Retro Drug Design: From Target Properties to Molecular Structures

Pyrazolines as potential anti-Alzheimer's agents: DFT, molecular docking, enzyme inhibition and pharmacokinetic studies

Alzheimer's disease (AD) is a neurodegenerative disease that is characterized as the main dementia in the elderly. Eighteen pyrazolines were synthesized and evaluated for their inhibitory effects against acetylcholinesterase (AChE) in vitro. Possible interactions between pyrazolines and the enzyme were explored by in silico experiments. Compound 2B of the series was the most active pyrazoline with an IC50 value of 58 nM. Molecular docking studies revealed two important π-π interactions with residues Trp 286 and Tyr 341. A correlation between the HOMO-1 surface and AChE inhibition was observed. ADMET assays demonstrated a good profile for compound 2B. From the abovementioned findings, a new avenue of compound 2B analogues could be explored to develop anti-AD agents.

Null models in network neuroscience

Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.

SARS-CoV-2 variants, spike mutations and immune escape

Although most mutations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome are expected to be either deleterious and swiftly purged or relatively neutral, a small proportion will affect functional properties and may alter infectivity, disease severity or interactions with host immunity. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis lasting about 11 months. Since late 2020, however, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations, in the context of 'variants of concern', that impact virus characteristics, including transmissibility and antigenicity, probably in response to the changing immune profile of the human population. There is emerging evidence of reduced neutralization of some SARS-CoV-2 variants by postvaccination serum; however, a greater understanding of correlates of protection is required to evaluate how this may impact vaccine effectiveness. Nonetheless, manufacturers are preparing platforms for a possible update of vaccine sequences, and it is crucial that surveillance of genetic and antigenic changes in the global virus population is done alongside experiments to elucidate the phenotypic impacts of mutations. In this Review, we summarize the literature on mutations of the SARS-CoV-2 spike protein, the primary antigen, focusing on their impacts on antigenicity and contextualizing them in the protein structure, and discuss them in the context of observed mutation frequencies in global sequence datasets.

Post-retrieval Distortions of Self-Referential Negative Memory: Valence Consistency Enhances Gist-Directed False, While Non-negative Interference Generates More Intrusive Updates

According to the theory of reconsolidation, the contents of an original memory can be updated after reactivation with subsequent new learnings. However, there seems to be a lack of an appropriate behavioral paradigm to study the reconsolidation of explicit self-related memory, which is of great significance to further explore its cognitive neural mechanism in the future. In two separate experiments, we adapted a trial-by-trial interfering paradigm with a self-episodic simulation process and investigated (1) whether it is possible to reconsolidate negative memories under the new behavioral paradigm and (2) how the emotional valence of post-retrieval interference material affects the reconsolidation of negative memories. The results showed that the negative memories under trial-by-trial self-simulation can be degraded and updated via post-retrieval interference processes. Individuals whose original memories were reactivated by initial background cues and who were then presented with new interference situations were less able to recall original scenes and showed more memory intrusions on these scenes than those who had experienced new learning without reactivation or only reactivation without interference. Furthermore, the extent and manner of memory change/updating were greatly influenced by the characteristics of interference information. For memories with negative valences, new learning materials with the same valence produced superior interference effects in the form of lower correct recalls and more integrated false; whereas the neutral interference materials can cause more memory intrusion. Post-retrieval memory distortions of negative self-memory may underlie different functional mechanisms.