Characterization of host receptor interaction with envelop protein of Kyasanur forest disease virus and predicting suitable epitopes for vaccine candidate

Computational Exploration of Potential Pharmacological Inhibitors Targeting the Envelope Protein of the Kyasanur Forest Disease Virus

The limitations of the current vaccination strategy for the Kyasanur Forest Disease virus (KFDV) underscore the critical need for effective antiviral treatments, highlighting the crucial importance of exploring novel therapeutic approaches through in silico drug design. Kyasanur Forest Disease, caused by KFDV, is a tick-borne disease with a mortality of 3-5% and an annual incidence of 400 to 500 cases. In the early stage of infection, the envelope protein plays a crucial role by facilitating host-virus interactions. The objective of this research is to develop effective antivirals targeting the envelope protein to disrupt the virus-host interaction. In line with this, the 3D structure of the envelope protein was modeled and refined through molecular modeling techniques, and subsequently, ligands were designed via de novo design and pharmacophore screening, yielding 12 potential hits followed by ADMET analysis. The top five candidates underwent geometry optimization and molecular docking. Notably, compounds L4 (SA28) and L3 (CNP0247967) are predicted to have significant binding affinities of -8.91 and -7.58 kcal/mol, respectively, toward the envelope protein, based on computational models. Both compounds demonstrated stability during 200 ns molecular dynamics simulations, and the MM-GBSA binding free-energy values were -85.26 ± 4.63 kcal/mol and -66.60 ± 2.92 kcal/mol for the envelope protein L3 and L4 complexes, respectively. Based on the computational prediction, it is suggested that both compounds have potential as drug candidates for controlling host-virus interactions by targeting the envelope protein. Further validation through in-vitro assays would complement the findings of the present in silico investigations.

High-throughput computational screening for identification of potential hits against bacterial Acriflavine resistance protein B (AcrB) efflux pump

Antibiotic resistance is a pressing global health challenge, driven in part by the remarkable efflux capabilities of efflux pump in AcrB (Acriflavine Resistance Protein B) protein in Gram-negative bacteria. In this study, a multi-approached computational screening strategy encompassing molecular docking, In silico absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis, druglikeness assessment, molecular dynamics simulations and density functional theory studies was employed to identify novel hits capable of acting against AcrB-mediated antibiotic resistance. Ligand library was acquired from the COCONUT database. Performed computational analyses unveiled four promising hit molecules (CNP0298667, CNP0399927, CNP0321542 and CNP0269513). Notably, CNP0298667 exhibited the highest negative binding affinity of -11.5 kcal/mol, indicating a possibility of strong potential to disrupt AcrB function. Importantly, all four hits met stringent druglikeness criteria and demonstrated favorable in silico ADMET profiles, underscoring their potential for further development. MD simulations over 100 ns revealed that the CNP0321542-4DX5 and CNP0269513-4DX5 complexes formed robust and stable interactions with the AcrB efflux pump. The identified hits represent a promising starting point for the design and optimization of novel therapeutics aimed at combating AcrB-mediated antibiotic resistance in Gram-negative bacteria.Communicated by Ramaswamy H. Sarma.