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Introducing the CSP analyzer: A novel machine learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.
Comp. Struc. Biotech. J. 18, 603-611 (2020)
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.
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6.018
1.785
3
4
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
2-d Nmr ; Automatic Csp Analysis ; C# Gui ; Fragment Screening ; Fragment-based Drug Discovery ; Machine-learning; Difference Std Nmr; Drug-binding; Spectroscopy; Software; Relaxation; Proteins; Ligands
Language
english
Publication Year
2020
HGF-reported in Year
2020
ISSN (print) / ISBN
2001-0370
e-ISSN
2001-0370
Quellenangaben
Volume: 18,
Pages: 603-611
Publisher
Research Network of Computational and Structural Biotechnology (RNCSB)
Publishing Place
Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)
POF-Topic(s)
30203 - Molecular Targets and Therapies
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503000-001
Grants
Helmholtz Association Initiative and Networking Funds
Accelerated Early staGe drug dIScovery (AEGIS)
European Union
Accelerated Early staGe drug dIScovery (AEGIS)
European Union
WOS ID
WOS:000607298800006
Scopus ID
85081995767
PubMed ID
32257044
Erfassungsdatum
2020-04-09