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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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Average RMSD of the best models selected from 5-top-ranked decoys in Jacobson's loop sets ranging from 4 to 12 residues.
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Figure 9: Average RMSD of the best models selected from 5-top-ranked decoys in Jacobson's loop sets ranging from 4 to 12 residues.

Mentions: Figure 9 presents the average RMSDs of the best of the top-5-ranked decoys in POC method compared to the individual scoring functions in Jacobson's decoy sets for 4- through 12-residue loop targets. The POC method outperforms the individual scoring functions on 4- through 11-residue loop targets and is at least as good as the best individual scoring function (Rosetta) in 12-residue ones. The average RMSDs of the best of the top-5-ranked decoys selected by the POC method are rather close to the baseline formed by the average RMSD values of the best decoys in loop targets of various lengths. More interestingly, for each individual scoring function, there is strong correlation between the selected model's RMSD and the length of the loop target. By contrast, in the POC method, the dependence on the quality of the selected decoys with the length of the loop is hardly noticeable.


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

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

Average RMSD of the best models selected from 5-top-ranked decoys in Jacobson's loop sets ranging from 4 to 12 residues.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Average RMSD of the best models selected from 5-top-ranked decoys in Jacobson's loop sets ranging from 4 to 12 residues.
Mentions: Figure 9 presents the average RMSDs of the best of the top-5-ranked decoys in POC method compared to the individual scoring functions in Jacobson's decoy sets for 4- through 12-residue loop targets. The POC method outperforms the individual scoring functions on 4- through 11-residue loop targets and is at least as good as the best individual scoring function (Rosetta) in 12-residue ones. The average RMSDs of the best of the top-5-ranked decoys selected by the POC method are rather close to the baseline formed by the average RMSD values of the best decoys in loop targets of various lengths. More interestingly, for each individual scoring function, there is strong correlation between the selected model's RMSD and the length of the loop target. By contrast, in the POC method, the dependence on the quality of the selected decoys with the length of the loop is hardly noticeable.

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