Applying undertaker cost functions to model quality assessment

Abstract

Undertaker is a program designed to help predict protein structure using alignments to proteins of known structure and fragment assembly. The program generates conformations and uses cost functions to select the best structures from among the generated conformations. This paper describes the use of Undertaker's cost functions for model quality assessment. We achieve an accuracy that is similar to other methods, without using consensus-based techniques. Adding consensus-based features further improves our approach substantially. We report several correlation measures, including a new weighted version of Kendall's tau (tau(3)) and show model quality assessment results superior to previously published results on all correlation measures when using only models with no missing atoms.

Figure 1

SystematicOpt(C). This algorithm tries to improve the average correlation by taking each component in turn and rebalancing it against the remaining components. T is the threshold for convergence. Initial weights for each cost function are the inverse of the pooled standard deviation of the corresponding cost function.

Figure 2

GreedyOpt(C). This algorithm seeks only to include those cost functions which are useful for model quality assessment, while ignoring the others. The result will be simpler if only a few cost function need to be included. The greedy optimization method seeks to identify individual components in C that can improve average correlation and to add them to a pool of useful components, P, only if they can improve average correlation. Initial weights for each cost function are the inverse of the pooled standard deviation of the corresponding cost function.