Expert Hybrid System for Prediction of Protein Structure

Period of Performance: 04/01/1998 - 09/30/1998

Unknown

Phase 1 SBIR

Recipient Firm

Orincon Corp.
4770 Eastgate Mall
San Diego, CA 92121
Principal Investigator

Abstract

Knowledge-based methods for prediction of protein secondary and tertiary structure are still not accurate enough to warrant their application to practical problems. Integration of different computational paradigms may produce a substantial improvement in their performance. ORINCON proposes to design, construct, and test a multiple expert hybrid system for prediction of protein structure. In Phase I of this work, ORINCON will adapt its MultEx paradigm integration software architecture for the task of secondary structure prediction. The proposed system will integrate a number of standard prediction algorithms and will additionally include nonlocal information about the protein that is normally not used in secondary structure prediction, such as amino acid composition, partial experimental knowledge about the structure, etc. Such information is related to long-range interactions between the amino acid residues, which are thought to influence both tertiary and secondary structure. The aim of the Phase I work is to demonstrate the superiority of the hybrid approach and the feasibility of exploring the encoded empirical model to provide the means for improving prediction accuracy through the inclusion on nonlocal data. The results of this work will lead to recommendations for the Phase II program, in which the Protein MultEx system, a commercially viable software package for the prediction of protein secondary structure, will be fully developed and an analogous approach will be used for integration of tertiary structure prediction methods. PROPOSED COMMERCIAL APPLICATION Methods for protein structure prediction are needed to extract structural/functional information from the growing database of protein sequences. The Protein MultEx system will be a commercially viable product that should find application in development and testing of new protein structure prediction methods, in structural biology, and in structure-based drug design.