PredMP: A Web Resource for Computationally Predicted Membrane Proteins via Deep Learning
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
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AbstractExperimental determination of membrane protein (MP) structures is challenging as they are often too large for nuclear magnetic resonance (NMR) experiments and difficult to crystallize. Currently there are only about 510 non-redundant MPs with solved structures in Protein Data Bank (PDB). To elucidate the MP structures computationally, we developed a novel web resource, denoted as PredMP (http://126.96.36.199:3001/#/proteinindex), that delivers one-dimensional (1D) annotation of the membrane topology and secondary structure, two-dimensional (2D) prediction of the contact/distance map, together with three-dimensional (3D) modeling of the MP structure in the lipid bilayer, for each MP target from a given model organism. The precision of the computationally constructed MP structures is leveraged by state-of-the-art deep learning methods as well as cutting-edge modeling strategies. In particular, (i) we annotate 1D property via DeepCNF (Deep Convolutional Neural Fields) that not only models complex sequence-structure relationship but also interdependency between adjacent property labels; (ii) we predict 2D contact/distance map through Deep Transfer Learning which learns the patterns as well as the complex relationship between contacts/distances and protein features from non-membrane proteins; and (iii) we model 3D structure by feeding its predicted contacts and secondary structure to the Crystallography & NMR System (CNS) suite combined with a membrane burial potential that is residue-specific and depth-dependent. PredMP currently contains more than 2,200 multi-pass transmembrane proteins (length<700 residues) from Human. These transmembrane proteins are classified according to IUPHAR/BPS Guide, which provides a hierarchical organization of receptors, channels, transporters, enzymes and other drug targets according to their molecular relationships and physiological functions. Among these MPs, we estimated that our approach could predict correct folds for 1,345-1,871 targets including a few hundred new folds, which shall facilitate the discovery of drugs targeting at MPs.
CitationWang S, Fei S, Zongan W, Li Y, Zhao F, et al. (2018) PredMP: A Web Resource for Computationally Predicted Membrane Proteins via Deep Learning. Biophysical Journal 114: 573a. Available: http://dx.doi.org/10.1016/j.bpj.2017.11.3136.