ProDCoNN: Protein design using a convolutional neural network |
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Authors: | Yuan Zhang Yang Chen Chenran Wang Chun-Chao Lo Xiuwen Liu Wei Wu Jinfeng Zhang |
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Institution: | 1. Department of Statistic, Florida State University, Tallahassee, Florida;2. Department of Computer Science, Florida State University, Tallahassee, Florida |
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Abstract: | Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C α atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets. |
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Keywords: | convolutional neural network inverse folding problem ProDCoNN protein design protein engineering |
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