DYRK1B inhibition exerts senolytic effects on endothelial cells and rescues endothelial dysfunctions

Aging is the major risk factor for chronic disease development. Cellular senescence is a key mechanism that triggers or contributes to age-related phenotypes and pathologies. The endothelium, a single layer of cells lining the inner surface of a blood vessel, is a critical interface between blood and all tissues. Many studies report a link between endothelial cell senescence, inflammation, and diabetic vascular diseases. Here we identify, using combined advanced AI and machine learning, the Dual Specificity Tyrosine Phosphorylation Regulated Kinase 1B (DYRK1B) protein as a possible senolytic target for senescent endothelial cells. We demonstrate that upon induction of senescence in vitro DYRK1B expression is increased in endothelial cells and localized at adherens junctions where it impairs their proper organization and functions. DYRK1B knock-down or inhibition restores endothelial barrier properties and collective behavior. DYRK1B is therefore a possible target to counteract diabetes-associated vascular diseases linked to endothelial cell senescence.

Long Time Scale Ensemble Methods in Molecular Dynamics: Ligand-Protein Interactions and Allostery in SARS-CoV-2 Targets

We subject a series of five protein-ligand systems which contain important SARS-CoV-2 targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose phosphatase, to long time scale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10 μs simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, regardless of their temporal duration individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this time scale, we compare the statistical distribution of protein-ligand contact frequencies for these ten/twelve 10 μs trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long time scale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that, although this is the standard way such quantities are currently reported at long time scale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study.

Predicting Residence Time of GPCR Ligands with Machine Learning

Drug-target residence time, the duration of binding at a given protein target, has been shown in some protein families to be more significant for conferring efficacy than binding affinity. To carry out efficient optimization of residence time in drug discovery, machine learning models that can predict that value need to be developed. One of the main challenges with predicting residence time is the paucity of data. This chapter outlines all of the currently available ligand kinetic data, providing a repository that contains the largest publicly available source of GPCR-ligand kinetic data to date. To help decipher the features of kinetic data that might be beneficial to include in computational models for the prediction of residence time, the experimental evidence for properties that influence residence time are summarized. Finally, two different workflows for predicting residence time with machine learning are outlined. The first is a single-target model trained on ligand features; the second is a multi-target model trained on features generated from molecular dynamics simulations.

Psychosocial outcomes of peer support for patients with an inherited cardiac condition

Funding Acknowledgements

Type of funding sources: None.

Introduction

Inherited cardiac conditions (ICCs) are feared for their risk of sudden death. Individuals are often young and diagnosed after the sudden death of an apparently healthy family member. A diagnosis can have a profound psychological impact and negative effect on quality of life. Uncertainty surrounding the natural history of some diseases causes anxiety and concern about existing children or starting a family. Necessary lifestyle adjustments are often associated with a sense of isolation during social engagement with peers. Psychological support for such patients is scarce. However, a specialist nurse led peer group support within the ICC service may improve psychological outcomes and empower patients to support others. 

Purpose

To determine the effect of a nurse led peer support group on subjective psychological symptoms for patients with ICCs.

Methods

A pilot specialist nurse support group was established in February 2020 including 30 patients with ICCs. This consisted of a meeting in person followed by 6 subsequent 2 monthly online video meetings. Each session lasted 2 hours and included a talk by a healthcare professional on an ICC related topic, followed by an open forum for group discussion facilitated by the specialist nurse. An online social media chat forum was also developed. After 1 year, a bespoke questionnaire was distributed to all participants enquiring about the effect of group support on anxiety level, sense of isolation, knowledge about their condition and empowerment to support themselves and others. 

Results

21 (70%) patients aged between 20 and 65 years old (mean age 49) responded. Diagnoses included Brugada syndrome, arrhythmogenic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy and long QT syndrome. All participants agreed that the group provided a comfortable platform to ask questions about their condition. 95% of participants were keen to know more about their condition after diagnosis of which 86% agreed that knowledge about their condition had improved since joining the group. 90% of participants experienced anxiety related to their condition before joining the group of which 76% reported reduced levels since joining. 76% felt isolated after their diagnosis of which 86% reported that these feelings had lessened since joining the group. 86% of the group agreed that group discussion empowered them and helped them support other affected individuals. 

Conclusion

A pilot study support group for patients with ICCs reduced anxiety and sense of isolation, improved knowledge, and sense of empowerment and willingness to support other patients in ≥ 80% of attendees. There is potential that patient support groups can be kick started by specialist nurses and subsequently allowed to run by patients themselves. Apart from improving psychological outcomes, such practice may reduce the workload for the ICC multidisciplinary team.

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.

Computational prediction of GPCR oligomerization

There has been a recent and prolific expansion in the number of GPCR crystal structures being solved: in both active and inactive forms and in complex with ligand, with G protein and with each other. Despite this, there is relatively little experimental information about the precise configuration of GPCR oligomers during these different biologically relevant states. While it may be possible to identify the experimental conditions necessary to crystallize a GPCR preferentially in a specific structural conformation, computational approaches afford a potentially more tractable means of describing the probability of formation of receptor dimers and higher order oligomers. Ensemble-based computational methods based on structurally determined dimers, coupled with a computational workflow that uses quantum mechanical methods to analyze the chemical nature of the molecular interactions at a GPCR dimer interface, will generate the reproducible and accurate predictions needed to predict previously unidentified GPCR dimers and to inform future advances in our ability to understand and begin to precisely manipulate GPCR oligomers in biological systems. It may also provide information needed to achieve an increase in the number of experimentally determined oligomeric GPCR structures.

Ensemble-Based Steered Molecular Dynamics Predicts Relative Residence Time of A2A Receptor Binders

Drug-target residence time, the length of time for which a small molecule stays bound to its receptor target, has increasingly become a key property for optimization in drug discovery programs. However, its in silico prediction has proven difficult. Here we describe a method, using atomistic ensemble-based steered molecular dynamics (SMD), to observe the dissociation of ligands from their target G protein-coupled receptor in a time scale suitable for drug discovery. These dissociation simulations accurately, precisely, and reproducibly identify ligand-residue interactions and quantify the change in ligand energy values for both protein and water. The method has been applied to 17 ligands of the A_2A adenosine receptor, all with published experimental kinetic binding data. The residues that interact with the ligand as it dissociates are known experimentally to have an effect on binding affinities and residence times. There is a good correlation ( R² = 0.79) between the computationally calculated change in water-ligand interaction energy and experimentally determined residence time. Our results indicate that ensemble-based SMD is a rapid, novel, and accurate semi-empirical method for the determination of drug-target relative residence time.

Landscape of activating cancer mutations in FGFR kinases and their differential responses to inhibitors in clinical use

Frequent genetic alterations discovered in FGFRs and evidence implicating some as drivers in diverse tumors has been accompanied by rapid progress in targeting FGFRs for anticancer treatments. Wider assessment of the impact of genetic changes on the activation state and drug responses is needed to better link the genomic data and treatment options. We here apply a direct comparative and comprehensive analysis of FGFR3 kinase domain variants representing the diversity of point-mutations reported in this domain. We reinforce the importance of N540K and K650E and establish that not all highly activating mutations (for example R669G) occur at high-frequency and conversely, that some "hotspots" may not be linked to activation. Further structural characterization consolidates a mechanistic view of FGFR kinase activation and extends insights into drug binding. Importantly, using several inhibitors of particular clinical interest (AZD4547, BGJ-398, TKI258, JNJ42756493 and AP24534), we find that some activating mutations (including different replacements of the same residue) result in distinct changes in their efficacy. Considering that there is no approved inhibitor for anticancer treatments based on FGFR-targeting, this information will be immediately translatable to ongoing clinical trials.