title: Statistical significance of sequence patterns in proteins.
abstract: I discuss three recent developments in sequence analysis by the statistical method of scores. First is the identification of segments of high aggregate score in a single protein sequence. Charge clusters and hyper-charge runs are prime examples. Proteins containing hyper-charge runs are principally associated with DNA and RNA processing, chromatin structure, ion storage and exchange, and protein complex assembly. Second is the protein sequence comparisons identifying common segments having high total similarity scores. These are illustrated by comparisons within the family of prokaryotic heat shock 70 kDa proteins. Third is the scoring protocols applied to the inverse folding problem.
authors.affiliation: Department of Mathematics, Stanford University, CA 94305-2125, USA.
authors.forename: S
authors.initials: S
authors.lastname: Karlin
mesh_terms: D000818:Animals; D006801:Humans; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D017421:Sequence Analysis / Q000706:statistics & numerical data*
chemical_list: D011506:Proteins