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Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.
Teixeira, Pedro L; Mendenhall, Jeff L; Heinze, Sten; Weiner, Brian; Skwark, Marcin J; Meiler, Jens.
Affiliation
  • Teixeira PL; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
  • Mendenhall JL; Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America.
  • Heinze S; Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America.
  • Weiner B; Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America.
  • Skwark MJ; Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville Tennessee, United States of America.
  • Meiler J; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
PLoS One ; 12(5): e0177866, 2017.
Article in En | MEDLINE | ID: mdl-28542325
De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proximity in the structure, are arguably the most effective way to restrict this conformational space. Inverse methods for co-evolutionary analysis predict a global set of position-pair couplings that best explain the observed amino acid co-occurrences, thus distinguishing between evolutionarily explained co-variances and these arising from spurious transitive effects. Here, we show that applying machine learning approaches and custom descriptors improves evolutionary contact prediction accuracy, resulting in improvement of average precision by 6 percentage points for the top 1L non-local contacts. Further, we demonstrate that predicted contacts improve protein folding with BCL::Fold. The mean RMSD100 metric for the top 10 models folded was reduced by an average of 2 Å for a benchmark of 25 membrane proteins.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Models, Molecular / Protein Structure, Secondary / Protein Folding / Machine Learning / Membrane Proteins Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2017 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Models, Molecular / Protein Structure, Secondary / Protein Folding / Machine Learning / Membrane Proteins Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2017 Document type: Article Affiliation country: United States Country of publication: United States