High-Resolution Phase-Sensitive NMR Reconstruction for Protein Studies Using Diffusion-Based Deep Learning

Abstract

Phase-sensitive NMR spectroscopy provides essential information for accurate component identification, quantitative analysis, and structural characterization, particularly in protein studies. However, the acquisition of high-quality phase-sensitive NMR spectra with absorptive line shapes typically requires complementary quadrature acquisition and elaborate phase correction, which often involves additional experimental repetitions and time-consuming manual operations. In this study, we present a diffusion-based deep-learning framework for automatic phase-sensitive NMR spectrum reconstruction directly from common NMR experimental data, free of quadrature acquisition and phase correction operation. The proposed method formulates the phasing problem as a conditional probabilistic generative process in which a denoising network iteratively refines noisy spectral estimates toward physically consistent absorption-mode spectra under the guidance of the observed magnitude-mode data. Comprehensive validation on a diverse set of protein samples demonstrates the effectiveness and robustness of the proposed method, thus providing an effective and automated solution for phase-sensitive NMR spectroscopy reconstruction.