Approximate counting with deterministic guarantees for affinity computation

Abstract

Computational Protein Design aims at rationally designing amino-acid sequences that fold into a given three-dimensional structure and that will bestow the designed protein with desirable properties/ functions. Usual criteria for design include stability of the designed protein and affinity between it and a ligand of interest. However, estimating the affinity between two molecules requires to compute the partition function, a #P-complete problem. Because of its extreme computational cost, bio-physicists have designed the K ∗ algorithm, which combines Best-First A ∗ search with dominance analysis to provide an estimate of the partition function with deterministic guarantees of quality. In this paper, we show that it is possible to speed up search and keep reasonable memory requirement using a Cost Function Network approach combining Depth First Search with arc consistency based lower bounds. We describe our algorithm and compare our first results to the CPD-dedicated software Osprey 2.0.