Improving ResidueResidue Contacts Prediction from Protein Sequences Using RNNBased LSTM Network Accurate prediction of residueresidue contacts is of crucial importance for protein structure predictions and function studies. The advantages of coevolutionbased methods to predict residueresidue contacts have been made manifest in the past decade. However, the prediction of residueresidue contacts remains a challenging task since these methods need abundant homologous protein sequences to obtain higher precision. Benefiting from the rapid development and the everwidening use of deep learning methods, we attempted to use an intelligent method to predict residueresidue contacts at an intraprotein level. The backbone of the deep learning method is a recurrent neural network (RNN) with 5layer long shortterm memory (LSTM) cells. We describe this computational model for predicting residueresidue contacts, evaluate the method on three datasets of protein chain, and report the predictive performance in obtaining 45.72%, 40.35%, 39.06% prediction precisions on long range at cutoff value L, respectively, which shows a small improvement. In addition, we also display the effects of amino acid features involved in predicting residueresidue contacts by using three unsupervised machine learning methods. The performance of our method trained on a small dataset of protein sequences sheds light on the potential usefulness of applying recurrent neural network into residueresidue contact prediction.

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.