Machine Learnable Fold Space Representation based on Residue Cluster Classes. (December 2015)

• Record Type: Journal Article
• Authors: Corral-Corral, Ricardo; Chavez, Edgar; Del Rio, Gabriel
• Is Part Of: Computational biology and chemistry. Volume 59:Part A(2015)
• Page Start: 1
• Page End: 7
• Publication Date: 2015-12
• Subjects: Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.07.010
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract : Graphical abstract: Abstract : Highlights: We implemented a vectorial representation of residues contacts We implemented an efficient statistical test for machine-learnable data Our vectorial model reproduces protein packing A predictor is trained to effectively reproduce CATH and SCOP classifications Our predictor automatically identified inconsistent classification in CATH and SCOP Abstract: Motivation: Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using similarity indexes and/or machine learning approaches, each with different limitations. Results: We propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts. We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine-learning algorithm. Availability: An API is freely available athttps://code.google.com/p/pyrcc/ .