Quantifying the Interactions between Biomolecules: Guidelines for Assay Design and Data Analysis
Author(s) -
Peter J. Tonge
Publication year - 2019
Publication title -
acs infectious diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.324
H-Index - 39
ISSN - 2373-8227
DOI - 10.1021/acsinfecdis.9b00012
Subject(s) - computer science , data mining , set (abstract data type) , drug discovery , computational biology , software , binding site , nonlinear system , biomolecule , biological system , data science , biochemical engineering , chemistry , bioinformatics , nanotechnology , biology , physics , engineering , materials science , biochemistry , quantum mechanics , programming language
The accurate and precise determination of binding interactions plays a central role in fields such as drug discovery where structure-activity relationships guide the selection and optimization of drug leads. Binding is often assessed by monitoring the response caused by varying one of the binding partners in a functional assay or by using methods where the concentrations of free and/or bound ligand can be directly determined. In addition, there are also many approaches where binding leads to a change in the properties of the binding partner(s) that can be directly quantified such as an alteration in mass or in a spectroscopic signal. The analysis of data resulting from these techniques invariably relies on computer software that enable rapid fitting of the data to nonlinear multiparameter equations. The objective of this Perspective is to serve as a reminder of the basic assumptions that are used in deriving these equations and thus that should be considered during assay design and subsequent data analysis. The result is a set of guidelines for authors considering submitting their work to journals such as ACS Infectious Diseases.
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