Ranking near-native candidate protein structures via random forest classification

In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein struct...

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Bibliographic Details
Published in:BMC bioinformatics 2019-12, Vol.20 (Suppl 25), p.683-683, Article 683
Main Authors: Wu, Hongjie, Huang, Hongmei, Lu, Weizhong, Fu, Qiming, Ding, Yijie, Qiu, Jing, Li, Haiou
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Candidates
Classification
Cluster Analysis
Clustering
Comparative analysis
Decoys
Forecasts and trends
Free energy
Identification and classification
Methods
Protein Conformation
Protein structural prediction
Protein structure
Proteins
Proteins - chemistry
Proteomics
Random forest
Ranking
SPICKER
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Summary:In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult. To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal. In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3257-8