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Shen X, Zhang S, Long J, Chen C, Wang M, Cui Z, Chen B, Tan T. A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction. J Chem Inf Model 2023; 63:4277-4290. [PMID: 37399293 DOI: 10.1021/acs.jcim.3c00273] [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: 07/05/2023]
Abstract
Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique local spatial configuration bound to the substrate at the active center of the enzyme determines the catalytic ability of enzymes and plays an important role in the catalytic site prediction. As a suitable tool, the graph neural network can better understand and identify the residue sites with unique local spatial configurations due to its remarkable ability to characterize the three-dimensional structural features of proteins. Consequently, a novel model for predicting enzyme catalytic sites has been developed, which incorporates a uniquely designed adaptive edge-gated graph attention neural network (AEGAN). This model is capable of effectively handling sequential and structural characteristics of proteins at various levels, and the extracted features enable an accurate description of the local spatial configuration of the enzyme active site by sampling the local space around candidate residues and special design of amino acid physical and chemical properties. To evaluate its performance, the model was compared with existing catalytic site prediction models using different benchmark datasets and achieved the best results on each benchmark dataset. The model exhibited a sensitivity of 0.9659, accuracy of 0.9226, and area under the precision-recall curve (AUPRC) of 0.9241 on the independent test set constructed for evaluation. Furthermore, the F1-score of this model is nearly four times higher than that of the best-performing similar model in previous studies. This research can serve as a valuable tool to help researchers understand protein sequence-structure-function relationships while facilitating the characterization of novel enzymes of unknown function.
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Affiliation(s)
- Xiaowei Shen
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Shiding Zhang
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Jianyu Long
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Changjing Chen
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Meng Wang
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Ziheng Cui
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Biqiang Chen
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, 100029, Beijing, China
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Galeb HA, Lamantia A, Robson A, König K, Eichhorn J, Baldock SJ, Ashton MD, Baum JV, Mort RL, Robinson BJ, Schacher FH, Chechik V, Taylor AM, Hardy JG. The Polymerization of Homogentisic Acid in Vitro as a Model for Pyomelanin Formation. MACROMOL CHEM PHYS 2022. [DOI: 10.1002/macp.202100489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Hanaa A. Galeb
- Department of Chemistry Lancaster University Lancaster LA1 4YB United Kingdom
- Department of Chemistry Science and Arts College, Rabigh Campus King Abdulaziz University Jeddah 21577 Saudi Arabia
| | - Angelo Lamantia
- Department of Physics Lancaster University Lancaster LA1 4YW United Kingdom
| | - Alexander Robson
- Department of Chemistry Lancaster University Lancaster LA1 4YB United Kingdom
| | - Katja König
- Institut für Organische und Makromolekulare Chemie Friedrich‐Schiller‐Universität Jena Lessingstraße 8 Jena 07743 Germany
| | - Jonas Eichhorn
- Institut für Organische und Makromolekulare Chemie Friedrich‐Schiller‐Universität Jena Lessingstraße 8 Jena 07743 Germany
| | - Sara J. Baldock
- Department of Chemistry Lancaster University Lancaster LA1 4YB United Kingdom
| | - Mark D. Ashton
- Department of Chemistry Lancaster University Lancaster LA1 4YB United Kingdom
| | - John V. Baum
- Department of Chemistry Lancaster University Lancaster LA1 4YB United Kingdom
| | - Richard L. Mort
- Division of Biomedical and Life Sciences Lancaster University Lancaster LA1 4YG United Kingdom
| | - Benjamin J. Robinson
- Department of Physics Lancaster University Lancaster LA1 4YW United Kingdom
- Materials Science Institute Lancaster University Lancaster LA1 4YB United Kingdom
| | - Felix H. Schacher
- Institut für Organische und Makromolekulare Chemie Friedrich‐Schiller‐Universität Jena Lessingstraße 8 Jena 07743 Germany
| | - Victor Chechik
- Department of Chemistry University of York Heslington, York YO10 5DD United Kingdom
| | - Adam M. Taylor
- Lancaster Medical School Lancaster University Lancaster LA1 4YW United Kingdom
| | - John G. Hardy
- Department of Chemistry Lancaster University Lancaster LA1 4YB United Kingdom
- Materials Science Institute Lancaster University Lancaster LA1 4YB United Kingdom
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Alapati R, Shuvo MH, Bhattacharya D. SPECS: Integration of side-chain orientation and global distance-based measures for improved evaluation of protein structural models. PLoS One 2020; 15:e0228245. [PMID: 32053611 PMCID: PMC7018003 DOI: 10.1371/journal.pone.0228245] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 01/11/2020] [Indexed: 12/23/2022] Open
Abstract
Significant advancements in the field of protein structure prediction have necessitated the need for objective and robust evaluation of protein structural models by comparing predicted models against the experimentally determined native structures to quantitate their structural similarities. Existing protein model versus native similarity metrics either consider the distances between alpha carbon (Cα) or side-chain atoms for computing the similarity. However, side-chain orientation of a protein plays a critical role in defining its conformation at the atomic-level. Despite its importance, inclusion of side-chain orientation in structural similarity evaluation has not yet been addressed. Here, we present SPECS, a side-chain-orientation-included protein model-native similarity metric for improved evaluation of protein structural models. SPECS combines side-chain orientation and global distance based measures in an integrated framework using the united-residue model of polypeptide conformation for computing model-native similarity. Experimental results demonstrate that SPECS is a reliable measure for evaluating structural similarity at the global level including and beyond the accuracy of Cα positioning. Moreover, SPECS delivers superior performance in capturing local quality aspect compared to popular global Cα positioning-based metrics ranging from models at near-experimental accuracies to models with correct overall folds-making it a robust measure suitable for both high- and moderate-resolution models. Finally, SPECS is sensitive to minute variations in side-chain χ angles even for models with perfect Cα trace, revealing the power of including side-chain orientation. Collectively, SPECS is a versatile evaluation metric covering a wide spectrum of protein modeling scenarios and simultaneously captures complementary aspects of structural similarities at multiple levels of granularities. SPECS is freely available at http://watson.cse.eng.auburn.edu/SPECS/.
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Affiliation(s)
- Rahul Alapati
- Department of Computer Science and Software Engineering, Auburn University, Auburn, Alabama, United States of America
| | - Md. Hossain Shuvo
- Department of Computer Science and Software Engineering, Auburn University, Auburn, Alabama, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science and Software Engineering, Auburn University, Auburn, Alabama, United States of America
- Department of Biological Sciences, Auburn University, Auburn, Alabama, United States of America
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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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Choudhary P, Kumar S, Bachhawat AK, Pandit SB. CSmetaPred: a consensus method for prediction of catalytic residues. BMC Bioinformatics 2017; 18:583. [PMID: 29273005 PMCID: PMC5741869 DOI: 10.1186/s12859-017-1987-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 12/05/2017] [Indexed: 01/27/2023] Open
Abstract
Background Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc. Results Both meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization. Conclusions The benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/. Electronic supplementary material The online version of this article (10.1186/s12859-017-1987-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Preeti Choudhary
- Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India
| | - Shailesh Kumar
- Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India.,Laboratory of Biochemistry and Genetics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anand Kumar Bachhawat
- Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India
| | - Shashi Bhushan Pandit
- Department of Biological Sciences, Indian Institute of Science Education and Research, Mohali, Knowledge City, Sector 81, SAS Nagar, Manuali PO 140306, India.
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Su MG, Weng JTY, Hsu JBK, Huang KY, Chi YH, Lee TY. Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions. BMC SYSTEMS BIOLOGY 2017; 11:132. [PMID: 29322920 PMCID: PMC5763307 DOI: 10.1186/s12918-017-0506-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Protein post-translational modification (PTM) plays an essential role in various cellular processes that modulates the physical and chemical properties, folding, conformation, stability and activity of proteins, thereby modifying the functions of proteins. The improved throughput of mass spectrometry (MS) or MS/MS technology has not only brought about a surge in proteome-scale studies, but also contributed to a fruitful list of identified PTMs. However, with the increase in the number of identified PTMs, perhaps the more crucial question is what kind of biological mechanisms these PTMs are involved in. This is particularly important in light of the fact that most protein-based pharmaceuticals deliver their therapeutic effects through some form of PTM. Yet, our understanding is still limited with respect to the local effects and frequency of PTM sites near pharmaceutical binding sites and the interfaces of protein-protein interaction (PPI). Understanding PTM’s function is critical to our ability to manipulate the biological mechanisms of protein. Results In this study, to understand the regulation of protein functions by PTMs, we mapped 25,835 PTM sites to proteins with available three-dimensional (3D) structural information in the Protein Data Bank (PDB), including 1785 modified PTM sites on the 3D structure. Based on the acquired structural PTM sites, we proposed to use five properties for the structural characterization of PTM substrate sites: the spatial composition of amino acids, residues and side-chain orientations surrounding the PTM substrate sites, as well as the secondary structure, division of acidity and alkaline residues, and solvent-accessible surface area. We further mapped the structural PTM sites to the structures of drug binding and PPI sites, identifying a total of 1917 PTM sites that may affect PPI and 3951 PTM sites associated with drug-target binding. An integrated analytical platform (CruxPTM), with a variety of methods and online molecular docking tools for exploring the structural characteristics of PTMs, is presented. In addition, all tertiary structures of PTM sites on proteins can be visualized using the JSmol program. Conclusion Resolving the function of PTM sites is important for understanding the role that proteins play in biological mechanisms. Our work attempted to delineate the structural correlation between PTM sites and PPI or drug-target binding. CurxPTM could help scientists narrow the scope of their PTM research and enhance the efficiency of PTM identification in the face of big proteome data. CruxPTM is now available at http://csb.cse.yzu.edu.tw/CruxPTM/. Electronic supplementary material The online version of this article (10.1186/s12918-017-0506-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Min-Gang Su
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Julia Tzu-Ya Weng
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei, 110, Taiwan
| | - Kai-Yao Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan.,Department of Medical Research, Hsinchu Mackay Memorial Hospital, Hsinchu City, 300, Taiwan
| | - Yu-Hsiang Chi
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, 320, Taiwan. .,Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, 320, Taiwan.
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Huang KY, Su MG, Kao HJ, Hsieh YC, Jhong JH, Cheng KH, Huang HD, Lee TY. dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins. Nucleic Acids Res 2015; 44:D435-46. [PMID: 26578568 PMCID: PMC4702878 DOI: 10.1093/nar/gkv1240] [Citation(s) in RCA: 136] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 11/02/2015] [Indexed: 01/23/2023] Open
Abstract
Owing to the importance of the post-translational modifications (PTMs) of proteins in regulating biological processes, the dbPTM (http://dbPTM.mbc.nctu.edu.tw/) was developed as a comprehensive database of experimentally verified PTMs from several databases with annotations of potential PTMs for all UniProtKB protein entries. For this 10th anniversary of dbPTM, the updated resource provides not only a comprehensive dataset of experimentally verified PTMs, supported by the literature, but also an integrative interface for accessing all available databases and tools that are associated with PTM analysis. As well as collecting experimental PTM data from 14 public databases, this update manually curates over 12 000 modified peptides, including the emerging S-nitrosylation, S-glutathionylation and succinylation, from approximately 500 research articles, which were retrieved by text mining. As the number of available PTM prediction methods increases, this work compiles a non-homologous benchmark dataset to evaluate the predictive power of online PTM prediction tools. An increasing interest in the structural investigation of PTM substrate sites motivated the mapping of all experimental PTM peptides to protein entries of Protein Data Bank (PDB) based on database identifier and sequence identity, which enables users to examine spatially neighboring amino acids, solvent-accessible surface area and side-chain orientations for PTM substrate sites on tertiary structures. Since drug binding in PDB is annotated, this update identified over 1100 PTM sites that are associated with drug binding. The update also integrates metabolic pathways and protein-protein interactions to support the PTM network analysis for a group of proteins. Finally, the web interface is redesigned and enhanced to facilitate access to this resource.
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Affiliation(s)
- Kai-Yao Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Min-Gang Su
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Hui-Ju Kao
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Yun-Chung Hsieh
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Jhih-Hua Jhong
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Kuang-Hao Cheng
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Hsien-Da Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan
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PINGU: PredIction of eNzyme catalytic residues usinG seqUence information. PLoS One 2015; 10:e0135122. [PMID: 26261982 PMCID: PMC4532418 DOI: 10.1371/journal.pone.0135122] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Accepted: 07/17/2015] [Indexed: 11/19/2022] Open
Abstract
Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU) for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues) in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues) in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.
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Xiao X, Hui MJ, Liu Z, Qiu WR. iCataly-PseAAC: Identification of Enzymes Catalytic Sites Using Sequence Evolution Information with Grey Model GM (2,1). J Membr Biol 2015; 248:1033-41. [DOI: 10.1007/s00232-015-9815-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 06/06/2015] [Indexed: 11/25/2022]
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Chen YJ, Lu CT, Su MG, Huang KY, Ching WC, Yang HH, Liao YC, Chen YJ, Lee TY. dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation. Nucleic Acids Res 2014; 43:D503-11. [PMID: 25399423 PMCID: PMC4383970 DOI: 10.1093/nar/gku1176] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Given the increasing number of proteins reported to be regulated by S-nitrosylation (SNO), it is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to regulate diverse pathophysiological processes by altering protein function, stability, and conformation change in various cancers and human disorders. Due to its importance in regulating protein functions and cell signaling, dbSNO (http://dbSNO.mbc.nctu.edu.tw) is extended as a resource for exploring structural environment of SNO substrate sites and regulatory networks of S-nitrosylated proteins. An increasing interest in the structural environment of PTM substrate sites motivated us to map all manually curated SNO peptides (4165 SNO sites within 2277 proteins) to PDB protein entries by sequence identity, which provides the information of spatial amino acid composition, solvent-accessible surface area, spatially neighboring amino acids, and side chain orientation for 298 substrate cysteine residues. Additionally, the annotations of protein molecular functions, biological processes, functional domains and human diseases are integrated to explore the functional and disease associations for S-nitrosoproteome. In this update, users are allowed to search a group of interested proteins/genes and the system reconstructs the SNO regulatory network based on the information of metabolic pathways and protein-protein interactions. Most importantly, an endogenous yet pathophysiological S-nitrosoproteomic dataset from colorectal cancer patients was adopted to demonstrate that dbSNO could discover potential SNO proteins involving in the regulation of NO signaling for cancer pathways.
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Affiliation(s)
- Yi-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan
| | - Cheng-Tsung Lu
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Min-Gang Su
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Kai-Yao Huang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Wei-Chieh Ching
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan
| | - Hsiao-Hsiang Yang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan
| | - Yen-Chen Liao
- Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan Department of Chemistry, National Taiwan University, Taipei 114, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan Department of Chemistry, National Taiwan University, Taipei 114, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 320, Taiwan
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EXIA2: web server of accurate and rapid protein catalytic residue prediction. BIOMED RESEARCH INTERNATIONAL 2014; 2014:807839. [PMID: 25295274 PMCID: PMC4177735 DOI: 10.1155/2014/807839] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 05/27/2014] [Accepted: 06/11/2014] [Indexed: 11/18/2022]
Abstract
We propose a method (EXIA2) of catalytic residue prediction based on protein structure without needing homology information. The method is based on the special side chain orientation of catalytic residues. We found that the side chain of catalytic residues usually points to the center of the catalytic site. The special orientation is usually observed in catalytic residues but not in noncatalytic residues, which usually have random side chain orientation. The method is shown to be the most accurate catalytic residue prediction method currently when combined with PSI-Blast sequence conservation. It performs better than other competing methods on several benchmark datasets that include over 1,200 enzyme structures. The areas under the ROC curve (AUC) on these benchmark datasets are in the range from 0.934 to 0.968.
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