Two competing and mutually exclusive mechanisms of ligand recognition - conformational selection and induced fit - have dominated our interpretation of ligand binding in biological macromolecules for almost six decades. Conformational selection posits the pre-existence of multiple conformations of the macromolecule from which the ligand selects the optimal one. Induced fit, on the other hand, postulates the existence of conformational rearrangements of the original conformation into an optimal one that are induced by binding of the ligand. In the former case, conformational transitions precede the binding event; in the latter, conformational changes follow the binding step. Kineticists have used a facile criterion to distinguish between the two mechanisms based on the dependence of the rate of relaxation to equilibrium, kobs, on the ligand concentration, [L]. A value of kobs decreasing hyperbolically with [L] has been seen as diagnostic of conformational selection, while a value of kobs increasing hyperbolically with [L] has been considered diagnostic of induced fit. However, this simple conclusion is only valid under the rather unrealistic assumption of conformational transitions being much slower than binding and dissociation events. In general, induced fit only produces values of kobs that increase with [L] but conformational selection is more versatile and is associated with values of kobs that increase with, decrease with or are independent of [L]. The richer repertoire of kinetic properties of conformational selection applies to kinetic mechanisms with single or multiple saturable relaxations and explains the behavior of nearly all experimental systems reported in the literature thus far. Conformational selection is always sufficient and often necessary to account for the relaxation kinetics of ligand binding to a biological macromolecule and is therefore an essential component of any binding mechanism. On the other hand, induced fit is never necessary and only sufficient in a few cases. Therefore, the long assumed importance and preponderance of induced fit as a mechanism of ligand binding should be reconsidered.
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http://dx.doi.org/10.1016/j.bpc.2013.09.003 | DOI Listing |
J Org Chem
January 2025
Department of Chemistry, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, India.
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December 2024
Tunneling Group, Biotechnology Centre, Silesian University of Technology, Bolesława Krzywoustego 8, Gliwice 44-100, Poland.
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View Article and Find Full Text PDFAnalyst
January 2025
Key Laboratory of Green and High-Value Utilization of Salt Lake Resources, State Key Laboratory of Petroleum Molecular & Process Engineering (RIPP, SINOPEC), CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.
Sensitive and rapid detection methods for rare earth elements (REEs), including lanthanides (Lns), will facilitate the mining and recovery of these elements. Here, we innovated a rapid, highly selective and sensitive fluorescence detection method for Lns, based on Hans-Lanmodulin, a newly discovered protein with high selectivity and binding affinity for rare earth elements. By labelling the fluorescein moiety FITC onto Hans-Lanmodulin, named as FITC-Hans-LanM.
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January 2025
Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Transcription factors guide tissue development by binding to developmental stage-specific targets and establishing an appropriate enhancer landscape. In turn, DNA and chromatin modifications direct the genomic binding of transcription factors. However, how transcription factors navigate chromatin features to selectively bind a small subset of all the possible genomic target loci remains poorly understood.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Designing binders to target undruggable proteins presents a formidable challenge in drug discovery. In this work, we provide an algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model and subsequently screen these novel sequences for target-selective interaction activity via a contrastive language-image pretraining (CLIP)-based contrastive learning architecture.
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