High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling
Jing Yang1, Richard Jang, Yang Zhang
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Motivation:
Residue-residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize.
Results:
We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers. Tested on 60 non-redundant polytopic proteins using a strict leave-one-out cross-validation protocol, MemBrain achieves an average accuracy of 62%, which is 12.5% higher than the current best method from the literature. When applied to 13 recently solved G protein-coupled receptors, the MemBrain contact predictions helped increase the TM-score of the I-TASSER models by 37% in the transmembrane region. The number of foldable cases (TM-score >0.5) increased by 100%, where all G protein-coupled receptor templates and homologous templates with sequence identity >30% were excluded. These results demonstrate significant progress in contact prediction and a potential for contact-driven structure modeling of transmembrane proteins.
Availability:
www.csbio.sjtu.edu.cn/bioinf/MemBrain/
