Analysis of protein sequence/structure similarity relationships

Hin Hark Gan, Rebecca A. Perlow, Sharmili Roy, Joy Ko, Min Wu, Jing Huang, Shixiang Yan, Angelo Nicoletta, Jonathan Vafai, Ding Sun, Lihua Wang, Joyce E. Noah, Samuela Pasquali, Tamar Schlick

Research output: Contribution to journalArticlepeer-review

Abstract

Current analyses of protein sequence/structure relationships have focused on expected similarity relationships for structurally similar proteins. To survey and explore the basis of these relationships, we present a general sequence/structure map that covers all combinations of similarity/dissimilarity relationships and provide novel energetic analyses of these relationships. To aid our analysis, we divide protein relationships into four categories: expected/unexpected similarity (S and S?) and expected/unexpected dissimilarity (D and D?) relationships. In the expected similarity region S, we show that trends in the sequence/structure relation can be derived based on the requirement of protein stability and the energetics of sequence and structural changes. Specifically, we derive a formula relating sequence and structural deviations to a parameter characterizing protein stiffness; the formula fits the data reasonably well, We suggest that the absence of data in region S? (high structural but low sequence similarity) is due to unfavorable energetics. In contrast to region S, region D? (high sequence but low structural similarity) is well-represented by proteins that can accommodate large structural changes. Our analyses indicate that there are several categories of similarity relationships and that protein energetics provide a basis for understanding these relationships.

Original languageEnglish (US)
Pages (from-to)2781-2791
Number of pages11
JournalBiophysical journal
Volume83
Issue number5
DOIs
StatePublished - Nov 1 2002

ASJC Scopus subject areas

  • Biophysics

Fingerprint

Dive into the research topics of 'Analysis of protein sequence/structure similarity relationships'. Together they form a unique fingerprint.

Cite this