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 amid

SUBMITTER: Li J 

PROVIDER: S-EPMC8152569 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature