  Computational protein design has a wide variety of applications. Despite its
remarkable success, designing a protein for a given structure and function is
still a challenging task. On the other hand, the number of solved protein
structures is rapidly increasing while the number of unique protein folds has
reached a steady number, suggesting more structural information is being
accumulated on each fold. Deep learning neural network is a powerful method to
learn such big data set and has shown superior performance in many machine
learning fields. In this study, we applied the deep learning neural network
approach to computational protein design for predicting the probability of 20
natural amino acids on each residue in a protein. A large set of protein
structures was collected and a multi-layer neural network was constructed. A
number of structural properties were extracted as input features and the best
network achieved an accuracy of 38.3%. Using the network output as residue type
restraints was able to improve the average sequence identity in designing three
natural proteins using Rosetta. Moreover, the predictions from our network show
~3% higher sequence identity than a previous method. Results from this study
may benefit further development of computational protein design methods.
