title: Learning Protein Folding Energy Functions.
abstract: A critical open problem inab initioprotein folding is protein energy function design, which pertains to defining the energy of protein conformations in a way that makes folding most efficient and reliable. In this paper, we address this issue as a weight optimization problem and utilize a machine learning approach, learning-to-rank, to solve this problem. We investigate the ranking-via-classification approach, especially the RankingSVM method and compare it with the state-of-the-art approach to the problem using the MINUIT optimization package. To maintain the physicality of the results, we impose non-negativity constraints on the weights. For this we develop two efficient non-negative support vector machine (NNSVM) methods, derived from L2-norm SVM and L1-norm SVMs, respectively. We demonstrate an energy function which maintains the correct ordering with respect to structure dissimilarity to the native state more often, is more efficient and reliable for learning on large protein sets, and is qualitatively superior to the current state-of-the-art energy function.
authors.affiliation: College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332.
authors.forename: Wei
authors.initials: W
authors.lastname: Guan
authors.affiliation: Georgia Tech Research Institute, Atlanta, Georgia 30318.
authors.forename: Arkadas
authors.initials: A
authors.lastname: Ozakin
authors.affiliation: College of Computing, Georgia Institute of Technology, Atlanta, Georgia 30332.
authors.forename: Alexander
authors.initials: A
authors.lastname: Gray
authors.affiliation: Department of Biology, Georgia Institute of Technology, Atlanta, GA.
authors.forename: Jose
authors.initials: J
authors.lastname: Borreguero
authors.affiliation: Department of Biology, Georgia Institute of Technology, Atlanta, GA.
authors.forename: Shashi
authors.initials: S
authors.lastname: Pandit
authors.affiliation: Department of Biology, Georgia Institute of Technology, Atlanta, GA.
authors.forename: Anna
authors.initials: A
authors.lastname: Jagielska
authors.affiliation: Department of Biology, Georgia Institute of Technology, Atlanta, GA.
authors.forename: Liliana
authors.initials: L
authors.lastname: Wroblewska
authors.affiliation: Department of Biology, Georgia Institute of Technology, Atlanta, GA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Skolnick
keywords: ab initio protein folding; energy function; learning-to-rank; non-negativity constrained SVM optimization; support vector machine