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SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning

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arxiv 2010.08162 v2 pith:FTE5RAEW submitted 2020-10-16 q-bio.BM cs.LG

Jonathan E. King , David Ryan Koes This is my paper
classification q-bio.BMcs.LG
keywords sidechainnetproteinstructureinformationdatasetlearningdatainclusion
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present SidechainNet, a new dataset that directly extends the ProteinNet dataset. SidechainNet includes angle and atomic coordinate information capable of describing all heavy atoms of each protein structure. In this paper, we provide background information on the availability of protein structure data and the significance of ProteinNet. Thereafter, we argue for the potentially beneficial inclusion of sidechain information through SidechainNet, describe the process by which we organize SidechainNet, and provide a software package (https://github.com/jonathanking/sidechainnet) for data manipulation and training with machine learning models.

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Cited by 6 Pith papers

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