The alpha-I domain, found in the alpha-subunit of the leucocyte integrins such as alphaMbeta2 and alphaLbeta2, switches between the open and closed tertiary conformations, reflecting the high- and low-affinity ligand-binding states of the integrin that are required for regulated cell adhesion and migration. In the present study we show, by using point mutations and engineered disulphide bonds, that ligand affinity can be reduced or increased allosterically by altering the equilibrium between the closed and open states. We determined equilibrium constants for the binding of two ligands, fibrinogen and intercellular cell-adhesion molecule 1, to the alphaM-I domain by surface plasmon resonance, and determined crystal structures of a low-affinity mutant. Locking the domain in the open conformation increases affinity by a factor of no greater than 10, consistent with a closely balanced equilibrium between the two conformations in the absence of ligand. This behaviour contrasts with that of the unliganded alphaL-I domain, for which the equilibrium lies strongly in favour of the closed conformation. These results suggest significant differences in the way the parent integrins regulate I domain conformation and hence ligand affinity.
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http://dx.doi.org/10.1042/BJ20021273 | DOI Listing |
Curr Top Med Chem
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Medicinal Chemistry Department, Theodor Bilharz Research Institute Kornaish El Nile, Warrak El-Hadar, Imbaba (P.O. 30), Giza 12411, Egypt.
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Heliyon
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Biomass Conversion and Bioproducts Laboratory, Center for Bioenergy, School of Chemical & Biotechnology, SASTRA Deemed University, Thirumalaisamudram, Tamil Nadu, India.
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View Article and Find Full Text PDFPowerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands.
View Article and Find Full Text PDFClin Oral Investig
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Department of Biology, Science Faculty, Atatürk University, Erzurum, Türkiye.
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J Med Chem
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Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark.
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