bioRxivBiophysicsPreprint
Unraveling the Impact of W215A/E217A Mutations on Thrombin Dynamics and Thrombomodulin Binding through Molecular Dynamics Simulations
Abstract
Thrombin, a central serine protease in hemostasis, exhibits dual functionality in coagulation processes--favoring fibrinogen cleavage in its native form while shifting towards protein C activation when complexed with thrombomodulin (TM). Thrombin also plays roles in cancer-associated thrombosis and may be involved in metastasis and tumorigenesis. The W215A/E217A (WE) double mutant of thrombin presents a unique case, with its fibrinogen cleavage activity diminished by 19,000-fold, contrasting a modest 7-fold reduction in protein C activation in the presence of TM. The differential substrate specificity of this mutant raises fundamental questions about the underlying molecular mechanisms. In this study, we employed all-atom microsecond-scale molecular dynamics (MD) simulations, complemented by Root Mean Square Fluctuation (RMSF) analysis, clustering algorithms, PCA-based free-energy surfaces, and logistic regression modeling, to dissect the structural and allosteric changes driving thrombins substrate specificity. Our results unveil distinct conformational states within the catalytic triad, each optimized for specific substrate interactions. We demonstrate that the WE mutations synergize with TM456 binding, resulting in altered hydrogen bond networks and distinct free energy landscapes. A key finding of our research is the identification of ARG125 as a pivotal element in these interactions, consistently forming critical hydrogen bonds across different thrombin variants. The persistent role of ARG125 not only elucidates aspects of thrombins functional plasticity but also positions it as a promising target for novel therapies. This comprehensive analysis enhances our understanding of thrombins structural dynamics, paving the way for more effective and targeted therapeutics.
- organism: human
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
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