Helix–helix interfaces and ligand binding

Kurochkina, Natalya; Choekyi, Tsering

doi:10.1016/j.jtbi.2011.05.014

Cited by 19 publications

(4 citation statements)

References 58 publications

(65 reference statements)

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“…Using graphic approaches to study biological problems can provide an intuitive picture or useful insights for revealing complicated relations in these systems, as demonstrated by many previous studies on a series of important biological topics, such as enzyme-catalyzed reactions [64], [65], [66], [67], inhibition of HIV-1 reverse transcriptase [68], [69], protein folding kinetics [70], drug metabolism systems [71], and using wenxiang diagram or graph [72] to study protein-protein interactions [73], [74], [75]. To introduce graphic approach for the current study, let us use the conversion scheme [18] to transform the nucleosome and non-nucleosome sequences into the numerical vectors (cf.…”

Section: Resultsmentioning

confidence: 99%

iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

et al. 2012

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Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor's code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design.

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Section: Resultsmentioning

confidence: 99%

iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

et al. 2012

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“…Currently, graph theory has been widely used to solve various biological and chemical problems, such as enzyme-catalyzed reactions, 14 drug metabolism systems, 15 and protein-protein interactions. 16,17 For the current research, each protein complex is firstly modeled as a node-weighted graph of interactions (i.e. topological structure), where nodes represent proteins and edges denote protein-protein interactions.…”

Section: Characterization Of Protein Complexesmentioning

confidence: 99%

Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm

et al. 2013

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In the post-genome era, one of the most important and challenging tasks is to identify the subcellular localizations of protein complexes, and further elucidate their functions in human health with applications to understand disease mechanisms, diagnosis and therapy. Although various experimental approaches have been developed and employed to identify the subcellular localizations of protein complexes, the laboratory technologies fall far behind the rapid accumulation of protein complexes. Therefore, it is highly desirable to develop a computational method to rapidly and reliably identify the subcellular localizations of protein complexes. In this study, a novel method is proposed for predicting subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm. Protein complexes are modeled as weighted graphs containing nodes and edges, where nodes represent proteins, edges represent protein-protein interactions and weights are descriptors of protein primary structures. Some topological structure features are proposed and adopted to characterize protein complexes based on graph theory. Random forest is employed to construct a model and predict subcellular localizations of protein complexes. Accuracies on a training set by a 10-fold cross-validation test for predicting plasma membrane/membrane attached, cytoplasm and nucleus are 84.78%, 71.30%, and 82.00%, respectively. And accuracies for the independent test set are 81.31%, 69.95% and 81.00%, respectively. These high prediction accuracies exhibit the state-of-the-art performance of the current method. It is anticipated that the proposed method may become a useful high-throughput tool and plays a complementary role to the existing experimental techniques in identifying subcellular localizations of mammalian protein complexes. The source code of Matlab and the dataset can be obtained freely on request from the authors.

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“…Protein triclique topological structure has been also studied before, for example, the tripartite complexes, such as CASK participated CASK-Velis-Mint 1 complex and CASK-Velis-Caskin 1 complex (Tabuchi et al, 2002), and gH-gL-gQ complex and gH-gL-gO complex (Mori et al, 2004). Furthermore, graphical approaches can be used to provide an intuitive picture or useful insights for helping analyzing complicated relations in biological problems (Lin and Lapointe, 2013), as demonstrated by many previous studies on a series of important biological topics, such as enzymecatalyzed reactions (Chou and Forsen, 1980;Zhou and Deng, 1984;Chou, 1989;Andraos, 2008;Lin and Neet, 1990;Chen et al, 2010a), protein pathway networks (Chen et al, 2010b;Huang et al, 2011), inhibition of HIV-1 reverse transcriptase (Althaus et al, 1993a,b), inhibition kinetics of processive nucleic acid polymerases and nucleases (Chou et al, 1994), drug metabolism systems (Chou, 2010), and using wenxiang diagram or graph (Chou et al, 2011) to study protein-protein interactions (Zhou, 2011a;Kurochkina and Choekyi, 2011;Zhou, 2011b;Zhou and Huang, 2013).…”

Section: Introductionmentioning

confidence: 99%

k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks

Liu

Chen

2014

Journal of Theoretical Biology

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Many studies are aimed at identifying dense clusters/subgraphs from protein-protein interaction (PPI) networks for protein function prediction. However, the prediction performance based on the dense clusters is actually worse than a simple guilt-by-association method using neighbor counting ideas. This indicates that the local topological structures and properties of PPI networks are still open to new theoretical investigation and empirical exploration. We introduce a novel topological structure called k-partite cliques of protein interactions-a functionally coherent but not-necessarily dense subgraph topology in PPI networks-to study PPI networks. A k-partite protein clique is a maximal k-partite clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI's maximal k-partite cliques, we propose to transform PPI networks into induced K-partite graphs where edges exist only between the partites. Then, we present a maximal k-partite clique mining (MaCMik) algorithm to enumerate maximal k-partite cliques from K-partite graphs. Our MaCMik algorithm is then applied to a yeast PPI network. We observed interesting and unusually high functional coherence in k-partite protein cliques-the majority of the proteins in k-partite protein cliques, especially those in the same partites, share the same functions, although k-partite protein cliques are not restricted to be dense compared with dense subgraph patterns or (quasi-)cliques. The idea of k-partite protein cliques provides a novel approach of characterizing PPI networks, and so it will help function prediction for unknown proteins.

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Helix–helix interfaces and ligand binding

Cited by 19 publications

References 58 publications

iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm

k-Partite cliques of protein interactions: A novel subgraph topology for functional coherence analysis on PPI networks

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