JACS AuBiophysicsPeer-reviewed journal article
Conformational Preference Classification of Integrin-Binding Ligands Using Free Energy Perturbation
Martin VögeleiD, Rezvan ShahoeiiD, Loukas PetridisiD, Jing Li, Fu-Yang Lin, Lingle WangiD, Timothy A. SpringeriD, Jérémie VendômeiD
Abstract
Integrins are crucial cell adhesion receptors and attractive therapeutic targets, but developing safe small-molecule inhibitors has been challenging, at least in part due to inadvertent partial agonism caused by stabilization of the integrins open, high-affinity state. To address this challenge, we present a computational approach using Absolute Binding Free Energy Perturbation (AB-FEP) calculations to predict whether a ligand will stabilize the open or closed integrin states, leveraging the difference between the ligands binding free energy to the respective end states. Despite challenges posed by Ca and Mg ions, metal-coordinating residues in the binding pocket, and the subtlety of structural differences between states, AB-FEP achieved excellent classification performance on a set of known opening and closing ligands, significantly outperforming docking scores and MM-GBSA results. We also showed a good correlation between AB-FEP binding free energy differences and experimental values. Furthermore, AB-FEP provided insights into intermediate integrin states and analysis of simulation trajectories confirmed the formation of a water-mediated hydrogen bond network with an ion in the binding pocket to be characteristic for closing ligands. This work demonstrates AB-FEP as a robust method for classifying integrin ligands by the conformation they stabilize and for understanding their functional mechanisms, offering valuable guidance for designing safe and conformationally selective integrin therapeutics. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/721214v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1a0b8eforg.highwire.dtl.DTLVardef@cd6b56org.highwire.dtl.DTLVardef@420cc0org.highwire.dtl.DTLVardef@533963_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: protein folding & dynamics
- research focus: cell signaling & trafficking
- research focus: chromatin & epigenetic regulation
- approach: deep learning
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 32%
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