Predicting residue-residue contacts using random forest models.
BACKGROUND
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.


BACKGROUND
http://www.abl.ku.edu/proc/proc_s3.html.


BACKGROUND
jwfang@ku.edu


BACKGROUND
Supplementary data are available at Bioinformatics online.
Applied Bioinformatics Laboratory, The University of Kansas, Lawrence, KS 66047, USA.
Yunqi
Y
Li
Yaping
Y
Fang
Jianwen
J
Fang
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry* / Q000378:metabolism; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D000596:Amino Acids; D011506:Proteins