Natively unstructured regions in proteins identified from contact predictions.

TitleNatively unstructured regions in proteins identified from contact predictions.
Publication TypeJournal Article
Year of Publication2007
AuthorsSchlessinger, A, Punta, M, Rost, B
JournalBioinformatics
Volume23
Issue18
Pagination2376-84
Date Published2007 Sep 15
ISSN1367-4811
KeywordsBinding Sites, Computer Simulation, Models, Chemical, Models, Molecular, Protein Binding, Protein Interaction Mapping, Protein Structure, Tertiary, Proteins, Sequence Analysis, Protein, Structure-Activity Relationship
Abstract

MOTIVATION: Natively unstructured (also dubbed intrinsically disordered) regions in proteins lack a defined 3D structure under physiological conditions and often adopt regular structures under particular conditions. Proteins with such regions are overly abundant in eukaryotes, they may increase functional complexity of organisms and they usually evade structure determination in the unbound form. Low propensity for the formation of internal residue contacts has been previously used to predict natively unstructured regions.RESULTS: We combined PROFcon predictions for protein-specific contacts with a generic pairwise potential to predict unstructured regions. This novel method, Ucon, outperformed the best available methods in predicting proteins with long unstructured regions. Furthermore, Ucon correctly identified cases missed by other methods. By computing the difference between predictions based on specific contacts (approach introduced here) and those based on generic potentials (realized in other methods), we might identify unstructured regions that are involved in protein-protein binding. We discussed one example to illustrate this ambitious aim. Overall, Ucon added quality and an orthogonal aspect that may help in the experimental study of unstructured regions in network hubs.AVAILABILITY: http://www.predictprotein.org/submit_ucon.html.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI10.1093/bioinformatics/btm349
Alternate JournalBioinformatics
PubMed ID17709338
Grant ListR01-LM07329 / LM / NLM NIH HHS / United States
U54-GM072980 / GM / NIGMS NIH HHS / United States
U54-GM074958 / GM / NIGMS NIH HHS / United States