Improving predicted protein loop structure ranking using a Pareto-optimality consensus method

Abstract

BackgroundAccurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.ResultsWe have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of ~20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods.ConclusionsBy integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology

“…Loop modeling is generally reliable for up to ten residues, but there is greater uncertainty as the length of loops increases[19, 21]. The same also applies for modeling N- and C-termini that are only anchored on one side by the rest of the structure.…”

A Data-Driven Evolutionary Algorithm for Mapping Multibasin Protein Energy Landscapes

“…Evolutionary algorithms (EAs) originating in the EC community have been shown powerful for challenging problems, such as loop modeling, protein-ligand binding, and even de novo structure prediction (Shehu, 2013). While they are often designed to serve as black-box optimization tools for NP-hard problems, equipping EAs with domain-specific expertise, such as state-of-the-art protein representations and energy functions, has resulted in performance that rivals that of MC-based methods (Li et al, 2010;Olson and Shehu, 2012a and b;Li and Yaseen, 2013;Shehu, 2013, 2014).…”

A multiscale hybrid evolutionary algorithm to obtain sample-based representations of multi-basin protein energy landscapes

“…While a review is beyond the scope of this paper, there is recently renewed interest in stochastic optimization under the umbrella of evolutionary computation for protein structure modeling [34]. Evolutionary algorithms (EAs), relying on the key idea of evolving a population of structures towards low-energy ones over generations, have been shown to be powerful for challenging problems, such as loop modeling and de novo structure prediction, when equipped with domain-specific expertise on representations of protein chains and state-of-the-art energy functions [19,20,[27][28][29][30][31].…”