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Kuochen Chou - One of the best experts on this subject based on the ideXlab platform.
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predicting the network of substrate enzyme product triads by combining compound similarity and Functional Domain composition
BMC Bioinformatics, 2010Co-Authors: Kuochen Chou, Lei Chen, Kaiyan Feng, Haipeng LiAbstract:Metabolic pathway is a highly regulated network consisting of many metabolic reactions involving substrates, enzymes, and products, where substrates can be transformed into products with particular catalytic enzymes. Since experimental determination of the network of substrate-enzyme-product triad (whether the substrate can be transformed into the product with a given enzyme) is both time-consuming and expensive, it would be very useful to develop a computational approach for predicting the network of substrate-enzyme-product triads. A mathematical model for predicting the network of substrate-enzyme-product triads was developed. Meanwhile, a benchmark dataset was constructed that contains 744,192 substrate-enzyme-product triads, of which 14,592 are networking triads, and 729,600 are non-networking triads; i.e., the number of the negative triads was about 50 times the number of the positive triads. The molecular graph was introduced to calculate the similarity between the substrate compounds and between the product compounds, while the Functional Domain composition was introduced to calculate the similarity between enzyme molecules. The nearest neighbour algorithm was utilized as a prediction engine, in which a novel metric was introduced to measure the "nearness" between triads. To train and test the prediction engine, one tenth of the positive triads and one tenth of the negative triads were randomly picked from the benchmark dataset as the testing samples, while the remaining were used to train the prediction model. It was observed that the overall success rate in predicting the network for the testing samples was 98.71%, with 95.41% success rate for the 1,460 testing networking triads and 98.77% for the 72,960 testing non-networking triads. It is quite promising and encouraged to use the molecular graph to calculate the similarity between compounds and use the Functional Domain composition to calculate the similarity between enzymes for studying the substrate-enzyme-product network system. The software is available upon request.
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a top down approach to enhance the power of predicting human protein subcellular localization hum mploc 2 0
Analytical Biochemistry, 2009Co-Authors: Hongbin Shen, Kuochen ChouAbstract:Predicting subcellular localization of human proteins is a challenging problem, particularly when query proteins may have a multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. In a previous study, we developed a predictor called "Hum-mPLoc" to deal with the multiplex problem for the human protein system. However, Hum-mPLoc has the following shortcomings. (1) The input of accession number for a query protein is required in order to obtain a higher expected success rate by selecting to use the higher-level prediction pathway; but many proteins, such as synthetic and hypothetical proteins as well as those newly discovered proteins without being deposited into databanks yet, do not have accession numbers. (2) Neither Functional Domain nor sequential evolution information were taken into account in Hum-mPLoc, and hence its power may be reduced accordingly. In view of this, a top-down strategy to address these shortcomings has been implemented. The new predictor thus obtained is called Hum-mPLoc 2.0, where the accession number for input is no longer needed whatsoever. Moreover, both the Functional Domain information and the sequential evolution information have been fused into the predictor by an ensemble classifier. As a consequence, the prediction power has been significantly enhanced. The web server of Hum-mPLoc2.0 is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/hum-multi-2/.
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quatident a web server for identifying protein quaternary structural attribute by fusing Functional Domain and sequential evolution information
Journal of Proteome Research, 2009Co-Authors: Hongbin Shen, Kuochen ChouAbstract:Many proteins exist in vivo as oligomers with various different quaternary structural attributes rather than as single individual chains. They are the structural bases of various marvelous biologic...
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predicting protein fold pattern with Functional Domain and sequential evolution information
Journal of Theoretical Biology, 2009Co-Authors: Hongbin Shen, Kuochen ChouAbstract:The fold pattern of a protein is one level deeper than its structural classification, and hence is more challenging and complicated for prediction. Many efforts have been made in this regard, but so far all the reported success rates are still under 70%, indicating that it is extremely difficult to enhance the success rate even by 1% or 2%. To address this problem, here a novel approach is proposed that is featured by combining the Functional Domain information and the sequential evolution information through a fusion ensemble classifier. The predictor thus developed is called PFP-FunDSeqE. Tests were performed for identifying proteins among their 27 fold patterns. Compared with the existing predictors tested by a same stringent benchmark dataset, the new predictor can, for the first time, achieve over 70% success rate. The PFP-FunDSeqE predictor is freely available to the public as a web server at http://www.csbio.sjtu.edu.cn/bioinf/PFP-FunDSeqE/.
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predicting protein structural class by Functional Domain composition
Biochemical and Biophysical Research Communications, 2004Co-Authors: Kuochen ChouAbstract:Abstract The Functional Domain composition is introduced to predict the structural class of a protein or Domain according to the following classification: all-α, all-β, α/β, α + β, μ (multi-Domain), σ (small protein), and ρ (peptide). The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins and Domains with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 98%. In contrast to this, the corresponding rates obtained by the simple geometry approaches based on the amino acid composition were only 36–39%. This indicates that using the Functional Domain composition to represent the sample of a protein for statistical prediction is very promising, and that the Functional type of a Domain is closely correlated with its structural class.
Yudong Cai - One of the best experts on this subject based on the ideXlab platform.
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a new hybrid approach to predict subcellular localization of proteins by incorporating gene ontology
Biochemical and Biophysical Research Communications, 2003Co-Authors: Kuochen Chou, Yudong CaiAbstract:Based on the recent development in the gene ontology and Functional Domain databases, a new hybridization approach is developed for predicting protein subcellular location by combining the gene product, Functional Domain, and quasi-sequence-order effects. As a showcase, the same prokaryotic and eukaryotic datasets, which were studied by many previous investigators, are used for demonstration. The overall success rate by the jackknife test for the prokaryotic set is 94.7% and that for the eukaryotic set 92.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. The very high success rates also reflect the fact that the subcellular localization of a protein is closely correlated with: (1). the biological objective to which the gene or gene product contributes, (2). the biochemical activity of a gene product, and (3). the place in the cell where a gene product is active.
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nearest neighbour algorithm for predicting protein subcellular location by combining Functional Domain composition and pseudo amino acid composition
Biochemical and Biophysical Research Communications, 2003Co-Authors: Yudong Cai, Kuochen ChouAbstract:Abstract In this paper, based on the approach by combining the “Functional Domain composition” [K.C. Chou, Y. D. Cai, J. Biol. Chem. 277 (2002) 45765] and the pseudo-amino acid composition [K.C. Chou, Proteins Struct. Funct. Genet. 43 (2001) 246; Correction Proteins Struct. Funct. Genet. 2044 (2001) 2060], the Nearest Neighbour Algorithm (NNA) was developed for predicting the protein subcellular location. Very high success rates were observed, suggesting that such a hybrid approach may become a useful high-throughput tool in the area of bioinformatics and proteomics.
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support vector machines for predicting membrane protein types by using Functional Domain composition
Biophysical Journal, 2003Co-Authors: Yudong Cai, Guoping Zhou, Kuochen ChouAbstract:Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type II membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the Functional Domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.
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using Functional Domain composition and support vector machines for prediction of protein subcellular location
Journal of Biological Chemistry, 2002Co-Authors: Kuochen Chou, Yudong CaiAbstract:Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "Functional Domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered Functional Domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.
Hongbin Shen - One of the best experts on this subject based on the ideXlab platform.
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a top down approach to enhance the power of predicting human protein subcellular localization hum mploc 2 0
Analytical Biochemistry, 2009Co-Authors: Hongbin Shen, Kuochen ChouAbstract:Predicting subcellular localization of human proteins is a challenging problem, particularly when query proteins may have a multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. In a previous study, we developed a predictor called "Hum-mPLoc" to deal with the multiplex problem for the human protein system. However, Hum-mPLoc has the following shortcomings. (1) The input of accession number for a query protein is required in order to obtain a higher expected success rate by selecting to use the higher-level prediction pathway; but many proteins, such as synthetic and hypothetical proteins as well as those newly discovered proteins without being deposited into databanks yet, do not have accession numbers. (2) Neither Functional Domain nor sequential evolution information were taken into account in Hum-mPLoc, and hence its power may be reduced accordingly. In view of this, a top-down strategy to address these shortcomings has been implemented. The new predictor thus obtained is called Hum-mPLoc 2.0, where the accession number for input is no longer needed whatsoever. Moreover, both the Functional Domain information and the sequential evolution information have been fused into the predictor by an ensemble classifier. As a consequence, the prediction power has been significantly enhanced. The web server of Hum-mPLoc2.0 is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/hum-multi-2/.
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quatident a web server for identifying protein quaternary structural attribute by fusing Functional Domain and sequential evolution information
Journal of Proteome Research, 2009Co-Authors: Hongbin Shen, Kuochen ChouAbstract:Many proteins exist in vivo as oligomers with various different quaternary structural attributes rather than as single individual chains. They are the structural bases of various marvelous biologic...
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predicting protein fold pattern with Functional Domain and sequential evolution information
Journal of Theoretical Biology, 2009Co-Authors: Hongbin Shen, Kuochen ChouAbstract:The fold pattern of a protein is one level deeper than its structural classification, and hence is more challenging and complicated for prediction. Many efforts have been made in this regard, but so far all the reported success rates are still under 70%, indicating that it is extremely difficult to enhance the success rate even by 1% or 2%. To address this problem, here a novel approach is proposed that is featured by combining the Functional Domain information and the sequential evolution information through a fusion ensemble classifier. The predictor thus developed is called PFP-FunDSeqE. Tests were performed for identifying proteins among their 27 fold patterns. Compared with the existing predictors tested by a same stringent benchmark dataset, the new predictor can, for the first time, achieve over 70% success rate. The PFP-FunDSeqE predictor is freely available to the public as a web server at http://www.csbio.sjtu.edu.cn/bioinf/PFP-FunDSeqE/.
Sunghou Kim - One of the best experts on this subject based on the ideXlab platform.
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crystal structure of a nicotinate phosphoribosyltransferase from thermoplasma acidophilum
Journal of Biological Chemistry, 2005Co-Authors: Dong Hae Shin, Natalia Oganesyan, Jaru Jancarik, Hisao Yokota, Rosalind Kim, Sunghou KimAbstract:We have determined the crystal structure of nicotinate phosphoribosyltransferase from Themoplasma acidophilum (TaNAPRTase). The TaNAPRTase has three Domains, an N-terminal Domain, a central Functional Domain, and a unique C-terminal Domain. The crystal structure revealed that the Functional Domain has a type II phosphoribosyltransferase fold that may be a common architecture for both nicotinic acid and quinolinic acid (QA) phosphoribosyltransferases (PRTase) despite low sequence similarity between them. Unlike QAPRTase, TaNAPRTase has a unique extra C-terminal Domain containing a zinc knuckle-like motif containing 4 cysteines. The TaNAPRTase forms a trimer of dimers in the crystal. The active site pocket is formed at dimer interfaces. The complex structures with phosphoribosylpyrophosphate (PRPP) and nicotinate mononucleotide (NAMN) showed, surprisingly, that Functional residues lining on the active site of TaNAPRTase are quite different from those of QAPRTase, although their substrates are quite similar to each other. The phosphate moiety of PRPP and NAMN is anchored to the phosphate-binding loops formed by backbone amides, as found in many alpha/beta barrel enzymes. The pyrophosphate moiety of PRPP is located at the entrance of the active site pocket, whereas the nicotinate moiety of NAMN is located deep inside. Interestingly, the nicotinate moiety of NAMN is intercalated between highly conserved aromatic residues Tyr(21) and Phe(138). Careful structural analyses combined with other NAPRTase sequence subfamilies reveal that TaNAPRTase represents a unique sequence subfamily of NAPRTase. The structures of TaNAPRTase also provide valuable insight for other sequence subfamilies such as pre-B cell colony-enhancing factor, known to have nicotinamide phosphoribosyltransferase activity.
Jorge Babul - One of the best experts on this subject based on the ideXlab platform.
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intrinsically disordered regions of the dna binding Domain of human foxp1 facilitate Domain swapping
Journal of Molecular Biology, 2020Co-Authors: Exequiel Medina, Pablo Villalobos, Elizabeth A Komives, George L Hamilton, Hugo Sanabria, Cesar A Ramirezsarmiento, Jorge BabulAbstract:Abstract Forkhead box P (FoxP) proteins are unique transcription factors that spatiotemporally regulate gene expression by tethering two chromosome loci together via Functional Domain-swapped dimers formed through their DNA-binding Domains. Further, the differential kinetics on this dimerization mechanism underlie an intricate gene regulation network at physiological conditions. Nonetheless, poor understanding of the structural dynamics and steps of the association process impedes to link the Functional Domain swapping to human-associated diseases. Here, we have characterized the DNA-binding Domain of human FoxP1 by integrating single-molecule Forster resonance energy transfer and hydrogen–deuterium exchange mass spectrometry data with molecular dynamics simulations. Our results confirm the formation of a previously postulated Domain-swapped (DS) FoxP1 dimer in solution and reveal the presence of highly populated, heterogeneous, and locally disordered dimeric intermediates along the dimer dissociation pathway. The unique features of FoxP1 provide a glimpse of how intrinsically disordered regions can facilitate Domain swapping oligomerization and other tightly regulated association mechanisms relevant in biological processes.
