title: Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach.
abstract: Integral membrane proteins constitute 25-30% of genomes and play crucial roles in many biological processes. However, less than 1% of membrane protein structures are in the Protein Data Bank. In this context, it is important to develop reliable computational methods for predicting the structures of membrane proteins. Here, we present the first application of random forest (RF) for residue-residue contact prediction in transmembrane proteins, which we term as TMhhcp. Rigorous cross-validation tests indicate that the built RF models provide a more favorable prediction performance compared with two state-of-the-art methods, i.e., TMHcon and MEMPACK. Using a strict leave-one-protein-out jackknifing procedure, they were capable of reaching the top L/5 prediction accuracies of 49.5% and 48.8% for two different residue contact definitions, respectively. The predicted residue contacts were further employed to predict interacting helical pairs and achieved the Matthew's correlation coefficients of 0.430 and 0.424, according to two different residue contact definitions, respectively. To facilitate the academic community, the TMhhcp server has been made freely accessible at http://protein.cau.edu.cn/tmhhcp.
authors: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
authors: Xiao-Feng
authors: XF
authors: Wang
authors: Zhen
authors: Z
authors: Chen
authors: Chuan
authors: C
authors: Wang
authors: Ren-Xiang
authors: RX
authors: Yan
authors: Ziding
authors: Z
authors: Zhang
authors: Jiangning
authors: J
authors: Song
mesh_terms: D000465:Algorithms; D000596:Amino Acids; D019295:Computational Biology / Q000379:methods*; D020407:Internet; D008565:Membrane Proteins / Q000737:chemistry*; D011487:Protein Conformation
chemical_list: D000596:Amino Acids; D008565:Membrane Proteins