Journal article Open Access

Machine Learnable Fold Space Representation based on Residue Cluster Classes

Corral-Corral, Ricardo; Del Rio, Gabriel; Chavez, Edgar


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Corral-Corral, Ricardo</dc:creator>
  <dc:creator>Del Rio, Gabriel</dc:creator>
  <dc:creator>Chavez, Edgar</dc:creator>
  <dc:date>2015-12-01</dc:date>
  <dc:description>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 at https://code.google.com/p/pyrcc/.</dc:description>
  <dc:identifier>https://zenodo.org/record/50192</dc:identifier>
  <dc:identifier>10.1016/j.compbiolchem.2015.07.010</dc:identifier>
  <dc:identifier>oai:zenodo.org:50192</dc:identifier>
  <dc:relation>doi:10.5281/zendoo.50193</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>Computational Biology and Chemistry 59 1-7 (2015)</dc:source>
  <dc:subject>Computational Biology</dc:subject>
  <dc:subject>Machine Learning</dc:subject>
  <dc:subject>Protein Structure</dc:subject>
  <dc:subject>CATH</dc:subject>
  <dc:subject>SCOP</dc:subject>
  <dc:subject>Protein Fold Space</dc:subject>
  <dc:subject>Sperner Family</dc:subject>
  <dc:title>Machine Learnable Fold Space Representation based on Residue Cluster Classes</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>
319
184
views
downloads
Views 319
Downloads 184
Data volume 250.7 MB
Unique views 316
Unique downloads 181

Share

Cite as