Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention.

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

Identification, prioritization, and evaluation of RlpA protein as a target against multidrug-resistant Pseudomonas aeruginosa.

According to the World Health Organization, infectious diseases, particularly those caused by multidrug-resistant bacteria (MDR), are projected to claim the lives of 15 million people by 2050. Septicemia carries a higher morbidity and mortality rate than infections caused by susceptible Pseudomonas aeruginosa, and MDR-mediated ocular infections can lead to impaired vision and blindness. To identify and develop a potential drug against MDR P. aeruginosa, we employed in silico reverse genetics-based target mining, drug prioritization, and evaluation. Rare Lipoprotein A (RlpA) was selected as the target protein, and its crystal structure was geometrically optimized. Molecular docking and virtual screening analyses revealed that RlpA exhibits strong binding affinity with 11 compounds. Among these, 3-chlorophthalic acid was evaluated, and subsequent in vitro assays demonstrated significant anti-Pseudomonas activity with negligible cytotoxicity. The compound was further evaluated against both drug-susceptible and MDR P. aeruginosa strains in vitro, with cytotoxicity assessed using an MTT assay. The study demonstrated that 3-chlorophthalic acid exhibits potent anti-Pseudomonas activity with minimal toxicity to host cells. Consequently, this compound emerges as a promising candidate against MDR P. aeruginosa, warranting further investigation.

Draft genome sequence of blaVeb-1, blaoxa-10producing multi-drug resistant (MDR) Pseudomonas aeruginosastrain VRFPA09 recovered from bloodstream infection

Pseudomonas aeruginosa (P. aeruginosa) bacteremia causes significant mortality rate due to emergence of multidrug resistant (MDR) nosocomial infections. We report the draft genome sequence of P. aeruginosa strain VRFPA09, a human bloodstream isolate, phenotypically proven as MDR strain. Whole genome sequencing on VRFPA09, deciphered betalactamase encoding bla_veb-1 and bla_OXA-10genes and multiple drug resistance, virulence factor encoding genes.