Literature DB >> 16649265

Sequence conservation and correlation measures in protein structure prediction.

K Hatrick1, W R Taylor.   

Abstract

The rapid elucidation of protein sequences has allowed multiple sequence alignments to be calculated for a wide variety of proteins. Such alignments reveal positions that exhibit amino acid conservation--either of specific chemical groups in active and binding sites or of the more chemically inert hydrophobic residues that contribute to the protein core. The latter can provide constraints on the position of the protein chain and any local periodicity can suggest the type of secondary structure. Conservation measures, however, cannot provide specific pairwise packing information (each conserved hydrophobic position might pack against any other). However, if correlated changes between positions were observed then specific pairs of residue could be identified as interacting and therefore probably spatially adjacent. Most 'observations' of correlated changes have been anecdotal and of the few systematic studies that have been made, most have mistakenly incorporated a strong bias towards selecting conserved positions. When the conservation effect is separated (as best as possible) then little correlation signal remains to help identify adjacent positions.

Mesh:

Substances:

Year:  1994        PMID: 16649265     DOI: 10.1016/0097-8485(94)85019-4

Source DB:  PubMed          Journal:  Comput Chem        ISSN: 0097-8485


  3 in total

1.  Direct correlation analysis improves fold recognition.

Authors:  Michael I Sadowski; Katarzyna Maksimiak; William R Taylor
Journal:  Comput Biol Chem       Date:  2011-08-22       Impact factor: 2.877

2.  Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.

Authors:  Tatjana Braun; Julia Koehler Leman; Oliver F Lange
Journal:  PLoS Comput Biol       Date:  2015-12-29       Impact factor: 4.475

3.  PconsFold: improved contact predictions improve protein models.

Authors:  Mirco Michel; Sikander Hayat; Marcin J Skwark; Chris Sander; Debora S Marks; Arne Elofsson
Journal:  Bioinformatics       Date:  2014-09-01       Impact factor: 6.937

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.