Selecting near-native protein structures from ab initio models using ensemble clustering |
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Authors: | Li Li Huanqian Yan Yonggang Lu |
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Institution: | School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China |
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Abstract: | Background: Ab initio protein structure prediction is to predict the tertiary structure of a protein from its amino acid sequence alone. As an important topic in bioinformatics, considerable efforts have been made on designing the ab initio methods. Unfortunately, lacking of a perfect energy function, it is a difficult task to select a good near-native structure from the predicted decoy structures in the last step. Methods: Here we propose an ensemble clustering method based on k-medoids to deal with this problem. The k-medoids method is run many times to generate clustering ensembles, and then a voting method is used to combine the clustering results. A confidence score is defined to select the final near-native model, considering both the cluster size and the cluster similarity. Results: We have applied the method to 54 single-domain targets in CASP-11. For about 70.4% of these targets, the proposed method can select better near-native structures compared to the SPICKER method used by the I-TASSER server. Conclusions: The experiments show that, the proposed method is effective in selecting the near-native structure from decoy sets for different targets in terms of the similarity between the selected structure and the native structure. |
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Keywords: | near-native structure protein structure prediction ab initio decoy ensemble clustering k-medoids |
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