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Accurate prediction of NMR chemical shifts can in principle help refineaqueous solution structure of proteins to the quality of X-ray structures. Wereport a new machine learning algorithm for protein chemical shift predictionthat outperforms existing chemical shift calculators on realistic NMR solutiondata. Our UCBShift predictor implements two modules: a transfer predictionmodule that employs both sequence and structural alignment to select referencecandidates for experimental chemical shift replication, and a redesignedmachine 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 chemicalshifts on a "real-world" dataset, with root-mean-square errors of 0.31 ppm foramide hydrogens, 0.19 ppm for Halpha, 0.87 ppm for C, 0.81 ppm for Calpha, 1.01ppm for Cbeta, and 1.83 ppm for N, exceeding current prediction accuracy ofpopular chemical shift predictors such as SPARTA+ and SHIFTX2.

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