Discrimination between conformational selection and induced fit protein-ligand binding using Integrated Global Fit analysis
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Updated: Mar 30, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
1Department of Chemical Engineering and Biotechnology, University of Applied Sciences, Schnittspahnstr. 12, 64287, Darmstadt, Germany. franz-josef.meyer-almes@h-da.de.
Abstract:
Molecular recognition between proteins and small molecule ligands is at the heart of biological function in cellular systems and the basis of modern rational drug development. Therefore, the mechanisms governing protein-ligand interaction have been objects of research for many decades. The last 15 years has seen a revival of a discussion whether conformational selection (CS) or induced fit (IF) is the most relevant binding mechanism. A decreasing observed rate constant, k obs, with increasing ligand concentration was considered to be a hallmark of CS, but according to contemporary knowledge, a positive saturating behavior of k obs can be explained by both CS and IF mechanisms. The only currently recognized kinetic method to differentiate between both binding mechanisms includes the measurement of two separate series of binding kinetics with variation of either protein or ligand under pseudo-first-order conditions. This study avoids the disadvantage of high protein concentrations and provides evidence that a comprehensive Integrated Global Fit analysis of sets of binding kinetics with just varied ligand concentration in combination with equilibrium data and optional displacement kinetics can effectively differentiate between CS and IF binding mechanisms. The limiting situation, when physical binding dominates over the previous (CS) or subsequent (IF) conformational changes, is carefully analyzed. Finally, the relevance of kinetic methods and the elucidation of more complex binding mechanisms are discussed for advanced rational selection and optimization of drug candidates.
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