Predicting protein-protein interactions from primary structure

By Joel R. Bock, David A. Gough. Bioinform., 2001

550 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a91e30dda8d868ef37b8fa73cfb145163690bfe2

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In Molecular Biology

Citations in the corpus

Estimation of Simultaneously Sparse and Low Rank Matrices (2012, background): “In addition to this, it is often suggested that interactions between two proteins are governed by a small set of factors, such as surface accessible amino acid side chains (Bock & Gough, 2001), which motivates the estimation of a low-rank representation.”
Prediction‐based fingerprints of protein–protein interactions (2006, background): “20–23 From the point of view of the representation (feature space) used to capture characteristic signatures (or finger-prints) of interaction interfaces, one may distinguish two main groups of approaches.”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, unknown): “These CTD features have been widely used for predicting protein folds [59], protein–protein interactions [60], and protein functional families [61] at accuracy levels of 74–100, 77–81, 67–99 %, respectively.”
Predicting protein-protein interactions using signature products (2005, methodology): “Using these same datasets we benchmark our algorithm and provide comparisons with the works of Sprinzak and Margalit (2001); Bock and Gough (2001, 2003) and Jansen et al .”
ProFET: Feature engineering captures high-level protein functions (2015, background): “Examples for such predictions include protein–protein interactions (Bock and Gough, 2001; Cheng and Baldi, 2007), discriminating outer membrane proteins (Gromiha and Suwa, 2005), membrane topology (Nugent and Jones, 2009), subcellular localization (Hua and Sun, 2001) and more.”
Inferring domain-domain interactions from protein-protein interactions (2002, methodology): “Other computational methods to predict protein–protein interaction have been presented on the basis of different principles, including the interaction domain pair profile method (Rain et al. 2001; Wojcik and Schachter 2001) and the support vector machine learning method ( Bock and Gough 2001 ).”
Supervised classification and mathematical optimization (2013, background): “…Biology and Medicine, such as classification of gene expression data [102] [245], homology detection [143], protein-protein interaction prediction [28] [171], abnormal brain activity classification [55] and cancer diagnosis [110] [165]; Machine Vision [75] [188]; Agriculture [179]; or Chemistry…”
Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence (2006, background): “Composition, transition and distribution features represent the amino acid distribution patterns of a specific structural or physicochemical property along a protein or peptide sequence (5,25), which have been used for recognition of protein folds (5) and prediction of protein–protein interactions (”