In Silico Investigation of Parkin-Activating Mutations Using Simulations and Network Modeling.

Complete loss-of-function mutations in the PRKN gene are a major cause of early-onset Parkinson's disease (PD). PRKN encodes the Parkin protein, an E3 ubiquitin ligase that works in conjunction with the ubiquitin kinase PINK1 in a distinct quality control pathway to tag damaged mitochondria for autophagic clearance, i.e., mitophagy. According to previous structural investigations, Parkin protein is typically kept in an inactive conformation via several intramolecular, auto-inhibitory interactions. Here, we performed molecular dynamics simulations (MDS) to provide insights into conformational changes occurring during the de-repression of Parkin and the gain of catalytic activity. We analyzed four different Parkin-activating mutations that are predicted to disrupt certain aspects of its auto-inhibition. All four variants showed greater conformational motions compared to wild-type protein, as well as differences in distances between domain interfaces and solvent-accessible surface area, which are thought to play critical roles as Parkin gains catalytic activity. Our findings reveal that the studied variants exert a notable influence on Parkin activation as they alter the opening of its closed inactive structure, a finding that is supported by recent structure- and cell-based studies. These findings not only helped further characterize the hyperactive variants but overall improved our understanding of Parkin's catalytic activity and nominated targets within Parkin's structure for potential therapeutic designs.

Structural and Functional Characterization of the Most Frequent Pathogenic PRKN Substitution p.R275W.

Mutations in the PINK1 and PRKN genes are the most frequent genetic cause of early-onset Parkinson disease. The pathogenic p.R275W substitution in PRKN is the most frequent substitution observed in patients, and thus far has been characterized mostly through overexpression models that suggest a possible gain of toxic misfunction. However, its effects under endogenous conditions are largely unknown. We used patient fibroblasts, isogenic neurons, and post-mortem human brain samples from carriers with and without PRKN p.R275W to assess functional impact. Immunoblot analysis and immunofluorescence were used to study mitophagy activation, and mitophagy execution was analyzed by flow cytometry of the reporter mitoKeima. The functional analysis was accompanied by structural investigation of PRKN p.R275W. We observed lower PRKN protein in fibroblasts with compound heterozygous p.R275W mutations. Isogenic neurons showed an allele-dose dependent decrease in PRKN protein. Lower PRKN protein levels were accompanied by diminished phosphorylated ubiquitin and decreased MFN2 modification. Mitochondrial degradation was also allele-dose dependently impaired. Consistently, PRKN protein levels were drastically reduced in human brain samples from p.R275W carriers. Finally, structural simulations showed significant changes in the closed form of PRKN p.R275W. Our data suggest that under endogenous conditions the p.R275W mutation results in a loss-of-function by destabilizing PRKN.

Statistical Mechanics Metrics in Pairing and Parsing In Silico and Phenotypic Data of a Novel Genetic NFκB1 (c.T638A) Variant.

(1) Background: Mutations in NFκB1, a transcriptional regulator of immunomodulating proteins, are a known cause of inborn errors of immunity. Our proband is a 22-year-old male with a diagnosis of common variable immunodeficiency (CVID), cytopenias with massive splenomegaly, and nodular regenerative hyperplasia of the liver. Genetic studies identified a novel, single-point mutation variant in NFκB1, c. T638A p. V213E. (2) Methods: Next-generation panel sequencing of the patient uncovered a novel single-point mutation in the NFκB1 gene that was modeled using the I-TASSER homology-modeling software, and molecular dynamics were assessed using the YASARA2 software (version 20.14.24). (3) Results: This variant replaces valine with glutamic acid at position 213 in the NFκB1 sequence. Molecular modeling and molecular dynamic studies showed altered dynamics in and around the rel homology domain, ankyrin regions, and death domain of the protein. We postulate that these changes alter overall protein function. (4) Conclusions: This case suggests the pathogenicity of a novel variant using protein-modeling techniques and molecular dynamic simulations.

Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics.

With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.