Chemical shift prediction for protein structure calculation and quality assessment using an optimally parameterized force field

[nmrlearner]

Chemical shift prediction for protein structure calculation and quality assessment using an optimally parameterized force field


January 2012
Publication year: 2012
Source:Progress in Nuclear Magnetic Resonance Spectroscopy, Volume 60



The exquisite sensitivity of chemical shifts as reporters of structural information, and the ability to measure them routinely and accurately, gives great import to formulations that elucidate the structure-chemical-shift relationship. Here we present a new and highly accurate, precise, and robust formulation for the prediction of NMR chemical shifts from protein structures. Our approach, shAIC (shift prediction guided by Akaikes Information Criterion), capitalizes on mathematical ideas and an information-theoretic principle, to represent the functional form of the relationship between structure and chemical shift as a parsimonious sum of smooth analytical potentials which optimally takes into account short-, medium-, and long-range parameters in a nuclei-specific manner to capture potential chemical shift perturbations caused by distant nuclei. shAIC outperforms the state-of-the-art methods that use analytical formulations. Moreover, for structures derived by NMR or structures with novel folds, shAIC delivers better overall results; even when it is compared to sophisticated machine learning approaches. shAIC provides for a computationally lightweight implementation that is unimpeded by molecular size, making it an ideal for use as a force field.
Graphical abstract

Highlights

shAIC predicts chemical shifts accurately for proteins based on their structure using a statistical approach summing local and long-range contributions applying Akaikes Information Criterion to optimally parameterize the method. ? We present a new method, shAIC, for predicting protein chemical shift based on the structure. ? shAIC is a statistical approach using a sum of analytical, smooth, differential potentials. ? shAIC uses Akaikes Information Criterion to optimally parameterize the method. ? shAIC is equally or more accurate than other methods. ? shAIC uses novel structural parameters of medium and long range.





More...