skip to main content

SciTech ConnectSciTech Connect

Title: TOWARDS A PROBABILISTIC RECOGNITION CODE FOR PROTEIN-DNA INTERACTIONS

We are investigating the rules that govern protein-DNA interactions, using a statistical mechanics based formalism that is related to the Boltzmann Machine of the neural net literature. Our approach is data-driven, in which probabilistic algorithms are used to model protein-DNA interactions, given SELEX and phage data as input. Under the ''one-to-one'' model for interactions (i.e. one amino acid contacts one base), we can successfully identify the wild-type binding sites of EGR and MIG protein families. The predictions using our method are the same or better than that of methods existing in the literature, however our methodology offers the potential to capitalize in quantitative detail on more data as it becomes available.
Authors:
;
Publication Date:
OSTI Identifier:
768775
Report Number(s):
LA-UR-00-4202
TRN: AH200123%%393
DOE Contract Number:
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Sep 2000
Research Org:
Los Alamos National Lab., NM (US)
Sponsoring Org:
US Department of Energy (US)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ALGORITHMS; AMINO ACIDS; PROTEINS; STATISTICAL MECHANICS; DNA; INTERACTIONS