RESUMO
NMR-based screening, especially fragment-based drug discovery is a valuable approach in early-stage drug discovery. Monitoring fragment-binding in protein-detected 2D NMR experiments requires analysis of hundreds of spectra to detect chemical shift perturbations (CSPs) in the presence of ligands screened. Computational tools are available that simplify the tracking of CSPs in 2D NMR spectra. However, to the best of our knowledge, an efficient automated tool for the assessment and binning of multiple spectra for ligand binding has not yet been described. We present a novel and fast approach for analysis of multiple 2D HSQC spectra based on machine-learning-driven statistical discrimination. The CSP Analyzer features a C# frontend interfaced to a Python ML classifier. The software allows rapid evaluation of 2D screening data from large number of spectra, reducing user-introduced bias in the evaluation. The CSP Analyzer software package is available on GitHub https://github.com/rubbs14/CSP-Analyzer/releases/tag/v1.0 under the GPL license 3.0 and is free to use for academic and commercial uses.
RESUMO
Molecular docking is a computational method employed to estimate the binding between a small ligand (the drug candidate) and a protein receptor that has become a standard part of workflow in drug discovery. Generally, when the binding site is known and a molecule is similar to known ligands, the most popular docking methods are rather accurate in the prediction of the geometry. Unfortunately, when the binding site is unknown, the blind docking analysis requires large computational resources and the results are often not accurate. Here we present Yada, a new tool for molecular docking that is capable to distribute efficiently calculations onto general purposes computer grid and that combines biological and structural information of the receptor. Yada is available for Windows and Linux and it is free to download at www.yada.unisa.it .