Ab initio folding is one of the most challenging problems in Computational Biology. Recently contact-assisted folding has made some progress on this problem, but it requires accurate inter-residue contact prediction, which by existing methods can only be achieved on some proteins with a very large number of sequence homologs. To deal with proteins without many sequence homologs, we have developed a novel CASP-winning deep learning (DL) method for contact prediction that formulates it similarly as image semantic segmentation and then applies the concatenation of two deep residual neural networks (ResNet). The first ResNet conducts convolutional transformation of 1-dimensional protein features to capture sequential context of one residue and the second conducts convolutional transformation of 2-dimensional features to exploit higher-order residue correlation.
Experimental results suggest that our DL method doubles the accuracy of pure co-evolutionary methods on proteins without many sequence homologs and can fold many more proteins than ever before. Our method is also officially ranked No. 1 in the latest protein structure prediction competition (CASP12) and works well on membrane proteins and inter-protein contact prediction even if trained by single-chain non-membrane proteins.
Dr. Jinbo Xu is a full professor at the Toyota Technological Institute at Chicago, a computer science research and educational institute located at the University of Chicago and a Senior Fellow at the Computational Institute of the University of Chicago. Dr. Xu’s research lies in machine learning, optimization and computational biology (especially protein bioinformatics and biological network analysis). He has developed several popular bioinformatics programs such as the CASP-winning RaptorX (http://raptorx.uchicago.edu) for protein structure prediction and IsoRank/HubAlign for comparative analysis of protein interaction networks. Dr. Xu is the recipient of Alfred P. Sloan Research Fellowship and NSF CAREER award.