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

Li Y, Rata I, Chiu SW, Jakobsson E - BMC Struct. Biol. (2010)

Bottom Line: Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 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.Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. yaohang@cs.odu.edu

ABSTRACT

Background: 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.

Results: 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 approximately 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.

Conclusions: 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.

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RMSD of the best-ranked decoy in 11-residue loop targets of Jacobson's decoy sets
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Figure 10: RMSD of the best-ranked decoy in 11-residue loop targets of Jacobson's decoy sets

Mentions: Figure 10 shows the RMSD of the top-ranked decoy by the POC method as well as the individual scoring functions in 11-residue loop targets. One can notice that the individual scoring functions behave differently on various loop targets. There is no superior individual scoring function that can always select the native-like decoy. An individual scoring function may find the native-like decoys in some loop targets but miss the good ones in the other targets. By integrating these scoring functions, the POC method often coincides with the scoring function with correct decoy selection and rarely agrees with a scoring function pointing to an erroneous decoy. More interestingly, the top-ranked decoy in POC method is often better than all the top-ranked decoys in individual scoring functions, as shown for the loop targets 5pti(7:17), 1mla(9:19), 2eng(124:134), and 1aru(297:307).


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

Li Y, Rata I, Chiu SW, Jakobsson E - BMC Struct. Biol. (2010)

RMSD of the best-ranked decoy in 11-residue loop targets of Jacobson's decoy sets
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914074&req=5

Figure 10: RMSD of the best-ranked decoy in 11-residue loop targets of Jacobson's decoy sets
Mentions: Figure 10 shows the RMSD of the top-ranked decoy by the POC method as well as the individual scoring functions in 11-residue loop targets. One can notice that the individual scoring functions behave differently on various loop targets. There is no superior individual scoring function that can always select the native-like decoy. An individual scoring function may find the native-like decoys in some loop targets but miss the good ones in the other targets. By integrating these scoring functions, the POC method often coincides with the scoring function with correct decoy selection and rarely agrees with a scoring function pointing to an erroneous decoy. More interestingly, the top-ranked decoy in POC method is often better than all the top-ranked decoys in individual scoring functions, as shown for the loop targets 5pti(7:17), 1mla(9:19), 2eng(124:134), and 1aru(297:307).

Bottom Line: Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 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.Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA. yaohang@cs.odu.edu

ABSTRACT

Background: 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.

Results: 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 approximately 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.

Conclusions: 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.

Show MeSH