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

In the elderly population, pathological inflammation has been associated with ageing-associated diseases. The term ‘inflammageing’, which was used for the first time by Franceschi and co-workers in 2000, is associated with the chronic, low-grade, subclinical inflammatory processes coupled to biological ageing. The source of these inflammatory processes is debated. The senescence-associated secretory phenotype (SASP) has been proposed as the main origin of inflammageing. The SASP is characterised by the release of inflammatory cytokines, elevated activation of the NLRP3 inflammasome, altered regulation of acetylcholine (ACh) nicotinic receptors, and abnormal NAD⁺ metabolism. Therefore, SASP may be ‘druggable’ by small molecule therapeutics targeting those emerging molecular targets. It has been shown that inflammageing is a hallmark of various cardiovascular diseases, including atherosclerosis, hypertension, and adverse cardiac remodelling. Therefore, the pathomechanism involving SASP activation via the NLRP3 inflammasome; modulation of NLRP3 via α7 nicotinic ACh receptors; and modulation by senolytics targeting other proteins have gained a lot of interest within cardiovascular research and drug development communities. In this review, which offers a unique view from both clinical and preclinical target-based drug discovery perspectives, we have focused on cardiovascular inflammageing and its molecular mechanisms. We have outlined the mechanistic links between inflammageing, SASP, interleukin (IL)-1β, NLRP3 inflammasome, nicotinic ACh receptors, and molecular targets of senolytic drugs in the context of cardiovascular diseases. We have addressed the ‘druggability’ of NLRP3 and nicotinic α7 receptors by small molecules, as these proteins represent novel and exciting targets for therapeutic interventions targeting inflammageing in the cardiovascular system and beyond.

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization 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 in the size of the potentially ‘druggable’ human proteome.

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

Signal transducer activator of transcription 3 (STAT3) is among the most investigated oncogenic transcription factors, as it is highly associated with cancer initiation, progression, metastasis, chemoresistance, and immune evasion. Evidences from both preclinical and clinical studies have demonstrated that STAT3 plays a critical role in several malignancies associated with poor prognosis such as glioblastoma and triple-negative breast cancer, and STAT3 inhibitors have shown efficacy in inhibiting cancer growth and metastasis. Constitutive activation of STAT3 by mutations occurs frequently in tumor cells and directly contributes to many malignant phenotypes. Unfortunately, detailed structural biology studies on STAT3 as well as target-based drug discovery efforts have been hampered by difficulties in the expression and purification of the full-length STAT3 and a lack of ligand-bound crystal structures. Considering these, molecular modeling and simulations offer an attractive strategy for the assessment of the “druggability” of STAT3 dimers and allow investigations of reported activating and inhibiting STAT3 mutants at the atomistic level of detail. In the present study, we focused on the effects exerted by reported STAT3 mutations on the protein structure, dynamics, DNA-binding, and dimerization, thus linking structure, dynamics, energetics, and the biological function. By employing atomistic molecular dynamics and umbrella-sampling simulations to a series of human STAT3 dimers, which comprised wild-type protein and four mutations, we explained the modulation of STAT3 activity by these mutations. Counter-intuitively, our results show that the D570K inhibitory mutation exerts its effect by enhancing rather than weakening STAT3–DNA interactions, which interfere with the DNA release by the protein dimer and thus inhibit STAT3 function as a transcription factor. We mapped the binding site and characterized the binding mode of a clinical candidate napabucasin/BBI-608 at STAT3, which resembles the effect of a D570K mutation. Our results contribute to understanding the activation/inhibition mechanism of STAT3, to explain the molecular mechanism of STAT3 inhibition by BBI-608. Alongside the characterization of the BBI-608 binding mode, we also discovered a novel binding site amenable to bind small-molecule ligands, which may pave the way to design novel STAT3 inhibitors and to suggest new strategies for pharmacological interventions to combat cancers associated with poor prognosis.

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.