Protein Chemical Shift Prediction

TL;DR

This paper introduces Procs14, a quantum mechanical-based chemical shift predictor for proteins, which outperforms existing empirical methods in accuracy and is suitable for integration into protein folding simulations.

Contribution

Development of Procs14, a novel QM-derived chemical shift predictor that incorporates corrections and is highly accurate for backbone atoms, enhancing protein structure analysis.

Findings

Procs14 outperforms empirical predictors in accuracy.

Procs14 closely matches QM chemical shifts in benchmarks.

Ensemble sampling improves chemical shift prediction accuracy.

Abstract

The protein chemical shifts holds a large amount of information about the 3-dimensional structure of the protein. A number of chemical shift predictors based on the relationship between structures resolved with X-ray crystallography and the corresponding experimental chemical shifts have been developed. These empirical predictors are very accurate on X-ray structures but tends to be insensitive to small structural changes. To overcome this limitation it has been suggested to make chemical shift predictors based on quantum mechanical(QM) calculations. In this thesis the development of the QM derived chemical shift predictor Procs14 is presented. Procs14 is based on 2.35 million density functional theory(DFT) calculations on tripeptides and contains corrections for hydrogen bonding, ring current and the effect of the previous and following residue. Procs14 is capable at performing predictions for the 13CA, 13CB, 13CO, 15NH, 1HN and 1HA backbone atoms. In order to benchmark Procs14, a number of QM NMR calculations are performed on full protein structures. Of the tested empirical and QM derived predictors, Procs14 reproduced the QM chemical shifts with the highest accuracy. A comparison with the QM derived predictor CheShift-2 on X-ray structures and NMR ensembles with experimental chemical shift data, showed that Procs14 predicted the chemical shifts with the best accuracy. The predictions on the NMR ensembles exhibited the best performance. This suggests that future work might benefit from using ensemble sampling when performing simulations of protein folding with chemical shifts. Procs14 is implemented in the markov chain monte carlo protein folding framework PHAISTOS. The computational efficient implementation of Procs14 allows for rapid predictions and therefore potential use in refinement and folding of protein structures.