modeling three-dimensional structures; in particular target T0806 was modeled exceedingly well with accuracy not yet seen for ab initio targets of this size (>250 residues). Proteins 2016; 84(Suppl 1):131-144. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
| | - Daniel D'Andrea
- Department of Physics, Sapienza-University of Rome, Rome, 00185, Italy
| | | | - Anna Tramontano
- Department of Physics, Sapienza-University of Rome, Rome, 00185, Italy
- Istituto Pasteur-Fondazione Cenci Bolognetti-University of Rome, Rome, 00185, Italy
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52
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Sharma A, Hussain A, Mun BG, Imran QM, Falak N, Lee SU, Kim JY, Hong JK, Loake GJ, Ali A, Yun BW. Comprehensive analysis of plant rapid alkalization factor (RALF) genes. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2016; 106:82-90. [PMID: 27155375 DOI: 10.1016/j.plaphy.2016.03.037] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 03/14/2016] [Accepted: 03/27/2016] [Indexed: 05/04/2023]
Abstract
Receptor mediated signal carriers play a critical role in the regulation of plant defense and development. Rapid alkalization factor (RALF) proteins potentially comprise important signaling components which may have a key role in plant biology. The RALF gene family contains large number of genes in several plant species, however, only a few RALF genes have been characterized to date. In this study, an extensive database search identified 39, 43, 34 and 18 RALF genes in Arabidopsis, rice, maize and soybean, respectively. These RALF genes were found to be highly conserved across the 4 plant species. A comprehensive analysis including the chromosomal location, gene structure, subcellular location, conserved motifs, protein structure, protein-ligand interaction and promoter analysis was performed. RALF genes from four plant species were divided into 7 groups based on phylogenetic analysis. In silico expression analysis of these genes, using microarray and EST data, revealed that these genes exhibit a variety of expression patterns. Furthermore, RALF genes showed distinct expression patterns of transcript accumulation in vivo following nitrosative and oxidative stresses in Arabidopsis. Predicted interaction between RALF and heme ligand also showed that RALF proteins may contribute towards transporting or scavenging oxygen moieties. This suggests a possible role for RALF genes during changes in cellular redox status. Collectively, our data provides a valuable resource to prime future research in the role of RALF genes in plant growth and development.
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Affiliation(s)
- Arti Sharma
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
| | - Adil Hussain
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea; Department of Agriculture, Abdul Wali Khan University, Mardan, Pakistan.
| | - Bong-Gyu Mun
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
| | - Qari Muhammad Imran
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
| | - Noreen Falak
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
| | - Sang-Uk Lee
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
| | - Jae Young Kim
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
| | - Jeum Kyu Hong
- Department of Horticultural Science, Gyeongnam National University of Science and Technology (GNTech), Jinju, Republic of Korea.
| | - Gary John Loake
- Institute of Molecular Plant Sciences, The University of Edinburgh, United Kingdom.
| | - Asad Ali
- Department of Plant Pathology, The University of Agriculture, Peshawar, Pakistan.
| | - Byung-Wook Yun
- School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
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53
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Simkovic F, Thomas JMH, Keegan RM, Winn MD, Mayans O, Rigden DJ. Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds. IUCRJ 2016; 3:259-70. [PMID: 27437113 PMCID: PMC4937781 DOI: 10.1107/s2052252516008113] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 05/18/2016] [Indexed: 05/05/2023]
Abstract
For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions ('decoys'), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue-residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.
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Affiliation(s)
- Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ronan M. Keegan
- Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot OX11 0FA, England
| | - Martyn D. Winn
- Science and Technology Facilities Council, Daresbury Laboratory, Warrington WA4 4AD, England
| | - Olga Mayans
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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54
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Taylor WR. An algorithm to parse segment packing in predicted protein contact maps. Algorithms Mol Biol 2016; 11:17. [PMID: 27330543 PMCID: PMC4912788 DOI: 10.1186/s13015-016-0080-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Accepted: 05/24/2016] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The analysis of correlation in alignments generates a matrix of predicted contacts between positions in the structure and while these can arise for many reasons, the simplest explanation is that the pair of residues are in contact in a three-dimensional structure and are affecting each others selection pressure. To analyse these data, A dynamic programming algorithm was developed for parsing secondary structure interactions in predicted contact maps. RESULTS The non-local nature of the constraints required an iterated approach (using a "frozen approximation") but with good starting definitions, a single pass was usually sufficient. The method was shown to be effective when applied to the transmembrane class of protein and error tolerant even when the signal becomes degraded. In the globular class of protein, where the extent of interactions are more limited and more complex, the algorithm still behaved well, classifying most of the important interactions correctly in both a small and a large test case. For the larger protein, this involved examples of the algorithm apportioning parts of a single large secondary structure element between two different interactions. CONCLUSIONS It is expected that the method will be useful as a pre-processor to coarse-grained modelling methods to extend the range of protein tertiary structure prediction to larger proteins or to data that is currently too 'noisy' to be used by current residue-based methods.
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Bhattacharya D, Cao R, Cheng J. UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling. Bioinformatics 2016; 32:2791-9. [PMID: 27259540 PMCID: PMC5018369 DOI: 10.1093/bioinformatics/btw316] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Accepted: 05/15/2016] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named 'foldons' through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling. RESULTS Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP. AVAILABILITY AND IMPLEMENTATION Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/ CONTACT: chengji@missouri.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Jianlin Cheng
- Department of Computer Science Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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56
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Yang J, Jin QY, Zhang B, Shen HB. R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter. ACTA ACUST UNITED AC 2016; 32:2435-43. [PMID: 27153618 DOI: 10.1093/bioinformatics/btw181] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 04/03/2016] [Indexed: 11/12/2022]
Abstract
MOTIVATION Inter-residue contacts in proteins dictate the topology of protein structures. They are crucial for protein folding and structural stability. Accurate prediction of residue contacts especially for long-range contacts is important to the quality of ab inito structure modeling since they can enforce strong restraints to structure assembly. RESULTS In this paper, we present a new Residue-Residue Contact predictor called R2C that combines machine learning-based and correlated mutation analysis-based methods, together with a two-dimensional Gaussian noise filter to enhance the long-range residue contact prediction. Our results show that the outputs from the machine learning-based method are concentrated with better performance on short-range contacts; while for correlated mutation analysis-based approach, the predictions are widespread with higher accuracy on long-range contacts. An effective query-driven dynamic fusion strategy proposed here takes full advantages of the two different methods, resulting in an impressive overall accuracy improvement. We also show that the contact map directly from the prediction model contains the interesting Gaussian noise, which has not been discovered before. Different from recent studies that tried to further enhance the quality of contact map by removing its transitive noise, we designed a new two-dimensional Gaussian noise filter, which was especially helpful for reinforcing the long-range residue contact prediction. Tested on recent CASP10/11 datasets, the overall top L/5 accuracy of our final R2C predictor is 17.6%/15.5% higher than the pure machine learning-based method and 7.8%/8.3% higher than the correlated mutation analysis-based approach for the long-range residue contact prediction. AVAILABILITY AND IMPLEMENTATION http://www.csbio.sjtu.edu.cn/bioinf/R2C/Contact:hbshen@sjtu.edu.cn SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jing Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Qi-Yu Jin
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Biao Zhang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
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57
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Zhang H, Gao Y, Deng M, Wang C, Zhu J, Li SC, Zheng WM, Bu D. Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix. Biochem Biophys Res Commun 2016; 472:217-22. [PMID: 26920058 DOI: 10.1016/j.bbrc.2016.01.188] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 01/30/2016] [Indexed: 10/22/2022]
Abstract
Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates protein contact prediction by removing background correlations. An implementation of the approach called COLORS (improving COntact prediction using LOw-Rank and Sparse matrix decomposition) is available from http://protein.ict.ac.cn/COLORS/.
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Affiliation(s)
- Haicang Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yujuan Gao
- Center for Quantitative Biology, Peking University, Beijing, China
| | - Minghua Deng
- Center for Quantitative Biology, Peking University, Beijing, China; School of Mathematical Sciences, Peking University, Beijing, China; Center for Statistical Sciences, Peking University, Beijing, China
| | - Chao Wang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jianwei Zhu
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Wei-Mou Zheng
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China.
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China.
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58
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Zhang H, Huang Q, Bei Z, Wei Y, Floudas CA. COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming. Proteins 2016; 84:332-48. [PMID: 26756402 DOI: 10.1002/prot.24979] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 11/19/2015] [Accepted: 12/10/2015] [Indexed: 12/28/2022]
Abstract
In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.
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Affiliation(s)
- Huiling Zhang
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qingsheng Huang
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhendong Bei
- Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yanjie Wei
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Christodoulos A Floudas
- Department of Chemical Engineering, Texas A&M University, College Station, Texas, 77843.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas, 77843
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59
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De novo protein conformational sampling using a probabilistic graphical model. Sci Rep 2015; 5:16332. [PMID: 26541939 PMCID: PMC4635387 DOI: 10.1038/srep16332] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 10/13/2015] [Indexed: 11/08/2022] Open
Abstract
Efficient exploration of protein conformational space remains challenging especially for large proteins when assembling discretized structural fragments extracted from a protein structure data database. We propose a fragment-free probabilistic graphical model, FUSION, for conformational sampling in continuous space and assess its accuracy using ‘blind’ protein targets with a length up to 250 residues from the CASP11 structure prediction exercise. The method reduces sampling bottlenecks, exhibits strong convergence, and demonstrates better performance than the popular fragment assembly method, ROSETTA, on relatively larger proteins with a length of more than 150 residues in our benchmark set. FUSION is freely available through a web server at http://protein.rnet.missouri.edu/FUSION/.
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60
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Márquez-Chamorro AE, Asencio-Cortés G, Santiesteban-Toca CE, Aguilar-Ruiz JS. Soft computing methods for the prediction of protein tertiary structures: A survey. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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61
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Raval A, Piana S, Eastwood MP, Shaw DE. Assessment of the utility of contact-based restraints in accelerating the prediction of protein structure using molecular dynamics simulations. Protein Sci 2015; 25:19-29. [PMID: 26266489 PMCID: PMC4815320 DOI: 10.1002/pro.2770] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 08/07/2015] [Accepted: 08/11/2015] [Indexed: 12/15/2022]
Abstract
Molecular dynamics (MD) simulation is a well-established tool for the computational study of protein structure and dynamics, but its application to the important problem of protein structure prediction remains challenging, in part because extremely long timescales can be required to reach the native structure. Here, we examine the extent to which the use of low-resolution information in the form of residue-residue contacts, which can often be inferred from bioinformatics or experimental studies, can accelerate the determination of protein structure in simulation. We incorporated sets of 62, 31, or 15 contact-based restraints in MD simulations of ubiquitin, a benchmark system known to fold to the native state on the millisecond timescale in unrestrained simulations. One-third of the restrained simulations folded to the native state within a few tens of microseconds-a speedup of over an order of magnitude compared with unrestrained simulations and a demonstration of the potential for limited amounts of structural information to accelerate structure determination. Almost all of the remaining ubiquitin simulations reached near-native conformations within a few tens of microseconds, but remained trapped there, apparently due to the restraints. We discuss potential methodological improvements that would facilitate escape from these near-native traps and allow more simulations to quickly reach the native state. Finally, using a target from the Critical Assessment of protein Structure Prediction (CASP) experiment, we show that distance restraints can improve simulation accuracy: In our simulations, restraints stabilized the native state of the protein, enabling a reasonable structural model to be inferred.
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Affiliation(s)
- Alpan Raval
- D. E. Shaw Research, New York, New York, 10036
| | | | | | - David E Shaw
- D. E. Shaw Research, New York, New York, 10036.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, 10032
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62
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Ma J, Wang S, Wang Z, Xu J. Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning. Bioinformatics 2015; 31:3506-13. [PMID: 26275894 DOI: 10.1093/bioinformatics/btv472] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 08/08/2015] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However, contact prediction is still challenging especially for proteins without a large number of sequence homologs. RESULTS This article presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins without many sequence homologs. Different from existing single-family EC analysis that uses residue coevolution information in only the target protein family, our joint EC analysis uses residue coevolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement this, we model a set of related protein families using Gaussian graphical models and then coestimate their parameters by maximum-likelihood, subject to the constraint that these parameters shall be similar to some degree. Our GGL method can also integrate supervised learning methods to further improve accuracy. Experiments show that our method outperforms existing methods on proteins without thousands of sequence homologs, and that our method performs better on both conserved and family-specific contacts. AVAILABILITY AND IMPLEMENTATION See http://raptorx.uchicago.edu/ContactMap/ for a web server implementing the method. CONTACT j3xu@ttic.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jianzhu Ma
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA
| | - Sheng Wang
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA
| | - Zhiyong Wang
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave. Chicago, Illinois 60637 USA
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63
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Adhikari B, Bhattacharya D, Cao R, Cheng J. CONFOLD: Residue-residue contact-guided ab initio protein folding. Proteins 2015; 83:1436-49. [PMID: 25974172 PMCID: PMC4509844 DOI: 10.1002/prot.24829] [Citation(s) in RCA: 101] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 04/11/2015] [Accepted: 05/02/2015] [Indexed: 12/20/2022]
Abstract
Predicted protein residue-residue contacts can be used to build three-dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three-dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two-stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β-sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM-score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM-score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/.
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Affiliation(s)
- Badri Adhikari
- Department of Computer Science, University of Missouri, Columbia, MO 65211 USA
| | | | - Renzhi Cao
- Department of Computer Science, University of Missouri, Columbia, MO 65211 USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO 65211 USA
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64
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Kozma D, Tusnády GE. TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool. BMC Bioinformatics 2015; 16:201. [PMID: 26123059 PMCID: PMC4486421 DOI: 10.1186/s12859-015-0638-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 06/06/2015] [Indexed: 12/26/2022] Open
Abstract
Background Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence. Results Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77 % of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70 %, whether the obtained, lowest energy structure is the native one. Conclusion These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses. Availability The program is available upon request for academic use. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0638-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Dániel Kozma
- "Momentum" Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, , H 1518, Budapest, Hungary.
| | - Gábor E Tusnády
- "Momentum" Membrane Protein Bioinformatics Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, PO Box 7, , H 1518, Budapest, Hungary.
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Andreani J, Söding J. bbcontacts: prediction of β-strand pairing from direct coupling patterns. ACTA ACUST UNITED AC 2015; 31:1729-37. [PMID: 25618863 DOI: 10.1093/bioinformatics/btv041] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 01/17/2015] [Indexed: 01/08/2023]
Abstract
MOTIVATION It has recently become possible to build reliable de novo models of proteins if a multiple sequence alignment (MSA) of at least 1000 homologous sequences can be built. Methods of global statistical network analysis can explain the observed correlations between columns in the MSA by a small set of directly coupled pairs of columns. Strong couplings are indicative of residue-residue contacts, and from the predicted contacts a structure can be computed. Here, we exploit the structural regularity of paired β-strands that leads to characteristic patterns in the noisy matrices of couplings. The β-β contacts should be detected more reliably than single contacts, reducing the required number of sequences in the MSAs. RESULTS bbcontacts predicts β-β contacts by detecting these characteristic patterns in the 2D map of coupling scores using two hidden Markov models (HMMs), one for parallel and one for antiparallel contacts. β-bulges are modelled as indel states. In contrast to existing methods, bbcontacts uses predicted instead of true secondary structure. On a standard set of 916 test proteins, 34% of which have MSAs with < 1000 sequences, bbcontacts achieves 50% precision for contacting β-β residue pairs at 50% recall using predicted secondary structure and 64% precision at 64% recall using true secondary structure, while existing tools achieve around 45% precision at 45% recall using true secondary structure. AVAILABILITY AND IMPLEMENTATION bbcontacts is open source software (GNU Affero GPL v3) available at https://bitbucket.org/soedinglab/bbcontacts .
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Affiliation(s)
- Jessica Andreani
- Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377 Munich, Germany and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377 Munich, Germany and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
| | - Johannes Söding
- Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377 Munich, Germany and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany Gene Center, LMU Munich, Feodor-Lynen-Strasse 25, 81377 Munich, Germany and Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
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Li G, Theys K, Verheyen J, Pineda-Peña AC, Khouri R, Piampongsant S, Eusébio M, Ramon J, Vandamme AM. A new ensemble coevolution system for detecting HIV-1 protein coevolution. Biol Direct 2015; 10:1. [PMID: 25564011 PMCID: PMC4332441 DOI: 10.1186/s13062-014-0031-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Accepted: 12/02/2014] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND A key challenge in the field of HIV-1 protein evolution is the identification of coevolving amino acids at the molecular level. In the past decades, many sequence-based methods have been designed to detect position-specific coevolution within and between different proteins. However, an ensemble coevolution system that integrates different methods to improve the detection of HIV-1 protein coevolution has not been developed. RESULTS We integrated 27 sequence-based prediction methods published between 2004 and 2013 into an ensemble coevolution system. This system allowed combinations of different sequence-based methods for coevolution predictions. Using HIV-1 protein structures and experimental data, we evaluated the performance of individual and combined sequence-based methods in the prediction of HIV-1 intra- and inter-protein coevolution. We showed that sequence-based methods clustered according to their methodology, and a combination of four methods outperformed any of the 27 individual methods. This four-method combination estimated that HIV-1 intra-protein coevolving positions were mainly located in functional domains and physically contacted with each other in the protein tertiary structures. In the analysis of HIV-1 inter-protein coevolving positions between Gag and protease, protease drug resistance positions near the active site mostly coevolved with Gag cleavage positions (V128, S373-T375, A431, F448-P453) and Gag C-terminal positions (S489-Q500) under selective pressure of protease inhibitors. CONCLUSIONS This study presents a new ensemble coevolution system which detects position-specific coevolution using combinations of 27 different sequence-based methods. Our findings highlight key coevolving residues within HIV-1 structural proteins and between Gag and protease, shedding light on HIV-1 intra- and inter-protein coevolution.
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Affiliation(s)
- Guangdi Li
- KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.
| | - Kristof Theys
- KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.
| | - Jens Verheyen
- Institute of Virology, University hospital, University Duisburg-Essen, Essen, Germany.
| | - Andrea-Clemencia Pineda-Peña
- KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium. .,Clinical and Molecular Infectious Disease Group, Faculty of Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia.
| | - Ricardo Khouri
- KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.
| | - Supinya Piampongsant
- KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium.
| | - Mónica Eusébio
- Centro de Malária e Outras Doenças Tropicais and Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal.
| | - Jan Ramon
- Department of Computer Science, KU Leuven - University of Leuven, Leuven, Belgium.
| | - Anne-Mieke Vandamme
- KU Leuven - University of Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, Leuven, Belgium. .,Centro de Malária e Outras Doenças Tropicais and Unidade de Microbiologia, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisboa, Portugal.
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Ma C, Zhang HH, Wang X. Machine learning for Big Data analytics in plants. TRENDS IN PLANT SCIENCE 2014; 19:798-808. [PMID: 25223304 DOI: 10.1016/j.tplants.2014.08.004] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/30/2014] [Accepted: 08/20/2014] [Indexed: 05/19/2023]
Abstract
Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences.
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Affiliation(s)
- Chuang Ma
- School of Plant Sciences, University of Arizona, 1140 E. South Campus Drive, Tucson, AZ 85721, USA
| | - Hao Helen Zhang
- Department of Mathematics, University of Arizona, 617 North Santa Rita Ave, Tucson, AZ 85721, USA
| | - Xiangfeng Wang
- School of Plant Sciences, University of Arizona, 1140 E. South Campus Drive, Tucson, AZ 85721, USA; Department of Plant Genetics and Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China.
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Improved contact predictions using the recognition of protein like contact patterns. PLoS Comput Biol 2014; 10:e1003889. [PMID: 25375897 PMCID: PMC4222596 DOI: 10.1371/journal.pcbi.1003889] [Citation(s) in RCA: 132] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 09/03/2014] [Indexed: 11/23/2022] Open
Abstract
Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction. Here, we introduce a novel protein contact prediction method PconsC2 that, to the best of our knowledge, outperforms earlier methods. PconsC2 is based on our earlier method, PconsC, as it utilizes the same set of contact predictions from plmDCA and PSICOV. However, in contrast to PconsC, where each residue pair is analysed independently, the initial predictions are analysed in context of neighbouring residue pairs using a deep learning approach, inspired by earlier work. We find that for each layer the deep learning procedure improves the predictions. At the end, after five layers of deep learning and inclusion of a few extra features provides the best performance. An improvement can be seen for all types of proteins, independent on length, number of homologous sequences and structural class. However, the improvement is largest for β-sheet containing proteins. Most importantly the improvement brings for the first time sufficiently accurate predictions to some protein families with less than 1000 homologous sequences. PconsC2 outperforms as well state of the art machine learning based predictors for protein families larger than 100 effective sequences. PconsC2 is licensed under the GNU General Public License v3 and freely available from http://c2.pcons.net/.
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Schneider M, Brock O. Combining physicochemical and evolutionary information for protein contact prediction. PLoS One 2014; 9:e108438. [PMID: 25338092 PMCID: PMC4206277 DOI: 10.1371/journal.pone.0108438] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 07/28/2014] [Indexed: 11/18/2022] Open
Abstract
We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.
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Affiliation(s)
- Michael Schneider
- Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Oliver Brock
- Robotics and Biology Laboratory, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
- * E-mail:
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Chen Y, Shang Y, Xu D. Multi-Dimensional Scaling and MODELLER-Based Evolutionary Algorithms for Protein Model Refinement. PROCEEDINGS OF THE ... CONGRESS ON EVOLUTIONARY COMPUTATION. CONGRESS ON EVOLUTIONARY COMPUTATION 2014; 2014:1038-1045. [PMID: 25844403 PMCID: PMC4380876 DOI: 10.1109/cec.2014.6900443] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Protein structure prediction, i.e., computationally predicting the three-dimensional structure of a protein from its primary sequence, is one of the most important and challenging problems in bioinformatics. Model refinement is a key step in the prediction process, where improved structures are constructed based on a pool of initially generated models. Since the refinement category was added to the biennial Critical Assessment of Structure Prediction (CASP) in 2008, CASP results show that it is a challenge for existing model refinement methods to improve model quality consistently. This paper presents three evolutionary algorithms for protein model refinement, in which multidimensional scaling(MDS), the MODELLER software, and a hybrid of both are used as crossover operators, respectively. The MDS-based method takes a purely geometrical approach and generates a child model by combining the contact maps of multiple parents. The MODELLER-based method takes a statistical and energy minimization approach, and uses the remodeling module in MODELLER program to generate new models from multiple parents. The hybrid method first generates models using the MDS-based method and then run them through the MODELLER-based method, aiming at combining the strength of both. Promising results have been obtained in experiments using CASP datasets. The MDS-based method improved the best of a pool of predicted models in terms of the global distance test score (GDT-TS) in 9 out of 16test targets.
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Affiliation(s)
- Yan Chen
- Yan Chen, Yi Shang, and Dong Xu are with the Department of Computer Science, University of Missouri, Columbia, MO 65211 USA. Dong Xu is also with the Christopher S. Bond Life Science Center, University of Missouri. (, , and )
| | - Yi Shang
- Yan Chen, Yi Shang, and Dong Xu are with the Department of Computer Science, University of Missouri, Columbia, MO 65211 USA. Dong Xu is also with the Christopher S. Bond Life Science Center, University of Missouri. (, , and )
| | - Dong Xu
- Yan Chen, Yi Shang, and Dong Xu are with the Department of Computer Science, University of Missouri, Columbia, MO 65211 USA. Dong Xu is also with the Christopher S. Bond Life Science Center, University of Missouri. (, , and )
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Ma J, Wang S, Wang Z, Xu J. MRFalign: protein homology detection through alignment of Markov random fields. PLoS Comput Biol 2014; 10:e1003500. [PMID: 24675572 PMCID: PMC3967925 DOI: 10.1371/journal.pcbi.1003500] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2013] [Accepted: 01/08/2014] [Indexed: 11/24/2022] Open
Abstract
Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5. Sequence-based protein homology detection has been extensively studied, but it remains very challenging for remote homologs with divergent sequences. So far the most sensitive methods employ HMM-HMM comparison, which models a protein family using HMM (Hidden Markov Model) and then detects homologs using HMM-HMM alignment. HMM cannot model long-range residue interaction patterns and thus, carries very little information regarding the global 3D structure of a protein family. As such, HMM comparison is not sensitive enough for distantly-related homologs. In this paper, we present an MRF-MRF comparison method for homology detection. In particular, we model a protein family using Markov Random Fields (MRF) and then detect homologs by MRF-MRF alignment. Compared to HMM, MRFs are able to model long-range residue interaction pattern and thus, contains information for the overall 3D structure of a protein family. Consequently, MRF-MRF comparison is much more sensitive than HMM-HMM comparison. To implement MRF-MRF comparison, we have developed a new scoring function to measure the similarity of two MRFs and also an efficient ADMM algorithm to optimize the scoring function. Experiments confirm that MRF-MRF comparison indeed outperforms HMM-HMM comparison in terms of both alignment accuracy and remote homology detection, especially for mainly beta proteins.
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Affiliation(s)
- Jianzhu Ma
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Sheng Wang
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Zhiyong Wang
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
- * E-mail:
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Kaján L, Hopf TA, Kalaš M, Marks DS, Rost B. FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics 2014; 15:85. [PMID: 24669753 PMCID: PMC3987048 DOI: 10.1186/1471-2105-15-85] [Citation(s) in RCA: 128] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 03/18/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND 20 years of improved technology and growing sequences now renders residue-residue contact constraints in large protein families through correlated mutations accurate enough to drive de novo predictions of protein three-dimensional structure. The method EVfold broke new ground using mean-field Direct Coupling Analysis (EVfold-mfDCA); the method PSICOV applied a related concept by estimating a sparse inverse covariance matrix. Both methods (EVfold-mfDCA and PSICOV) are publicly available, but both require too much CPU time for interactive applications. On top, EVfold-mfDCA depends on proprietary software. RESULTS Here, we present FreeContact, a fast, open source implementation of EVfold-mfDCA and PSICOV. On a test set of 140 proteins, FreeContact was almost eight times faster than PSICOV without decreasing prediction performance. The EVfold-mfDCA implementation of FreeContact was over 220 times faster than PSICOV with negligible performance decrease. EVfold-mfDCA was unavailable for testing due to its dependency on proprietary software. FreeContact is implemented as the free C++ library "libfreecontact", complete with command line tool "freecontact", as well as Perl and Python modules. All components are available as Debian packages. FreeContact supports the BioXSD format for interoperability. CONCLUSIONS FreeContact provides the opportunity to compute reliable contact predictions in any environment (desktop or cloud).
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Affiliation(s)
| | | | | | | | - Burkhard Rost
- Department for Bioinformatics and Computational Biology, TU Munich, Boltzmannstraße 3, Garching 85748, Germany.
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Ma J, Wang S. Algorithms, Applications, and Challenges of Protein Structure Alignment. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2014; 94:121-75. [DOI: 10.1016/b978-0-12-800168-4.00005-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Abstract
Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known. Results: We report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations between the native and the predicted with L-pfSeqGNM B-factors and between the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein–protein interactions and protein functions. Contact:zerozhua@126.com Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hua Zhang
- School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, P.R. China and Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada
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