Long-term cognitive dysfunction after the COVID-19 pandemic: a narrative review.

Introduction: SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has brought a conglomerate of novel chronic disabling conditions described as 'Long COVID/Post-COVID-19 Syndrome'. Recent evidence suggests that the multifaceted nature of this syndrome results in both pulmonary and extrapulmonary sequelae,chronic dyspnoea, persistent fatigue, and cognitive dysfunction being the most common, debilitating symptoms. Several mechanisms engender or exacerbate cognitive impairment, including central nervous system and extra-central nervous system causes, although the exact mechanism remains unclear. Both hospitalized and non-hospitalized patients may suffer varying degrees of cognitive impairment, ranging from fatigue and brain fog to prolonged deficits in memory and attention, detrimental to the quality-of-life years post-recovery. The aim of this review is to understand the underlying mechanisms, associations, and attempts for prevention with early intervention of long-term cognitive impairment post-COVID-19. Methodology: A systematic search was conducted through multiple databases such as Medline, National Library of Medicine, Ovid, Scopus database to retrieve all the articles on the long-term sequalae of cognitive dysfunction after SARS-CoV-2 infection. The inclusion criteria included all articles pertinent to this specific topic and exclusion criteria subtracted studies pertaining to other aetiologies of cognitive dysfunction. This search was carefully screened for duplicates and the relevant information was extracted and analysed. Results/discussion: To date, the exact pathogenesis, and underlying mechanisms behind cognitive dysfunction in COVID-19, remain unclear, hindering the development of adequate management strategies. However, the proposed mechanisms suggested by various studies include direct damage to the blood-brain barrier, systemic inflammation, prolonged hypoxia, and extended intensive care admissions. However, no clear-cut guidelines for management are apparent. Conclusion: This review of the COVID-19 pandemic has elucidated a new global challenge which is affecting individuals' quality of life by inducing long-term impaired cognitive function. The authors have found that comprehensive evaluations and interventions are crucial to address the cognitive sequelae in all COVID-19 patients, especially in patients with pre-existing cognitive impairment. Nevertheless, the authors recommend further research for the development of relevant, timely neurocognitive assessments and treatment plans.

Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis.

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor-peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

Deep learning approaches for conformational flexibility and switching properties in protein design.

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.

Structure of a D2 dopamine receptor-G-protein complex in a lipid membrane.

The D2 dopamine receptor (DRD2) is a therapeutic target for Parkinson's disease1 and antipsychotic drugs2. DRD2 is activated by the endogenous neurotransmitter dopamine and synthetic agonist drugs such as bromocriptine3, leading to stimulation of Gi and inhibition of adenylyl cyclase. Here we used cryo-electron microscopy to elucidate the structure of an agonist-bound activated DRD2-Gi complex reconstituted into a phospholipid membrane. The extracellular ligand-binding site of DRD2 is remodelled in response to agonist binding, with conformational changes in extracellular loop 2, transmembrane domain 5 (TM5), TM6 and TM7, propagating to opening of the intracellular Gi-binding site. The DRD2-Gi structure represents, to our knowledge, the first experimental model of a G-protein-coupled receptor-G-protein complex embedded in a phospholipid bilayer, which serves as a benchmark to validate the interactions seen in previous detergent-bound structures. The structure also reveals interactions that are unique to the membrane-embedded complex, including helix 8 burial in the inner leaflet, ordered lysine and arginine side chains in the membrane interfacial regions, and lipid anchoring of the G protein in the membrane. Our model of the activated DRD2 will help to inform the design of subtype-selective DRD2 ligands for multiple human central nervous system disorders.

Fast-forward Langevin dynamics with momentum flips.

Stochastic thermostats based on the Langevin equation, in which a system is coupled to an external heat bath, are popular methods for temperature control in molecular dynamics simulations due to their ergodicity and their ease of implementation. Traditionally, these thermostats suffer from sluggish behavior in the limit of high friction, unlike thermostats of the Nosé-Hoover family whose performance degrades more gently in the strong coupling regime. We propose a simple and easy-to-implement modification to the integration scheme of the Langevin algorithm that addresses the fundamental source of the overdamped behavior of high-friction Langevin dynamics: if the action of the thermostat causes the momentum of a particle to change direction, it is flipped back. This fast-forward Langevin equation preserves the momentum distribution and so guarantees the correct equilibrium sampling. It mimics the quadratic behavior of Nosé-Hoover thermostats and displays similarly good performance in the strong coupling limit. We test the efficiency of this scheme by applying it to a 1-dimensional harmonic oscillator, as well as to water and Lennard-Jones polymers. The sampling efficiency of the fast-forward Langevin equation thermostat, measured by the correlation time of relevant system variables, is at least as good as the traditional Langevin thermostat, and in the overdamped regime, the fast-forward thermostat performs much better, improving the efficiency by an order of magnitude at the highest frictions we considered.