Prediction of Protein B-factor Profiles based on Bidirectional Long Short-Term Memory Network

Abstract

B-factor is a measure of ray attenuation or scattering caused by atomic thermal motion during X-ray diffraction of protein crystal structure. B-factor reflects the vibration of atoms; hence, it is the most common experimental descriptor of protein flexibility and has been extensively applied in the studies of protein dynamics, screening of bioactive small molecules, and protein engineering. The prediction of B-factor profiles has considerable significance for analyzing the dynamic properties of unknown proteins. Deep learning technology has developed rapidly in recent years and has been widely implemented in many research fields, especially structural biology. In this paper, a deep neural network model based on bidirectional long short-term memory (biLSTM) network is proposed to predict the B-factor profile of a protein by combining its sequence-based features and structure-based features. Based on a large dataset of high-resolution proteins, our method predicts the B-factor profiles with an average Pearson correlation coefficient (PCC) of 0.71, and 85% of the B-factor profiles have a PCC greater than 0.6, which indicates a strong correlation between predicted and experimental values. In addition, our method remarkably outperforms the existing methods on four test datasets with different protein sizes.