ArticleJournal of biomolecular NMR2004
Automated evaluation of chemical shift perturbation spectra: New approaches to quantitative analysis of receptor-ligand interaction NMR spectra.
Article in Journal of biomolecular NMR, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Harmonizing Peak Matching between Multidimensional NMR Spectra.Journal of chemical information and modeling · 2026Article
- The Bifunctional Effects of Lactoferrin (LFcinB11) in Inhibiting Neural Cell Adhesive Molecule (NCAM) Polysialylation and the Release of Neutrophil Extracellular Traps (NETs).International journal of molecular sciences · 2024Article
- Fully Automated Characterization of Protein-Peptide Binding by Microfluidic 2D NMR.Journal of the American Chemical Society · 2023Article
- Automated Determination of Nuclear Magnetic Resonance Chemical Shift Perturbations in Ligand Screening Experiments: The PICASSO Web Server.Journal of chemical information and modeling · 2021Article
- Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening.Computational and structural biotechnology journal · 2020Article
- Evaluation of integrin αvβNature communications · 2019Article
- Fragment-based discovery of a chemical probe for the PWWP1 domain of NSD3.Nature chemical biology · 2019Article
- Automation of peak-tracking analysis of stepwise perturbed NMR spectra.Journal of biomolecular NMR · 2017Article
- Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY.BMC bioinformatics · 2012Article
- Modulation of the structure, catalytic activity, and fidelity of African swine fever virus DNA polymerase X by a reversible disulfide switch.The Journal of biological chemistry · 2009Article
- A multi-step NMR screen for the identification and evaluation of chemical leads for drug discovery.Combinatorial chemistry & high throughput screening · 2009Article
- A general algorithm for peak-tracking in multi-dimensional NMR experiments.Journal of biomolecular NMR · 2007Article
- NvMap: automated analysis of NMR chemical shift perturbation data.Bioinformatics (Oxford, England) · 2007Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper presents new methods designed for quantitative analysis of chemical shift perturbation NMR spectra. The methods automatically trace the displacements of cross peaks between a perturbed test spectrum and the reference spectrum (or among a series of titration spectra), and measure the changes of chemical shifts, heights, and widths of the altered peaks. The methods are primary aimed at the (1)H-(15)N HSQC spectra of relatively small proteins (<15 kDa) assuming fast exchange between free and ligand-bound states on the chemical shift time scale, or for comparing spectra of free and fully bound states in the slow exchange situation. Using the (1)H-(15)N HSQC spectra from a titration experiment of the 74-residue Pex13p SH3 domain with a Pex14p peptide ligand (14 residues, K (d)= approximately 40 microM), we demonstrate the scope and limits of our automatic peak tracing (APET) algorithm for efficient scoring of high-throughput SAR by NMR type HSQC spectra, and progressive peak tracing (PROPET) algorithm for detailed analysis of ligand titration spectra. Simulated spectra with low signal-to-noise ratios (S/N ranged from 20 to 1) were used to demonstrate the reliability and reproducibility of the results when dealing with poor quality spectra. These algorithms have been implemented in a new software module, FELIX-Autoscreen, for streamlined processing, analysis and visualization of SAR by NMR and other high-throughput receptor/ligand interaction experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.