NMR Lineshape Analysis of Intrinsically Disordered Protein Interactions

Interactions of intrinsically disordered proteins are central to their cellular functions, and solution-state NMR spectroscopy provides a powerful tool for characterizing both structural and mechanistic aspects of such interactions. Here we focus on the analysis of IDP interactions using NMR titration measurements. Changes in resonance lineshapes in two-dimensional NMR spectra upon titration with a ligand contain rich information on structural changes in the protein and the thermodynamics and kinetics of the interaction, as well as on the microscopic association mechanism. Here we present protocols for the optimal design of titration experiments, data acquisition, and data analysis by two-dimensional lineshape fitting using the TITAN software package

AI is a viable alternative to high throughput screening: a 318-target study

Data availability: All data generated or analyzed during this study are included in this published article and its supplementary information files.Supplementary Information is available online at: https://www.nature.com/articles/s41598-024-54655-z#Sec15 .High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery