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B-factor is highly correlated with protein internal motion, which is used to measure the uncertainty in the position of an atom within a crystal structure. Although the rapid progress of structural biology in recent years make more accurate protein structures available than ever, the gap between the known protein sequences and the known protein structures becomes wider and wider with the avalanche of new protein sequences emerging during the post-genomic Era. It is urgent to develop automated methods to predict B-factor profile from the amino acid sequences directly, so as to be able to timely utilize them for basic research and drug discovery. In this article, we propose a novel approach, called PredBF, to predict the real value of B-factor. We firstly extract both global and local features from the protein sequences as well as their evolution information, then the random forests feature selection is applied to rank their importance and the most important features are inputted to a two-stage support vector regression (SVR) for prediction, where the initial predicted outputs from the 1st SVR are further inputted to the 2nd layer SVR for final refinement. Our results have revealed that a systematic analysis of the importance of different features makes us have deep insights into the different contributions of features and is very necessary for developing effective B-factor prediction tools. The two-layer SVR prediction model designed in this study further enhanced the robustness of predicting the B-factor profile.
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