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.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 768775
- Report Number(s):
- LA-UR-00-4202; TRN: AH200123%%393
- Resource Relation:
- Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Sep 2000
- Country of Publication:
- United States
- Language:
- English
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