Principal Component Analysis Based Deconvolution of NMR Chemical Shifts for Objective Biomolecular Interaction Mapping

Abstract

Identifying ligand-binding interfaces from NMR chemical shift perturbation data is a cornerstone of structural biology, yet it remains hampered by subjective thresholding and the loss of directional information in conventional scalar metrics. Traditional methods rely on empirical weighting factors that lack physical universality and ignore critical parameters like exchange-induced line broadening. Here, we introduce PALI (principal component analysis for ligand interactions), an objective and PCA-based framework designed to standardize multivariate NMR analysis. By implementing Z-score standardization, PALI replaces arbitrary constants with a rigorously data-driven statistical framework, preserving the multidimensionality of spectral changes. We demonstrate that PALI effectively filters stochastic noise and identifies binding hotspots, including features such as intermediate exchange that are often obscured in conventional 1D plots. Validation across diverse systems, from structured proteins to complex dynamic assemblies and intrinsically disordered regions/proteins, proves that PALI provides a robust, reproducible, and automated solution for interaction mapping. PALI is freely available as a Web-based dashboard, bridging the gap between advanced multivariate statistics and routine structural biology workflows.