Open AccessSelecting near‐native protein structures from ab initio models using ensemble clustering
Author(s) -
Li Li,
Yan Huanqian,
Lu Yonggang
Publication year - 2018
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-018-0158-1
Subject(s) - decoy , casp , cluster analysis , computer science , protein structure prediction , similarity (geometry) , medoid , ab initio , data mining , protein structure , cluster (spacecraft) , artificial intelligence , pattern recognition (psychology) , algorithm , chemistry , image (mathematics) , biochemistry , receptor , organic chemistry , programming language
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.