Recognizing protein folds with discriminative learning

Sloan-Kettering Inst Can Research, New York NY

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Abstract

DESCRIPTION (provided by applicant): One (1) of the fundamental problems in computational biology is the prediction of a protein's 3D structural class -- that is, recognition of its fold from its linear sequence of amino acids. The proposed project aims to develop computational methods and tools for recognizing protein folds. The first specific aim involves building and delivering to the scientific community a web-based, discriminative fold-recognition software engine. This tool will instantiate for the first time in a user-friendly form a discriminative fold-recognition algorithm. This type of algorithm has been described and repeatedly validated in the scientific literature over the past 5 years, but no easy-to-use software tools yet exists to bring this technology to the end user. The second specific aim improves upon existing fold-recognition algorithms by exploiting the inherently multiclass nature of the problem. Previous approaches have treated each fold class independently, thereby sacrificing statistical power. This project will produce algorithms and software that dramatically improve our ability to recognize, from the primary amino acid sequence, subtle structural similarities among proteins.