There have been several studies suggesting that protein structures solved by NMR spectroscopy and X‐ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high‐quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures—in the root‐mean‐square deviations of the C
DLPacker : Deep learning for prediction of amino acid side chain conformations in proteins
Prediction of side chain conformations of amino acids in proteins (also termed “packing”) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.
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- Award ID(s):
- 2019745
- NSF-PAR ID:
- 10320625
- Editor(s):
- EDITOR-IN-CHIEF Dokholyan, Nikolay V.; ASSOCIATE EDITORS: Bahar, Ivet ; Feig, Michael ; Varadarajan, Raghavan ; Wodak, Shoshana; Moult, John Center
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- ISSN:
- 0887-3585
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/prot.26311
