  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.
