Protein structure prediction by a data-level parallel algorithm

Authors:
X. Zhang;D. Waltz;J. P. Mesirov
Affiliations:
Thinking Machines Corporation, 245 First Street, Cambridge, MA and Brandeis University, 415 South Street, Walthham, MA;Thinking Machines Corporation, 245 First Street, Cambridge, MA and Brandeis University, 415 South Street, Walthham, MA;Thinking Machines Corporation
Venue:
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Year:
1989

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Abstract

We have developed a software system, PHI-PSI, on the Connection Machine that uses a parallel algorithm to retrieve and use information from a database of 112 known protein structures (selected from the Brookhaven Protein Databank) to predict the structures of other proteins. The &phgr; and &psgr; angles of each amino acid (the angles each amino acid forms with its immediate neighbors) in a protein are used to represent its 3-D structure. PHI-PSI's algorithm is based on the idea of Memory-based reasoning (MBR) [10] and extends it to include a recursive procedure to refine its initial prediction and a “window” of varying sizes to look at different contexts of an input. PHI-PSI has been tested with all the available data. Initial results show that it performs better than distribution-based guesses for most of the &phgr; and &psgr; angle values.