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Molecular Therapy. Methods & Clinical Development logoLink to Molecular Therapy. Methods & Clinical Development
. 2020 Oct 26;19:374. doi: 10.1016/j.omtm.2020.10.008

Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate

Jared A Delmar , Jihong Wang, Seo Woo Choi, Jason A Martins, John P Mikhail
PMCID: PMC7593340  PMID: 33145373

(Methods Clin Dev. 15, 264–274; December 13, 2019)

In the originally published version of this article, phi and chi2 angles appearing in Data S1 were miscalculated. The correct dihedral angles now appear in Data S1.

The authors regret this error.

Acknowledgments

The authors would like to thank Benjamin Looker and Romina Hofele, for helping to compile the training and validation datasets; Sydney Clark, Arun Parupudi, Chen Qian, and Weichen Xu for their help with sample preparation; Ben Niu, Chen Qian, and Anthony Shannon for help with data collection; David Arancibia, Trina Do, Lauren Johnson, Nicholas Knoepfle, and Mohamed Ndiaye for help compiling samples for the training set; Hege Beard, Christopher Negron, and Kevin Halligan for their help with homology model building and parameter extraction; Xiaoyu Chen, Bojana Popovic, and Bruck Taddese for helpful discussion and manuscript review; Fiona Cusdin and Christopher Lloyd for help with sample generation for the validation set; and Gail Wasserman, Xiangyang Wang, Xiaoyu Chen, Milton Axley, Thomas Blacklock, Ray Field, T. Alan Hatton, and the 2018 MIT Practice School Cohort for their continual support of the project.

Supplemental Information

Data S1. Training and Validation Datasets
mmc1.xlsx (119.2KB, xlsx)

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Training and Validation Datasets
mmc1.xlsx (119.2KB, xlsx)

Articles from Molecular Therapy. Methods & Clinical Development are provided here courtesy of American Society of Gene & Cell Therapy

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