Abstract
Understanding the structural mechanisms of protein–ligand binding and their dependence on protein sequence and conformation is of fundamental importance for biomedical research. Here we investigate the interplay of conformational change and ligand-binding kinetics for the serine protease Trypsin and its competitive inhibitor Benzamidine with an extensive set of 150 μs molecular dynamics simulation data, analysed using a Markov state model. Seven metastable conformations with different binding pocket structures are found that interconvert at timescales of tens of microseconds. These conformations differ in their substrate-binding affinities and binding/dissociation rates. For each metastable state, corresponding solved structures of Trypsin mutants or similar serine proteases are contained in the protein data bank. Thus, our wild-type simulations explore a space of conformations that can be individually stabilized by adding ligands or making suitable changes in protein sequence. These findings provide direct evidence of conformational plasticity in receptors.
Highlights
Understanding the structural mechanisms of protein–ligand binding and their dependence on protein sequence and conformation is of fundamental importance for biomedical research
Extensive sets of molecular dynamics (MD) simulations have been successfully combined with Markov state models (MSMs)[13] to reveal complex multistate kinetics of folding of peptides and small proteins[14,15,16] and conformational changes[17,18,19,20,21,22]
Extensive MD simulations of B150 ms cumulated simulation time of the serine protease Trypsin with its reversible competitive inhibitor Benzamidine are analysed with a Markov model
Summary
Understanding the structural mechanisms of protein–ligand binding and their dependence on protein sequence and conformation is of fundamental importance for biomedical research. We investigate the interplay of conformational change and ligand-binding kinetics for the serine protease Trypsin and its competitive inhibitor Benzamidine with an extensive set of 150 ms molecular dynamics simulation data, analysed using a Markov state model. A thorough understanding of protein–ligand binding encompasses a complete characterization of the bindingcompetent conformations of the protein, the binding poses and the complex kinetics between these conformations Employing this full kinetic network for drug efficacy optimization may significantly enhance our ability to do computational drug design[11,12]. It is found that Trypsin has multiple long-lived bindingcompetent conformations Both open and closed states of binding pocket S1 are found that bind Benzamidine in the crystal structure[27,28]. A third conformational switch is found that regulates the binding affinity
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