title: Machine Learnable Fold Space Representation based on Residue Cluster Classes.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  An API is freely available at https://code.google.com/p/pyrcc/.
authors.affiliation: Department of Biochemistry and Structural Biology, Instituto de Fisiologa Celular, Universidad Nacional Autónoma de México, México D. F., México.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Corral-Corral
authors.affiliation: Centro de Investigación Científica y de Educación Superior de Ensenada, México.
authors.forename: Edgar
authors.initials: E
authors.lastname: Chavez
authors.affiliation: Department of Biochemistry and Structural Biology, Instituto de Fisiologa Celular, Universidad Nacional Autónoma de México, México D. F., México. Electronic address: gdelrio@ifc.unam.mx.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Del Rio
mesh_terms: D016000:Cluster Analysis; D000069550:Machine Learning; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins