Maximum feasibility guideline in the design and analysis of protein folding potentials

Journal of Computational Chemistry
Jaroslaw MellerRon Elber

Abstract

Protein folding potentials are expected to have the lowest energy for the native shape. The Linear Programming (LP) approach achieves exactly that goal for a training set, or indicates that this goal is impossible to obtain. If a solution cannot be found (i.e., the problem is infeasible) two possible routes are possible: (a) choosing a new functional form for the potential, (b) finding the best potential with a feasible subset of the data, and (or) detecting inconsistent subset of the data in the training set. Here, we explore option (b). A simple heuristic for finding an approximate solution to an infeasible set of linear inequalities is outlined. An approximately feasible solution is obtained iteratively, starting from a certain initial guess, by computing a series of analytic centers of the polyhedra defined by all the inequalities satisfied at the subsequent iterations. Standard interior point algorithms for Linear Programming can be used to compute efficiently the analytic center of a polyhedron. We demonstrate how this procedure can be used for the design of folding potentials that are linear in their parameters. The procedure shows an improvement in the quality of the potentials and sometimes points to flaws in the origi...Continue Reading

Citations

Apr 22, 2004·Journal of Molecular Graphics & Modelling·N-V BucheteD Thirumalai
Apr 20, 2004·Current Opinion in Structural Biology·N-V BucheteD Thirumalai
Dec 10, 2002·Current Opinion in Chemical Biology·Jie Liang
Jan 11, 2005·The Journal of Chemical Physics·Alan E van Giessen, John E Straub
Mar 5, 2011·Current Opinion in Structural Biology·Chun Wu, Joan-Emma Shea
Feb 15, 2011·Computational Biology and Chemistry·Kien Ming NgKim Leng Poh
Aug 27, 2010·Proteins·Mookyung CheonCarol K Hall
Sep 16, 2009·Biophysical Journal·Ashwin SubramaniChristodoulos A Floudas

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