<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>Kassem, Maher M.</dc:creator>
  <dc:creator>Christoffersen, Lars B.</dc:creator>
  <dc:creator>Cavalli, Andrea</dc:creator>
  <dc:creator>Lindorff-Larsen, Kresten</dc:creator>
  <dc:date>2018-07-24</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Based on the development of new algorithms and growth of sequence databases, it has  recently become possible to build robust higher-order sequence models based on sets of  aligned protein sequences. Such models have proven useful in de novo structure prediction,  where the sequence models are used to find pairs of residues that co-vary during evolution,  and hence are likely to be in spatial proximity in the native protein. The accuracy of these  algorithms, however, drop dramatically when the number of sequences in the alignment is  small. We have developed a method that we termed CE-YAPP (CoEvolution-YAPP), that is  based on YAPP (Yet Another Peak Processor), which has been shown to solve a similar  problem in NMR spectroscopy. By simultaneously performing structure prediction and contact  assignment, CE-YAPP uses structural self-consistency as a filter to remove false positive  contacts. Furthermore, CE-YAPP solves another problem, namely how many contacts to  choose from the ordered list of covarying amino acid pairs. We show that CE-YAPP  consistently improves contact prediction from multiple sequence alignments, in particular for  proteins that are difficult targets. We further show that the structures determined from CE- YAPP are also in better agreement with those determined using traditional methods in  structural biology.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://susi.usi.ch/global/documents/318935</dc:identifier>
  <dc:identifier>https://n2t.net/ark:/12658/srd1318935</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/318935/files/Kassem_SR_2018.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-018-29357-y</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1318935</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>CC BY</dc:rights>
  <dc:source>Scientific reports. - 2018, vol. 8, no. 1, p. 11112</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Computational biophysics</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Molecular modelling</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Protein structure predictions</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/57</dc:subject>
  <dc:title xmlns:ns4="xml" ns4:lang="en">Enhancing coevolution-based contact prediction by imposing structural self-consistency of the contacts</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
