Browsing by Author "Yu, Chin-Sheng"

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CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation
(2014-06-09)
Yu, Chin-Sheng
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Cheng, Chih-Wen
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Su, Wen-Chi
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Chang, Kuei-Chung
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Huang, Shao-Wei
CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.
MI6: Metal Ion-Binding Site Prediction and Docking Server
(2016-12)
Lin, Yu-Feng
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Cheng, Chih-Wen
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Shih, Chung-Shiuan
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Hwang, Jenn-Kang
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Yu, Chin-Sheng
The structure of a protein determines its biological function(s) and its interactions with other factors; the binding regions tend to be conserved in sequence and structure, and the interacting residues involved are usually in close 3D space. The Protein Data Bank currently contains more than 110 000 protein structures, approximately one-third of which contain metal ions. Identifying and characterizing metal ion-binding sites is thus essential for investigating a protein\'s function(s) and interactions. However, experimental approaches are time-consuming and costly. The web server reported here was built to predict metal ion-binding residues and to generate the predicted metal ion-bound 3D structure. Binding templates have been constructed for regions that bind 12 types of metal ion-binding residues have been used to construct binding templates. The templates include residues within 3.5 angstrom of the metal ion, and the fragment transformation method was used for structural comparison between query proteins and templates without any data training. Through the adjustment of scoring functions, which are based on the similarity of structure and binding residues. Twelve kinds of metal ions (Ca2+, Cu2+, Fe3+, Mn2+, Zn2+, Cd2+, Fe2+, Ni2+, Hg2+, Co2+, and Cu+) binding residues prediction are supported. MIB also provides the metal ions docking after prediction. The MIB server is available at http://bioinfo.cmu.edu.tw/MIB/.
Predicting disulfide connectivity patterns
(2007-05-01)
Lu, Chih-Hao
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Chen, Yu-Ching
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Yu, Chin-Sheng
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Hwang, Jenn-Kang
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生物資訊及系統生物研究所
Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. Therefore, the ability to infer disulfide connectivity from protein sequences will be valuable in structural modeling and functional analysis. However, to predict disulfide connectivity directly from sequences presents a challenge to computational biologists due to the nonlocal nature of disulfide bonds, i.e., the close spatial proximity of the cysteine pair that forms the disulfide bond does not necessarily imply the short sequence separation of the cysteine residues. Recently, Chen and Hwang (Proteins 2005;61:507-512) treated this problem as a multiple class classification by defining each distinct disulfide pattern as a class. They used multiple support vector machines based on a variety of sequence features to predict the disulfide patterns. Their results compare favorably with those in the literature for a benchmark dataset sharing less than 30% sequence identity. However, since the number of disulfide patterns grows rapidly when the number of disulfide bonds increases, their method performs unsatisfactorily for the cases of large number of disulfide bonds. In this work, we propose a novel method to represent disulfide connectivity in terms of cysteine pairs, instead of disulfide patterns. Since the number of bonding states of the cysteine pairs is independent of that of disulfide bonds, the problem of class explosion is avoided. The bonding states of the cysteine pairs are predicted using the support vector machines together with the genetic algorithm optimization for feature selection. The complete disulfide patterns are then determined from the connectivity matrices that are constructed from the predicted bonding states of the cysteine pairs. Our approach outperforms the current approaches in the literature.
Predicting Flavin and Nicotinamide Adenine Dinucleotide-Binding Sites in Proteins Using the Fragment Transformation Method
(2015-01-01)
Lu, Chih-Hao
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Yu, Chin-Sheng
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Lin, Yu-Feng
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Chen, Jin-Yi
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生物資訊及系統生物研究所
We developed a computational method to identify NAD- and FAD-binding sites in proteins. First, we extracted from the Protein Data Bank structures of proteins that bind to at least one of these ligands. NAD-/FAD-binding residue templates were then constructed by identifying binding residues through the ligand-binding database BioLiP. The fragment transformation method was used to identify structures within query proteins that resembled the ligand-binding templates. By comparing residue types and their relative spatial positions, potential binding sites were identified and a ligand-binding potential for each residue was calculated. Setting the false positive rate at 5%, our method predicted NAD- and FAD-binding sites at true positive rates of 67.1% and 68.4%, respectively. Our method provides excellent results for identifying FAD- and NAD-binding sites in proteins, and the most important is that the requirement of conservation of residue types and local structures in the FAD- and NAD-binding sites can be verified.
Prediction of Metal Ion-Binding Sites in Proteins Using the Fragment Transformation Method
(2012-06-18)
Lu, Chih-Hao
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Lin, Yu-Feng
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Lin, Jau-Ji
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Yu, Chin-Sheng
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生物資訊及系統生物研究所
The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion-binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion-binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion-binding sites. Six types of metal ion-binding templates-those involving Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, and Zn2+-were constructed using the residues within 3.5 angstrom of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion-binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.
Prediction of protein subcellular localization
(2006-08-15)
Yu, Chin-Sheng
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Chen, Yu-Ching
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Lu, Chih-Hao
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Hwang, Jenn-Kang
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生物科技學系
Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
Prediction of Protein Subcellular Localizations
(2008)
Yu, Chin-Sheng
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Hwang, Jenn-Kang
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生物科技學系
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Department of Biological Science and Technology
The support vector machine (SVM) method based on n-peptide composition (Yu et al, Proteins: Struct. Funct. Genet. 2003:50:531-536) is used to predict the subcellular localizations of proteins. For an unbiased assessment of the results, we apply our approach to two independent data sets: one set consisting of two parts (Reinhardt and Hubbard, Nucleic Acids Res. 1998; 26:2230-2236): the prokaryotic set includes 997 protein sequences in three categories and the eukaryotic set includes 2427. sequences in four localization categories; another set comprising 2191 proteins in 12 subcellular localizations (Chou and Cai, J. Biol. Chem. 2002; 277:45765-45769). Our approach provides excellent results for both data sets. For the first data set, our approach gives an overall prediction accuracy 93.2% for prokaryotic sequences, 88.1% for eukaryotic sequences. Our approach also yields significantly better Matthews correlation coefficient for each subcellular localization than the existing approaches. For the second data set, our approach achieves an overall prediction accuracy 83.2%, which is also around 10% higher than the best existing result. Our approaches should be valuable in the high throughput analysis of genomics and proteomics.