A novel approach to protein chemical shift prediction from sequences using a protein language model

Abstract

Chemical shifts are crucial parameters in protein Nuclear Magnetic Resonance (NMR) experiments. Specifically, the chemical shifts of backbone atoms are essential for determining the constraints in protein structure analysis. Despite their importance, protein NMR experiments are costly and spectral analysis presents challenges due to sample impurities, complex experimental environments, and spectral overlap. Here, we propose a chemical shift prediction method that requires only protein sequences as input. This low-cost chemical shift predictor provides a chemical shift corresponding to each backbone atom, offers valuable prior information for peak assignment, and can significantly aid protein NMR spectrum analysis. Our approach leverages recent advances in pre-trained protein language models (PLMs) and employs a deep learning model to obtain chemical shifts. Different from other chemical shift prediction programs, our method does not require protein structures as input, significantly reducing costs and enhancing robustness. Our method can achieve comparable accuracy to other existing programs that require protein structures as input. In summary, this work introduces a novel method for protein chemical shift prediction and demonstrates the potential of PLMs for diverse applications.