Identification of protein hot regions by integrated machine learning algorithm Discovering hot regions in proteinprotein interaction is important for understanding the interactions between proteins, while because of the complexity and timeconsuming of experimental methods, the computational prediction method can be very helpful to improve the efficiency to predict hot regions. In previous researches, some models are based on a single aspect, such as structure, energy, and sequence, each aspect has its advantage and limitations. In this paper, a new method that combing structurebased classification, energybased clustering and sequencebased conservation in evolution is proposed. This method makes full use of three aspects of protein information and compensates for the limitations of using one single aspect information. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.

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.