Issue 12, 2020

Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data

Abstract

Here we report a new machine learning algorithm for protein chemical shift prediction that outperforms existing chemical shift calculators on realistic data that is not heavily curated, nor eliminates test predictions ad hoc. Our UCBShift predictor implements two modules: a transfer prediction module that employs both sequence and structural alignment to select reference candidates for experimental chemical shift replication, and a redesigned machine learning module based on random forest regression which utilizes more, and more carefully curated, feature extracted data. When combined together, this new predictor achieves state-of-the-art accuracy for predicting chemical shifts on a randomly selected dataset without careful curation, with root-mean-square errors of 0.31 ppm for amide hydrogens, 0.19 ppm for Hα, 0.84 ppm for C′, 0.81 ppm for Cα, 1.00 ppm for Cβ, and 1.81 ppm for N. When similar sequences or structurally related proteins are available, UCBShift shows superior native state selection from misfolded decoy sets compared to SPARTA+ and SHIFTX2, and even without homology we exceed current prediction accuracy of all other popular chemical shift predictors.

Graphical abstract: Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data

Supplementary files

Article information

Article type
Edge Article
Submitted
29 Dec 2019
Accepted
02 Mar 2020
First published
03 Mar 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2020,11, 3180-3191

Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data

J. Li, K. C. Bennett, Y. Liu, M. V. Martin and T. Head-Gordon, Chem. Sci., 2020, 11, 3180 DOI: 10.1039/C9SC06561J

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