We demonstrate mechanochemical regulation of platelet adhesion to von Willebrand factor in thrombosis and hemostasis.

Protein dynamics finely tune the “druggability” of mammalian PAS-B domains, as assessed by atomistic molecular dynamics simulations and hotspot mapping.

Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in risk assessment of mAb candidates to promote early-stage screening of potentially problematic mAb candidates. In this study, a quantitative structure–activity relationship (QSAR) modelling workflow was designed for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs. Three novel descriptor sets derived from primary sequence, homology modelling, and atomistic molecular dynamics (MD) simulations were developed and assessed to determine the necessary level of structural resolution needed to accurately capture the relationship between mAb structures and HIC retention times. The results showed that descriptors derived from 3D structures obtained after MD simulations were the most suitable for HIC retention time prediction with a R2 = 0.63 in an external test set. It was found that when using homology modelling, the resulting 3D structures became biased towards the used structural template. Performing an MD simulation therefore proved to be a necessary post-processing step for the mAb structures in order to relax the structures and allow them to attain a more natural conformation. Based on the results, the proposed workflow in this paper could therefore potentially contribute to aid in risk assessment of mAb candidates in early development.

Cholinergic α7 nicotinic receptors encoded by the CHRNA7 gene are ligand-gated ion channels directly related to memory and immunomodulation. Exons 5–7 in CHRNA7 can be duplicated and fused to exons A-E of FAR7a, resulting in a hybrid gene known as CHRFAM7A, unique to humans. Its product, denoted herein as Dupα7, is a truncated subunit where the N-terminal 146 residues of the ligand binding domain of the α7 receptor have been replaced by 27 residues from FAM7. Dupα7 negatively affects the functioning of α7 receptors associated with neurological disorders, including Alzheimer’s diseases and schizophrenia. However, the stoichiometry for the α7 nicotinic receptor containing dupα7 monomers remains unknown. In this work, we developed computational models of all possible combinations of wild-type α7 and dupα7 pentamers and evaluated their stability via atomistic molecular dynamics and coarse-grain simulations. We assessed the effect of dupα7 subunits on the Ca2+ conductance using free energy calculations. We showed that receptors comprising of four or more dupα7 subunits are not stable enough to constitute a functional ion channel. We also showed that models with dupα7/α7 interfaces are more stable and are less detrimental for the ion conductance in comparison to dupα7/dupα7 interfaces. Based on these models, we used protein–protein docking to evaluate how such interfaces would interact with an antagonist, α-bungarotoxin, and amyloid Aβ42. Our findings show that the optimal stoichiometry of dupα7/α7 functional pentamers should be no more than three dupα7 monomers, in favour of a dupα7/α7 interface in comparison to a homodimer dupα7/dupα7 interface. We also showed that receptors bearing dupα7 subunits are less sensitive to Aβ42 effects, which may shed light on the translational gap reported for strategies focused on nicotinic receptors in ‘Alzheimer’s disease research.

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterisation of allosteric and cryptic binding sites, which can be rendered "druggable" by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein "hotspots" in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase of the size of the potentially 'druggable' human proteome.