Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 Prediction of residueresidue distance relationships (e.g. contacts) has become the key direction to advance protein tertiary structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, contact distancedriven templatefree (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction, in addition to an update of other components such as template library, sequence database, and alignment tools. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both templatefree and templatebased protein structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residueresidue features such as coevolution scores to substantially improve interresidue contact distance prediction, which played a decisive role in correctly folding some free modeling and hard templatebased modeling targets from scratch. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system in the CASP13 experiment clearly shows that protein contact distance prediction and model selection driven by powerful deep learning holds the key of solving protein structure prediction problem. However, there are still major challenges in accurately predicting protein contact distance when there are few homologous sequences to generate coevolutionary signals, folding proteins from noisy contact distances, and ranking models of hard targets.

MUFoldContact and TPCref: New Methods for Protein Structure Contact Prediction and Refinement When predicting proteinsu0027 3D structures from their primary sequences, many existing tools use predicted residue contact information, i.e. which residues are in contact with each other. In this paper, we propose two new methods: MUFoldContact, a new twostage multibranch deep neural network for predicting structure contact from protein sequences, and TPCref for refining the result of a contact prediction tool using template information. MUFoldContact uses four independentlytrained deep neural networks to predict residueresidue distances in various ranges, followed by one deep neural network to predict residue contact. TPCref is a novel approach to use protein templates to refine contact prediction generated by a particular contact prediction method. It first finds multiple template sequences based on the target sequence, and use the templatesu0027 structures and the templatesu0027 predicted contact map generated by the contactprediction method to form a target contactmap filter, which is then used to refine the predicted contact map of the target sequence. Experimental results using recently released PDB proteins show that the performance of MUFoldContact was comparable with those of the stateoftheart methods, while TPCref significantly improved the contact prediction results of existing methods.