Folding Membrane Proteins by Contacts Inferred from Non-Membrane Proteins and Near-Atomic Level Refinement

Abstract

Computational prediction of membrane protein structure is very challenging due to lack of sufficient solved structures as templates or training data. Recently direct evolutionary coupling (EC) analysis has shed some light on protein contact prediction and accordingly contact-assisted folding. Limited by information within a single protein family, EC analysis is effective only on some very large-sized families. We have developed a deep transfer learning method to predict contacts for membrane proteins not in a large family, by making use of information in thousands of non-membrane protein families. Deep learning can learn complex patterns from large datasets and has recently revolutionized object and speech recognition and the GO game. Our deep learning method learns contact patterns and the complex relationship between contacts and protein features (amino acid property, residue conservation level and residue co-evolution strength) from non-membrane proteins, but can predict membrane protein contacts very well. Tested on 398 non-redundant membrane proteins, the average top L/10 long-range contact prediction accuracy of our method is 0.78, much better than the representative EC method CCMpred (0.52), the CASP11 winner MetaPSICOV (0.61) and the methods trained by only membrane proteins (0.60). When some membrane proteins are added into the training data, we can further improve accuracy by ∼4%. Using only our predicted contacts, we can correctly fold 160 test membrane proteins with RMSD <8A while homology modeling, CCMpred and MetaPSICOV contacts can fold only 10, 60 and 87 proteins, respectively. We further refine the 3D models built from predicted contacts using Upside, a near-atom-level and ultra-fast molecular dynamics method. In addition to membrane-, residue-specific and depth-dependent energy potential, Upside improves model quality by also refining non-membrane portions and refolding loop regions. Initial results indicate that Upside can indeed improve our contact-assisted models.