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

<p>Abstract</p> <p>Background</p> <p>Accurate 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.</p> <p>Results</p> <p>We 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.</p> <p>Conclusions</p> <p>By 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.</p

Information-Based Torsion Angle Potential for Proteins and Applications

139 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.Statistical potentials offer a significant bioinformation-based alternative to physical potentials in protein structure prediction and design. As an introduction we survey the aspects and methods involved in the derivation and applicability of various statistical potentials. The statistical potential created in this work is based on a reduced protein representation described by the residue sequence and the backbone structure including the Cbeta atoms. This potential has two components constructed separately. The component characterizes the protein interactions between the neighboring residues along the sequence. As structural variables we use the principal internal degrees of freedom, which are the backbone &amp;phiv; and psi dihedral angles corresponding to each residue. We quantify the correlation between every pair of adjacent dihedrals (&amp;phiv;i, psii) and (psii, &amp;phiv; i+1) in the context of local residue sequence (Ri--1, Ri, Ri+1) by extracting and processing the related information from a database of protein loops structures. We focus in detail on an important application of our local potential: the sequence design of peptidic inhibitors for HIV-1 protease. The second component of our statistical potential is designed mainly for assessing the &quot;non-local&quot; protein interactions and is obtained from a distance-based potential applied to our particular protein representation. In association with an adequate search technique we find an efficient way to assemble the two parts of our statistical potential, which appropriately combines both local and non-local contributions. We successfully apply the resulting method to the protein loop modeling.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD