Linear programming optimization and a double statistical filter for protein threading protocols

The design of scoring functions (or potentials) for threading, differentiat ing native-like from non-native structures with a limited computational cos t, is an active field of research. We revisit two widely used families of t hreading potentials: the pairwise and profile models. To design optimal sco ring functions we use linear programming (LP). The LP protocol makes it pos sible to measure the difficulty of a particular training set in conjunction with a specific form of the scoring function. Gapless threading demonstrat es that pair potentials have larger prediction capacity compared with profi le energies. However, alignments with gaps are easier to compute with profi le potentials. We therefore search and propose a new profile model with com parable prediction capacity to contact potentials. A protocol to determine optimal energy parameters for gaps, using LP, is also presented. A statisti cal test, based on a combination of local and global Z-scores, is employed to filter out false-positives. Extensive tests of the new protocol are pres ented. The new model provides an efficient alternative for threading with p air energies, maintaining comparable accuracy. The code, databases, and a p rediction server are available at http://www.tc.cornell.edu/ CBIO/loopp. (C ) 2001 Wiley-Liss, Inc.