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Tao L, Zhou T, Wu Z, Hu F, Yang S, Kong X, Li C. ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots. J Chem Inf Model 2024; 64:3548-3557. [PMID: 38587997 DOI: 10.1021/acs.jcim.3c02011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.
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Affiliation(s)
- Lianci Tao
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Tong Zhou
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Zhixiang Wu
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Fangrui Hu
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Shuang Yang
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Xiaotian Kong
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
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2
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Chandra A, Sharma A, Dehzangi I, Tsunoda T, Sattar A. PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features. Sci Rep 2023; 13:20882. [PMID: 38016996 PMCID: PMC10684570 DOI: 10.1038/s41598-023-47624-5] [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: 09/07/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023] Open
Abstract
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .
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Affiliation(s)
- Abel Chandra
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, USA
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, School of Science, The University of Tokyo, Tokyo, Japan
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
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Gou X, Feng X, Shi H, Guo T, Xie R, Liu Y, Wang Q, Li H, Yang B, Chen L, Lu Y. PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:1417-1431. [PMID: 35398963 PMCID: PMC9241370 DOI: 10.1111/pbi.13823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 04/03/2022] [Indexed: 05/31/2023]
Abstract
Single amino acid substitution (SAAS) produces the most common variant of protein function change under physiological conditions. As the number of SAAS events in plants has increased exponentially, an effective prediction tool is required to help identify and distinguish functional SAASs from the whole genome as either potentially causal traits or as variants. Here, we constructed a plant SAAS database that stores 12 865 SAASs in 6172 proteins and developed a tool called Plant Protein Variation Effect Detector (PPVED) that predicts the effect of SAASs on protein function in plants. PPVED achieved an 87% predictive accuracy when applied to plant SAASs, an accuracy that was much higher than those from six human database software: SIFT, PROVEAN, PANTHER-PSEP, PhD-SNP, PolyPhen-2, and MutPred2. The predictive effect of six SAASs from three proteins in Arabidopsis and maize was validated with wet lab experiments, of which five substitution sites were accurately predicted. PPVED could facilitate the identification and characterization of genetic variants that explain observed phenotype variations in plants, contributing to solutions for challenges in functional genomics and systems biology. PPVED can be accessed under a CC-BY (4.0) license via http://www.ppved.org.cn.
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Affiliation(s)
- Xiangjian Gou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest ChinaWenjiangSichuanChina
- Maize Research InstituteSichuan Agricultural UniversityWenjiangSichuanChina
| | - Xuanjun Feng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest ChinaWenjiangSichuanChina
- Maize Research InstituteSichuan Agricultural UniversityWenjiangSichuanChina
| | - Haoran Shi
- Chengdu Academy of Agricultural and Forestry SciencesWenjiangSichuanChina
| | - Tingting Guo
- National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanHubeiChina
| | - Rongqian Xie
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest ChinaWenjiangSichuanChina
- Maize Research InstituteSichuan Agricultural UniversityWenjiangSichuanChina
| | - Yaxi Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest ChinaWenjiangSichuanChina
- Triticeae Research InstituteSichuan Agricultural UniversityWenjiangSichuanChina
| | - Qi Wang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest ChinaWenjiangSichuanChina
| | - Hongxiang Li
- College of Information EngineeringSichuan Agricultural UniversityYa’anSichuanChina
| | - Banglie Yang
- College of Information EngineeringSichuan Agricultural UniversityYa’anSichuanChina
| | - Lixue Chen
- College of Information EngineeringSichuan Agricultural UniversityYa’anSichuanChina
| | - Yanli Lu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest ChinaWenjiangSichuanChina
- Maize Research InstituteSichuan Agricultural UniversityWenjiangSichuanChina
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4
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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction. Nat Methods 2022; 19:730-739. [DOI: 10.1038/s41592-022-01490-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 04/12/2022] [Indexed: 11/08/2022]
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5
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Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Song J. iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Res 2022; 50:W434-W447. [PMID: 35524557 PMCID: PMC9252729 DOI: 10.1093/nar/gkac351] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China.,Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yanan Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Chris Bain
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
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6
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Zhou T, Rong J, Liu Y, Gong W, Li C. An ensemble approach to predict binding hotspots in protein-RNA interactions based on SMOTE data balancing and random grouping feature selection strategies. Bioinformatics 2022; 38:2452-2458. [PMID: 35253843 DOI: 10.1093/bioinformatics/btac138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/15/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The identification of binding hotspots in protein-RNA interactions is crucial for understanding their potential recognition mechanisms and drug design. The experimental methods have many limitations, since they are usually time-consuming and labor-intensive. Thus, developing an effective and efficient theoretical method is urgently needed. RESULTS Here we present SREPRHot, a method to predict hotspots, defined as the residues whose mutation to alanine generate a binding free energy change ≥ 2.0 kcal/mol, while others use a cutoff of 1.0 kcal/mol to obtain balanced datasets. To deal with the dataset imbalance, Synthetic Minority Over-sampling Technique (SMOTE) is utilized to generate minority samples to achieve a dataset balance. Additionally, besides conventional features, we use two types of new features, residue interface propensity previously developed by us, and topological features obtained using node-weighted networks, and propose an effective Random Grouping feature selection strategy combined with a two-step method to determine an optimal feature set. Finally, a stacking ensemble classifier is adopted to build our model. The results show SREPRHot achieves a good performance with SEN, MCC and AUC of 0.900, 0.557 and 0.829 on the independent testing dataset. The comparison study indicates SREPRHot shows a promising performance. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/ChunhuaLiLab/SREPRHot. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tong Zhou
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Jie Rong
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Yang Liu
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Weikang Gong
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Chunhua Li
- Falcuty of Environmental and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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7
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Roura Padrosa D, Marchini V, Paradisi F. CapiPy: python based GUI-application to assist in protein immobilization. Bioinformatics 2021; 37:2761-2762. [PMID: 33459767 DOI: 10.1093/bioinformatics/btab030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/14/2020] [Accepted: 01/11/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Protein immobilization, while widespread to unlock enzyme potential in biocatalysis, remains tied to a trial an error approach. Nonetheless, several databases and computational methods have been developed for protein characterization and their study. CapiPy is a user-friendly application for protein model creation and subsequent analysis with a special focus on the ease of use and interpretation of the results to help the users to make an informed decision on the immobilization approach which should be ideal for a protein of interest. The package has been tested with three separate random sets of 150 protein sequences from Uniprot with more than a 70% overall success rate (see Supplementary information and Supplementary Dataset). AVAILABILITY AND IMPLEMENTATION The package is free to use under the GNU General Public License v3.0. All necessary files can be downloaded from https://github.com/drou0302/CapiPy or https://pypi.org/project/CapiPy/. All external requirements are also freely available, with some restrictions for non-academic users. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Roura Padrosa
- Department of Chemistry and Biochemistry, Freiestrasse 3, Bern, 3012, Switzerland
| | - Valentina Marchini
- Department of Chemistry and Biochemistry, Freiestrasse 3, Bern, 3012, Switzerland
| | - Francesca Paradisi
- Department of Chemistry and Biochemistry, Freiestrasse 3, Bern, 3012, Switzerland
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8
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Pan Y, Zhou S, Guan J. Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach. BMC Bioinformatics 2020; 21:384. [PMID: 32938375 PMCID: PMC7495898 DOI: 10.1186/s12859-020-03675-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods. RESULTS Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones. CONCLUSIONS PreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .
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Affiliation(s)
- Yuliang Pan
- Department of Computer Science and Technology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China
| | - Shuigeng Zhou
- Shanghai Key Laboratory of Intelligent Information Processing, and School of Computer Science, Fudan University, No. 220 Handan Road, Shanghai, 200433, China
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China.
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9
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Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou KC. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2020; 20:638-658. [PMID: 29897410 PMCID: PMC6556904 DOI: 10.1093/bib/bby028] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 03/02/2018] [Indexed: 01/03/2023] Open
Abstract
Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.
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Affiliation(s)
- Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.,Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Yanan Wang
- 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
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan
| | - Neil D Rawlings
- EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA and Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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10
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Deng L, Sui Y, Zhang J. XGBPRH: Prediction of Binding Hot Spots at Protein⁻RNA Interfaces Utilizing Extreme Gradient Boosting. Genes (Basel) 2019; 10:genes10030242. [PMID: 30901953 PMCID: PMC6471955 DOI: 10.3390/genes10030242] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/14/2019] [Accepted: 03/15/2019] [Indexed: 01/24/2023] Open
Abstract
Hot spot residues at protein⁻RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein⁻RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein⁻RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein⁻RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots.
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Affiliation(s)
- Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.
| | - Yuanchao Sui
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.
| | - Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China.
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11
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Pan Y, Wang Z, Zhan W, Deng L. Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach. Bioinformatics 2019; 34:1473-1480. [PMID: 29281004 DOI: 10.1093/bioinformatics/btx822] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/19/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Results Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. Availability and implementation The PrabHot webserver is freely available at http://denglab.org/PrabHot/. Contact leideng@csu.edu.cn. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuliang Pan
- School of Software, Central South University, Changsha 410075, China
| | - Zixiang Wang
- School of Software, Central South University, Changsha 410075, China
| | - Weihua Zhan
- School of Electronics and Computer Science, Zhejiang Wanli University, Ningbo 315100, China
| | - Lei Deng
- School of Software, Central South University, Changsha 410075, China
- Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
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12
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Heffernan R, Paliwal K, Lyons J, Singh J, Yang Y, Zhou Y. Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning. J Comput Chem 2018; 39:2210-2216. [PMID: 30368831 DOI: 10.1002/jcc.25534] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/11/2018] [Accepted: 06/14/2018] [Indexed: 02/01/2023]
Abstract
Predicting protein structure from sequence alone is challenging. Thus, the majority of methods for protein structure prediction rely on evolutionary information from multiple sequence alignments. In previous work we showed that Long Short-Term Bidirectional Recurrent Neural Networks (LSTM-BRNNs) improved over regular neural networks by better capturing intra-sequence dependencies. Here we show a single-sequence-based prediction method employing LSTM-BRNNs (SPIDER3-Single), that consistently achieves Q3 accuracy of 72.5%, and correlation coefficient of 0.67 between predicted and actual solvent accessible surface area. Moreover, it yields reasonably accurate prediction of eight-state secondary structure, main-chain angles (backbone ϕ and ψ torsion angles and C α-atom-based θ and τ angles), half-sphere exposure, and contact number. The method is more accurate than the corresponding evolutionary-based method for proteins with few sequence homologs, and computationally efficient for large-scale screening of protein-structural properties. It is available as an option in the SPIDER3 server, and a standalone version for download, at http://sparks-lab.org. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Rhys Heffernan
- Signal Processing Laboratory, Griffith University, Brisbane, QLD, 4111, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, QLD, 4111, Australia
| | - James Lyons
- Signal Processing Laboratory, Griffith University, Brisbane, QLD, 4111, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, Griffith University, Brisbane, QLD, 4111, Australia
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yet-Sen University, Guangzhou, China
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD, 4222, Australia
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13
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Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting. Sci Rep 2018; 8:14285. [PMID: 30250210 PMCID: PMC6155324 DOI: 10.1038/s41598-018-32511-1] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 09/10/2018] [Indexed: 12/11/2022] Open
Abstract
Identification of hot spots, a small portion of protein-protein interface residues that contribute the majority of the binding free energy, can provide crucial information for understanding the function of proteins and studying their interactions. Based on our previous method (PredHS), we propose a new computational approach, PredHS2, that can further improve the accuracy of predicting hot spots at protein-protein interfaces. Firstly we build a new training dataset of 313 alanine-mutated interface residues extracted from 34 protein complexes. Then we generate a wide variety of 600 sequence, structure, exposure and energy features, together with Euclidean and Voronoi neighborhood properties. To remove redundant and irrelevant information, we select a set of 26 optimal features utilizing a two-step feature selection method, which consist of a minimum Redundancy Maximum Relevance (mRMR) procedure and a sequential forward selection process. Based on the selected 26 features, we use Extreme Gradient Boosting (XGBoost) to build our prediction model. Performance of our PredHS2 approach outperforms other machine learning algorithms and other state-of-the-art hot spot prediction methods on the training dataset and the independent test set (BID) respectively. Several novel features, such as solvent exposure characteristics, second structure features and disorder scores, are found to be more effective in discriminating hot spots. Moreover, the update of the training dataset and the new feature selection and classification algorithms play a vital role in improving the prediction quality.
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14
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Taherzadeh G, Yang Y, Xu H, Xue Y, Liew AWC, Zhou Y. Predicting lysine-malonylation sites of proteins using sequence and predicted structural features. J Comput Chem 2018; 39:1757-1763. [DOI: 10.1002/jcc.25353] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/30/2018] [Accepted: 04/08/2018] [Indexed: 12/21/2022]
Affiliation(s)
- Ghazaleh Taherzadeh
- School of Information and Communication Technology; Griffith University, Parklands Drive; Southport Queensland 4222 Australia
| | - Yuedong Yang
- School of Data and Computer Science; Sun Yat-sen University; Guangzhou 510275 China
| | - Haodong Xu
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering; Huazhong University of Science and Technology; Wuhan Hubei 430074 China
| | - Yu Xue
- Key Laboratory of Molecular Biophysics of Ministry of Education, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering; Huazhong University of Science and Technology; Wuhan Hubei 430074 China
| | - Alan Wee-Chung Liew
- School of Information and Communication Technology; Griffith University, Parklands Drive; Southport Queensland 4222 Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology; Griffith University, Parklands Drive; Southport Queensland 4222 Australia
- Institute for Glycomics, Griffith University, Parklands Dr; Southport Queensland 4222 Australia
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15
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Song J, Wang Y, Li F, Akutsu T, Rawlings ND, Webb GI, Chou KC. iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Brief Bioinform 2018. [DOI: 10.1093/bib/bby028 epub ahead of print].] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia and ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Yanan Wang
- 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
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan
| | - Neil D Rawlings
- EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA 02478, USA and Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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16
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Song J, Li F, Takemoto K, Haffari G, Akutsu T, Chou KC, Webb GI. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. J Theor Biol 2018; 443:125-137. [DOI: 10.1016/j.jtbi.2018.01.023] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 01/17/2018] [Accepted: 01/18/2018] [Indexed: 10/18/2022]
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17
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Pan Y, Liu D, Deng L. Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties. PLoS One 2017; 12:e0179314. [PMID: 28614374 PMCID: PMC5470696 DOI: 10.1371/journal.pone.0179314] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Accepted: 05/27/2017] [Indexed: 12/20/2022] Open
Abstract
Single amino acid variations (SAVs) potentially alter biological functions, including causing diseases or natural differences between individuals. Identifying the relationship between a SAV and certain disease provides the starting point for understanding the underlying mechanisms of specific associations, and can help further prevention and diagnosis of inherited disease.We propose PredSAV, a computational method that can effectively predict how likely SAVs are to be associated with disease by incorporating gradient tree boosting (GTB) algorithm and optimally selected neighborhood features. A two-step feature selection approach is used to explore the most relevant and informative neighborhood properties that contribute to the prediction of disease association of SAVs across a wide range of sequence and structural features, especially some novel structural neighborhood features. In cross-validation experiments on the benchmark dataset, PredSAV achieves promising performances with an AUC score of 0.908 and a specificity of 0.838, which are significantly better than that of the other existing methods. Furthermore, we validate the capability of our proposed method by an independent test and gain a competitive advantage as a result. PredSAV, which combines gradient tree boosting with optimally selected neighborhood features, can return reliable predictions in distinguishing between disease-associated and neutral variants. Compared with existing methods, PredSAV shows improved specificity as well as increased overall performance.
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Affiliation(s)
- Yuliang Pan
- School of Software, Central South University, Changsha, China
| | - Diwei Liu
- School of Software, Central South University, Changsha, China
| | - Lei Deng
- School of Software, Central South University, Changsha, China
- Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
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18
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Wang W, Sun L, Zhang S, Zhang H, Shi J, Xu T, Li K. Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences. BMC Bioinformatics 2017; 18:300. [PMID: 28606086 PMCID: PMC5469069 DOI: 10.1186/s12859-017-1715-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/06/2017] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND DNA-binding proteins perform important functions in a great number of biological activities. DNA-binding proteins can interact with ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA), and DNA-binding proteins can be categorized as single-stranded DNA-binding proteins (SSBs) and double-stranded DNA-binding proteins (DSBs). The identification of DNA-binding proteins from amino acid sequences can help to annotate protein functions and understand the binding specificity. In this study, we systematically consider a variety of schemes to represent protein sequences: OAAC (overall amino acid composition) features, dipeptide compositions, PSSM (position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs. RESULTS Our results suggest that some sequence features can significantly differentiate DSBs and SSBs. Evaluated by 10 fold cross-validation on the benchmark datasets, our prediction method can achieve the accuracy of 88.7% and AUC (area under the curve) of 0.919. Moreover, our method has good performance in independent testing. CONCLUSIONS Using various sequence-derived features, a novel method is proposed to distinguish DSBs and SSBs accurately. The method also explores novel features, which could be helpful to discover the binding specificity of DNA-binding proteins.
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Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province 453007 China
- Laboratory of Computation Intelligence and Information Processing, Engineering Technology Research Center for Computing Intelligence and Data Mining, Xinxiang, Henan Province 453007 China
| | - Lin Sun
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province 453007 China
| | - Shiguang Zhang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province 453007 China
| | - Hongjun Zhang
- School of Aviation Engineering, Anyang University, Anyang, Henan Province 455000 China
| | - Jinling Shi
- School of International Education, Xuchang University, Xuchang, Henan Province 461000 China
| | - Tianhe Xu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province 453007 China
| | - Keliang Li
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan Province 453007 China
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Heffernan R, Yang Y, Paliwal K, Zhou Y. Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. Bioinformatics 2017; 33:2842-2849. [DOI: 10.1093/bioinformatics/btx218] [Citation(s) in RCA: 234] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 04/15/2017] [Indexed: 11/14/2022] Open
Affiliation(s)
- Rhys Heffernan
- Signal Processing Laboratory, Griffith University, Brisbane, QLD, Australia
| | - Yuedong Yang
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, QLD, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD, Australia
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20
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3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction. J Theor Biol 2016; 391:81-7. [PMID: 26718864 DOI: 10.1016/j.jtbi.2015.12.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2015] [Revised: 11/22/2015] [Accepted: 12/01/2015] [Indexed: 11/23/2022]
Abstract
Predicting the native structure of proteins based on half-sphere exposure and contact numbers has been studied deeply within recent years. Online predictors of these vectors and secondary structures of amino acids sequences have made it possible to design a function for the folding process. By choosing variant structures and directs for each secondary structure, a random conformation can be generated, and a potential function can then be assigned. Minimizing the potential function utilizing meta-heuristic algorithms is the final step of finding the native structure of a given amino acid sequence. In this work, Imperialist Competitive algorithm was used in order to accelerate the process of minimization. Moreover, we applied an adaptive procedure to apply revolutionary changes. Finally, we considered a more accurate tool for prediction of secondary structure. The results of the computational experiments on standard benchmark show the superiority of the new algorithm over the previous methods with similar potential function.
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21
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Yan R, Wang X, Xu W, Cai W, Lin J, Li J, Song J. A neural network learning approach for improving the prediction of residue depth based on sequence-derived features. RSC Adv 2016. [DOI: 10.1039/c6ra12275b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Residue depth is a solvent exposure measure that quantitatively describes the depth of a residue from the protein surface.
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Affiliation(s)
- Renxiang Yan
- School of Biological Sciences and Engineering
- Fuzhou University
- Fuzhou 350108
- China
- Fujian Key Laboratory of Marine Enzyme Engineering
| | - Xiaofeng Wang
- College of Mathematics and Computer Science
- Shanxi Normal University
- Linfen 041004
- China
| | - Weiming Xu
- School of Biological Sciences and Engineering
- Fuzhou University
- Fuzhou 350108
- China
| | - Weiwen Cai
- School of Biological Sciences and Engineering
- Fuzhou University
- Fuzhou 350108
- China
| | - Juan Lin
- School of Biological Sciences and Engineering
- Fuzhou University
- Fuzhou 350108
- China
- Fujian Key Laboratory of Marine Enzyme Engineering
| | - Jian Li
- Infection and Immunity Program
- Biomedicine Discovery Institute
- Monash University
- Melbourne
- Australia
| | - Jiangning Song
- Infection and Immunity Program
- Biomedicine Discovery Institute
- Monash University
- Melbourne
- Australia
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22
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Heffernan R, Dehzangi A, Lyons J, Paliwal K, Sharma A, Wang J, Sattar A, Zhou Y, Yang Y. Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins. Bioinformatics 2015; 32:843-9. [DOI: 10.1093/bioinformatics/btv665] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 11/07/2015] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation: Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ.
Results: This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction.
Availability and implementation: The method is available at http://sparks-lab.org.
Contact: yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au
Supplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rhys Heffernan
- Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia,
| | - Abdollah Dehzangi
- Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia,
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia,
- Medical Research Center (MRC), Department of Psychiatry, University of Iowa, Iowa City, USA,
| | - James Lyons
- Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia,
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia,
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia,
- School of Engineering and Physics, University of the South Pacific, Private Mail Bag, Laucala Campus, Suva, Fiji,
| | - Jihua Wang
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, Shandong 253023, China,
| | - Abdul Sattar
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia,
- National ICT Australia (NICTA), Brisbane, Australia and
| | - Yaoqi Zhou
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, Shandong 253023, China,
- Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Yuedong Yang
- Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
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23
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Li Q, Wong YL, Yueqi Lee M, Li Y, Kang C. Solution structure of the transmembrane domain of the mouse erythropoietin receptor in detergent micelles. Sci Rep 2015; 5:13586. [PMID: 26316120 PMCID: PMC4551963 DOI: 10.1038/srep13586] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Accepted: 07/31/2015] [Indexed: 12/19/2022] Open
Abstract
Erythropoiesis is regulated by the erythropoietin receptor (EpoR) binding to its ligand. The transmembrane domain (TMD) and the juxtamembrane (JM) regions of the EpoR are important for signal transduction across the cell membrane. We report a solution NMR study of the mouse erythropoietin receptor (mEpoR) comprising the TMD and the JM regions reconstituted in dodecylphosphocholine (DPC) micelles. The TMD and the C-terminal JM region of the mEpoR are mainly α-helical, adopting a similar structure to those of the human EpoR. Residues from S216 to T219 in mEpoR form a short helix. Relaxation study demonstrates that the TMD of the mEpoR is rigid whilst the N-terminal region preceding the TMD is flexible. Fluorescence spectroscopy and sequence analysis indicate that the C-terminal JM region is exposed to the solvent. Helix wheel result shows that there is hydrophilic patch in the TMD of the mEpoR formed by residues S231, S238 and T242, and these residues might be important for the receptor dimerization.
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Affiliation(s)
- Qingxin Li
- Institute of Chemical &Engineering Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Ying Lei Wong
- Experimental Therapeutics Centre, Agency for Science, Technology and Research (A*STAR), Singapore, 138669 Singapore
| | - Michelle Yueqi Lee
- Experimental Therapeutics Centre, Agency for Science, Technology and Research (A*STAR), Singapore, 138669 Singapore
| | - Yan Li
- Experimental Therapeutics Centre, Agency for Science, Technology and Research (A*STAR), Singapore, 138669 Singapore
| | - CongBao Kang
- Experimental Therapeutics Centre, Agency for Science, Technology and Research (A*STAR), Singapore, 138669 Singapore
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24
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Xu R, Zhou J, Wang H, He Y, Wang X, Liu B. Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 1:S10. [PMID: 25708928 PMCID: PMC4331676 DOI: 10.1186/1752-0509-9-s1-s10] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. RESULTS We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. CONCLUSIONS The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.
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Affiliation(s)
- Ruifeng Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Jiyun Zhou
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Hongpeng Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Yulan He
- School of Engineering & Applied Science, Aston University, Birmingham, UK
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
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25
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newDNA-Prot: Prediction of DNA-binding proteins by employing support vector machine and a comprehensive sequence representation. Comput Biol Chem 2014; 52:51-9. [PMID: 25240115 DOI: 10.1016/j.compbiolchem.2014.09.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2014] [Revised: 09/05/2014] [Accepted: 09/06/2014] [Indexed: 11/21/2022]
Abstract
Identification of DNA-binding proteins is essential in studying cellular activities as the DNA-binding proteins play a pivotal role in gene regulation. In this study, we propose newDNA-Prot, a DNA-binding protein predictor that employs support vector machine classifier and a comprehensive feature representation. The sequence representation are categorized into 6 groups: primary sequence based, evolutionary profile based, predicted secondary structure based, predicted relative solvent accessibility based, physicochemical property based and biological function based features. The mRMR, wrapper and two-stage feature selection methods are employed for removing irrelevant features and reducing redundant features. Experiments demonstrate that the two-stage method performs better than the mRMR and wrapper methods. We also perform a statistical analysis on the selected features and results show that more than 95% of the selected features are statistically significant and they cover all 6 feature groups. The newDNA-Prot method is compared with several state of the art algorithms, including iDNA-Prot, DNAbinder and DNA-Prot. The results demonstrate that newDNA-Prot method outperforms the iDNA-Prot, DNAbinder and DNA-Prot methods. More specific, newDNA-Prot improves the runner-up method, DNA-Prot for around 10% on several evaluation measures. The proposed newDNA-Prot method is available at http://sourceforge.net/projects/newdnaprot/
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26
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Shen HB, Yi DL, Yao LX, Yang J, Chou KC. Knowledge-based computational intelligence development for predicting protein secondary structures from sequences. Expert Rev Proteomics 2014; 5:653-62. [DOI: 10.1586/14789450.5.5.653] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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27
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Sai Ramesh A, Sethumadhavan R, Thiagarajan P. Structure–Function Studies on Non-synonymous SNPs of Chemokine Receptor Gene Implicated in Cardiovascular Disease: A Computational Approach. Protein J 2013; 32:657-65. [DOI: 10.1007/s10930-013-9529-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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28
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Zou C, Gong J, Li H. An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis. BMC Bioinformatics 2013; 14:90. [PMID: 23497329 PMCID: PMC3602657 DOI: 10.1186/1471-2105-14-90] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2012] [Accepted: 03/04/2013] [Indexed: 11/10/2022] Open
Abstract
Background DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence. Results In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively. Conclusions The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.
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Affiliation(s)
- Chuanxin Zou
- Shanghai Key Laboratory of New Drug Design, State Key Laboratory of Bioreactor Engineering, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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29
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PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites. PLoS One 2012; 7:e50300. [PMID: 23209700 PMCID: PMC3510211 DOI: 10.1371/journal.pone.0050300] [Citation(s) in RCA: 222] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2012] [Accepted: 10/18/2012] [Indexed: 12/04/2022] Open
Abstract
The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.
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An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins. PLoS One 2012; 7:e49716. [PMID: 23166753 PMCID: PMC3499040 DOI: 10.1371/journal.pone.0049716] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2012] [Accepted: 10/12/2012] [Indexed: 11/30/2022] Open
Abstract
Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function but also important for the prediction of 3D structure. Here, we present a new integrative framework that combines multiple sequence and structural properties and graph-theoretic network features, followed by an efficient feature selection to improve prediction of zinc-binding sites. We investigate what information can be retrieved from the sequence, structure and network levels that is relevant to zinc-binding site prediction. We perform a two-step feature selection using random forest to remove redundant features and quantify the relative importance of the retrieved features. Benchmarking on a high-quality structural dataset containing 1,103 protein chains and 484 zinc-binding residues, our method achieved >80% recall at a precision of 75% for the zinc-binding residues Cys, His, Glu and Asp on 5-fold cross-validation tests, which is a 10%-28% higher recall at the 75% equal precision compared to SitePredict and zincfinder at residue level using the same dataset. The independent test also indicates that our method has achieved recall of 0.790 and 0.759 at residue and protein levels, respectively, which is a performance better than the other two methods. Moreover, AUC (the Area Under the Curve) and AURPC (the Area Under the Recall-Precision Curve) by our method are also respectively better than those of the other two methods. Our method can not only be applied to large-scale identification of zinc-binding sites when structural information of the target is available, but also give valuable insights into important features arising from different levels that collectively characterize the zinc-binding sites. The scripts and datasets are available at http://protein.cau.edu.cn/zincidentifier/.
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FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model. PLoS One 2012; 7:e43847. [PMID: 22937107 PMCID: PMC3427247 DOI: 10.1371/journal.pone.0043847] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2012] [Accepted: 07/26/2012] [Indexed: 11/26/2022] Open
Abstract
Single amino acid variants (SAVs) are the most abundant form of known genetic variations associated with human disease. Successful prediction of the functional impact of SAVs from sequences can thus lead to an improved understanding of the underlying mechanisms of why a SAV may be associated with certain disease. In this work, we constructed a high-quality structural dataset that contained 679 high-quality protein structures with 2,048 SAVs by collecting the human genetic variant data from multiple resources and dividing them into two categories, i.e., disease-associated and neutral variants. We built a two-stage random forest (RF) model, termed as FunSAV, to predict the functional effect of SAVs by combining sequence, structure and residue-contact network features with other additional features that were not explored in previous studies. Importantly, a two-step feature selection procedure was proposed to select the most important and informative features that contribute to the prediction of disease association of SAVs. In cross-validation experiments on the benchmark dataset, FunSAV achieved a good prediction performance with the area under the curve (AUC) of 0.882, which is competitive with and in some cases better than other existing tools including SIFT, SNAP, Polyphen2, PANTHER, nsSNPAnalyzer and PhD-SNP. The sourcecodes of FunSAV and the datasets can be downloaded at http://sunflower.kuicr.kyoto-u.ac.jp/sjn/FunSAV.
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Lu WW, Huang RB, Wei YT, Meng JZ, Du LQ, Du QS. Statistical energy potential: reduced representation of Dehouck–Gilis–Rooman function by selecting against decoy datasets. Amino Acids 2012; 42:2353-61. [DOI: 10.1007/s00726-011-0977-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2010] [Accepted: 07/06/2011] [Indexed: 11/24/2022]
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Zhang YN, Yu DJ, Li SS, Fan YX, Huang Y, Shen HB. Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features. BMC Bioinformatics 2012; 13:118. [PMID: 22651691 PMCID: PMC3424114 DOI: 10.1186/1471-2105-13-118] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2011] [Accepted: 05/31/2012] [Indexed: 12/23/2022] Open
Abstract
Background Adenosine-5′-triphosphate (ATP) is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. Results In this paper, we propose a novel framework for predicting the proteins’ functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target’s knowledge and multiple sources of descriptors can be complementary. Conclusions Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.
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Affiliation(s)
- Ya-Nan Zhang
- Department of Automation, 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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Zhao X, Li J, Huang Y, Ma Z, Yin M. Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles. Int J Mol Sci 2012; 13:3650-3660. [PMID: 22489173 PMCID: PMC3317733 DOI: 10.3390/ijms13033650] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Revised: 02/21/2012] [Accepted: 03/05/2012] [Indexed: 12/21/2022] Open
Abstract
Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins' functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.
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Affiliation(s)
- Xiaowei Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China; E-Mails: (X.Z.); (J.L.)
- School of Life Sciences, Northeast Normal University, Changchun 130024, China
| | - Jiakui Li
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China; E-Mails: (X.Z.); (J.L.)
| | - Yanxin Huang
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China
| | - Zhiqiang Ma
- School of Life Sciences, Northeast Normal University, Changchun 130024, China
| | - Minghao Yin
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China; E-Mails: (X.Z.); (J.L.)
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Song J, Tan H, Wang M, Webb GI, Akutsu T. TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences. PLoS One 2012; 7:e30361. [PMID: 22319565 PMCID: PMC3271071 DOI: 10.1371/journal.pone.0030361] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2011] [Accepted: 12/14/2011] [Indexed: 12/29/2022] Open
Abstract
Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/.
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Affiliation(s)
- Jiangning Song
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
- National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
- * E-mail: (JS); (GIW); (TA)
| | - Hao Tan
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Mingjun Wang
- National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Geoffrey I. Webb
- Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
- * E-mail: (JS); (GIW); (TA)
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
- * E-mail: (JS); (GIW); (TA)
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Li P, Pok G, Jung KS, Shon HS, Ryu KH. QSE: A new 3-D solvent exposure measure for the analysis of protein structure. Proteomics 2011; 11:3793-801. [PMID: 21761564 DOI: 10.1002/pmic.201100189] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2011] [Revised: 06/29/2011] [Accepted: 07/05/2011] [Indexed: 11/05/2022]
Abstract
Solvent exposure of amino acids measures how deep residues are buried in tertiary structure of proteins, and hence it provides important information for analyzing and predicting protein structure and functions. Existing methods of calculating solvent exposure such as accessible surface area, relative accessible surface area, residue depth, contact number, and half-sphere exposure still have some limitations. In this article, we propose a novel solvent exposure measure named quadrant-sphere exposure (QSE) based on eight quadrants derived from spherical neighborhood. The proposed measure forms a microenvironment around Cα atom as a sphere with a radius of 13 Å, and subdivides it into eight quadrants according to a rectangular coordinate system constructed based on geometric relationships of backbone atoms. The number of neighboring Cα atoms whose labels are the same is given as the QSE value of the center Cα atom at hand. As evidenced by histograms that show very different distributions for different structure configurations, the proposed measure captures local properties that are characteristic for a residue's eight-directional neighborhood within a sphere. Compared with other measures, QSE provides a different view of solvent exposure, and provides information that is specific for different tertiary structure. As the experimental results show, QSE measure can potentially be used in protein structure analysis and predictions.
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Affiliation(s)
- Peipei Li
- College of Electrical and Computer Engineering, Chungbuk National University, Chungbuk, Korea
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Mizianty MJ, Zhang T, Xue B, Zhou Y, Dunker AK, Uversky VN, Kurgan L. In-silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics 2011; 12:245. [PMID: 21682902 PMCID: PMC3212983 DOI: 10.1186/1471-2105-12-245] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Accepted: 06/17/2011] [Indexed: 11/25/2022] Open
Abstract
Background Intrinsically disordered proteins play important roles in various cellular activities and their prevalence was implicated in a number of human diseases. The knowledge of the content of the intrinsic disorder in proteins is useful for a variety of studies including estimation of the abundance of disorder in protein families, classes, and complete proteomes, and for the analysis of disorder-related protein functions. The above investigations currently utilize the disorder content derived from the per-residue disorder predictions. We show that these predictions may over-or under-predict the overall amount of disorder, which motivates development of novel tools for direct and accurate sequence-based prediction of the disorder content. Results We hypothesize that sequence-level aggregation of input information may provide more accurate content prediction when compared with the content extracted from the local window-based residue-level disorder predictors. We propose a novel predictor, DisCon, that takes advantage of a small set of 29 custom-designed descriptors that aggregate and hybridize information concerning sequence, evolutionary profiles, and predicted secondary structure, solvent accessibility, flexibility, and annotation of globular domains. Using these descriptors and a ridge regression model, DisCon predicts the content with low, 0.05, mean squared error and high, 0.68, Pearson correlation. This is a statistically significant improvement over the content computed from outputs of ten modern disorder predictors on a test dataset with proteins that share low sequence identity with the training sequences. The proposed predictive model is analyzed to discuss factors related to the prediction of the disorder content. Conclusions DisCon is a high-quality alternative for high-throughput annotation of the disorder content. We also empirically demonstrate that the DisCon's predictions can be used to improve binary annotations of the disordered residues from the real-value disorder propensities generated by current residue-level disorder predictors. The web server that implements the DisCon is available at http://biomine.ece.ualberta.ca/DisCon/.
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Affiliation(s)
- Marcin J Mizianty
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada
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Song J, Tan H, Boyd SE, Shen H, Mahmood K, Webb GI, Akutsu T, Whisstock JC, Pike RN. Bioinformatic approaches for predicting substrates of proteases. J Bioinform Comput Biol 2011; 9:149-78. [PMID: 21328711 DOI: 10.1142/s0219720011005288] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2010] [Revised: 10/08/2010] [Accepted: 10/09/2010] [Indexed: 11/18/2022]
Abstract
Proteases have central roles in "life and death" processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the "substrate identification" problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction.
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Affiliation(s)
- Jiangning Song
- Department of Biochemistry and Molecular Biology, Monash University, Victoria 3800, Australia.
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39
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Zhang YN, Pan XY, Huang Y, Shen HB. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence. J Theor Biol 2011; 283:44-52. [PMID: 21635901 DOI: 10.1016/j.jtbi.2011.05.023] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2010] [Revised: 04/20/2011] [Accepted: 05/16/2011] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems.
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Affiliation(s)
- Ya-Nan Zhang
- Department of Automation, 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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40
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Jia C, Liu T, Chang AK, Zhai Y. Prediction of mitochondrial proteins of malaria parasite using bi-profile Bayes feature extraction. Biochimie 2011; 93:778-82. [DOI: 10.1016/j.biochi.2011.01.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2010] [Accepted: 01/22/2011] [Indexed: 11/26/2022]
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Prediction of neurotoxins by support vector machine based on multiple feature vectors. Interdiscip Sci 2010; 2:241-6. [PMID: 20658336 DOI: 10.1007/s12539-010-0044-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2010] [Revised: 03/27/2010] [Accepted: 03/29/2010] [Indexed: 10/19/2022]
Abstract
Neurotoxin is a toxin which acts on nerve cells by interacting with membrane proteins. Different neurotoxins have different functions and sources. With much more knowledge of neurotoxins it would be greatly helpful for the development of drug design. The support vector machine (SVM) was used to predict the neurotoxin based on multiple feature vector descriptors, including the amino acid composition, length of the protein sequence, weight of the protein and the evolution information described by position specific scoring matrix (PSSM). After a five-fold cross-validation procedure, the method achieved an accuracy of 100% in discriminating neurotoxins from non-toxins. As for classifying neurotoxins based on their sources and functions, the accuracy was 99.50% and 99.38% respectively. At last, the method yielded a good performance in sub-classification of ion channels inhibitors with the total accuracy of 87.27%. These results indicate that this method outperforms previously described NTXpred method.
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Tian J, Wu N, Chu X, Fan Y. Predicting changes in protein thermostability brought about by single- or multi-site mutations. BMC Bioinformatics 2010; 11:370. [PMID: 20598148 PMCID: PMC2906492 DOI: 10.1186/1471-2105-11-370] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2010] [Accepted: 07/02/2010] [Indexed: 01/24/2023] Open
Abstract
Background An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔG) of thermal denaturation. Results We have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations. Conclusions The performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.
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Affiliation(s)
- Jian Tian
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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43
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Fonseca R, Paluszewski M, Winter P. Protein Structure Prediction Using Bee Colony Optimization Metaheuristic. ACTA ACUST UNITED AC 2010. [DOI: 10.1007/s10852-010-9125-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Song J, Tan H, Shen H, Mahmood K, Boyd SE, Webb GI, Akutsu T, Whisstock JC. Cascleave: towards more accurate prediction of caspase substrate cleavage sites. ACTA ACUST UNITED AC 2010; 26:752-60. [PMID: 20130033 DOI: 10.1093/bioinformatics/btq043] [Citation(s) in RCA: 132] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
MOTIVATION The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates. RESULTS In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P(1) Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship. AVAILABILITY http://sunflower.kuicr.kyoto-u.ac.jp/ approximately sjn/Cascleave/ CONTACT jiangning.song@med.monash.edu.au; takutsu@kuicr.kyoto-u.ac.jp; james; whisstock@med.monash.edu.au SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiangning Song
- Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
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Song J, Tan H, Mahmood K, Law RHP, Buckle AM, Webb GI, Akutsu T, Whisstock JC. Prodepth: predict residue depth by support vector regression approach from protein sequences only. PLoS One 2009; 4:e7072. [PMID: 19759917 PMCID: PMC2742725 DOI: 10.1371/journal.pone.0007072] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2009] [Accepted: 08/20/2009] [Indexed: 11/24/2022] Open
Abstract
Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.
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Affiliation(s)
- Jiangning Song
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
- * E-mail: (JS); (JCW)
| | - Hao Tan
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia
| | - Khalid Mahmood
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia
- ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Clayton, Melbourne, Victoria, Australia
| | - Ruby H. P. Law
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia
| | - Ashley M. Buckle
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia
| | - Geoffrey I. Webb
- Faculty of Information Technology, Monash University, Clayton, Melbourne, Victoria, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
| | - James C. Whisstock
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Melbourne, Victoria, Australia
- ARC Centre of Excellence for Structural and Functional Microbial Genomics, Monash University, Clayton, Melbourne, Victoria, Australia
- * E-mail: (JS); (JCW)
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