Predicting residue-residue contacts using random forest models

• Published in: Bioinformatics Vol. 27; no. 24; pp. 3379 - 3384
• Main Authors: Li, Yunqi, Fang, Yaping, Fang, Jianwen
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.12.2011
• Subjects: Algorithms; Amino Acids - chemistry; Amino Acids - metabolism; Artificial Intelligence; Biological and medical sciences; Computational Biology - methods; Fundamental and applied biological sciences. Psychology; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Molecular; Proteins - chemistry; Proteins - metabolism; Residue; Models
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btr579

Motivation: Protein residue-residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement. Results: We develop ProC_S3, a set of Random Forest algorithm-based models, for predicting residue-residue contact maps. The models are constructed based on a collection of 1490 non-redundant, high-resolution protein structures using >1280 sequence-based features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L 5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥24). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L 5, L 10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers. Availability: http://www.abl.ku.edu/proc/proc_s3.html. Contact: jwfang@ku.edu Supplementary Information: Supplementary data are available at Bioinformatics online.