Accurate Prediction of Chemical Shifts for Aqueous Protein Structure for "Real World" Cases using Machine Learning

TL;DR

This paper introduces UCBShift, a machine learning-based predictor that significantly improves the accuracy of NMR chemical shift predictions for proteins in aqueous solutions, aiding structural refinement.

Contribution

The paper presents a novel machine learning algorithm combining sequence-structure alignment and random forest regression for superior chemical shift prediction.

Findings

Achieves state-of-the-art accuracy with low RMS errors for various nuclei.

Outperforms existing predictors like SPARTA+ and SHIFTX2.

Effective on realistic, real-world NMR data.

Abstract

Accurate prediction of NMR chemical shifts can in principle help refine aqueous solution structure of proteins to the quality of X-ray structures. We report a new machine learning algorithm for protein chemical shift prediction that outperforms existing chemical shift calculators on realistic NMR solution data. 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 "real-world" dataset, with root-mean-square errors of 0.31 ppm for amide hydrogens, 0.19 ppm for Halpha, 0.87 ppm for C, 0.81 ppm for Calpha, 1.01 ppm for Cbeta, and 1.83 ppm for N, exceeding current prediction accuracy of popular chemical shift predictors such as SPARTA+ and SHIFTX2.