Hendra virus (HeV) is a highly pathogenic zoonotic paramyxovirus that poses a serious threat to human and equine health, yet no approved antivirals or vaccines currently exist. RNA-dependent RNA polymerase (RdRp) of Hendra virus represents a critical and attractive target for antiviral drug development, given its essential role in both viral genome replication and mRNA transcription. Due to the lack of a human homolog, it is more druggable and less likely to cause host toxicity. Its sequence conservation among related paramyxoviruses further highlights its potential for the development of broad-spectrum inhibitors. This study offers the first comprehensive computational analysis of the Hendra virus RdRp, potentially promising FDA-approved drugs as possible inhibitors. A homology model of RdRp was generated in the absence of experimental three-dimensional (3D) structure, followed by virtual screening and molecular dynamics (MD) simulations to evaluate the drug binding and stability. Based on the highest energy, four FDA-approved drugs selected were menadiol diphosphate (−49.88 kcal/mol), masoprocol (−39.69 kcal/mol), pamidronic acid (−34.29 kcal/mol), and dinoprostone (−46.90 kcal/mol). Furthermore, these compounds exhibited significant interactions with the catalytic GDNE motif. With strong conformational stability and pharmacokinetic profile, masoprocol and menadiol diphosphate showed the most stable and energetically favorable interactions within the RdRp active site. These findings suggest their potential as repurposed therapeutic candidates against Hendra virus infection and they provide a structural basis for the development of broad-spectrum paramyxovirus inhibitors, justifying additional experimental confirmation.

Chandipura virus (CHPV) is a recently discovered neurotropic virus associated with severe encephalitis in children and for which there is currently no specific antiviral treatment. The viral RNA‐dependent RNA polymerase (RdRp) is an attractive antiviral target because of its essential role in viral RNA synthesis and the presence of conserved catalytic motifs required for polymerase function. In this study, a structure‐based computational approach was employed to investigate coumarin‐triazole derivatives as potential inhibitors targeting the conserved QGDNQ motif‐containing region of CHPV RdRp. A homology model of CHPV RdRp was generated and structurally evaluated, followed by prediction of the ligand‐binding pocket. A set of 80 coumarin‐triazole derivatives was subjected to extra‐precision (XP) molecular docking, and compounds 7a, 7b, and 7d were identified as potential candidates based on their docking scores and predicted binding free energies. Compound 7a showed a favorable docking score and interactions with the residues close to the catalytic motif, especially ASN705. The ADMET analysis also indicated favorable physicochemical and pharmacokinetic properties for the selected compounds. Molecular dynamics simulations were subsequently performed for 200 ns for both the protein‐only system and the ligand‐bound complexes to assess their structural stability and conformational behavior. RMSD and RMSF analysis revealed that Compound 7a exhibited comparatively lower residue fluctuations among the ligand‐bound systems. PCA indicated a more compact conformational distribution comparable to that observed for the remdesivir‐bound system. Post‐MD MM/GBSA analysis further supported the favorable binding of Compound 7a. Overall, the integrated computational analysis identified Compound 7a as a promising coumarin‐triazole candidate for further investigation against CHPV RdRp. Experimental studies are required to validate its antiviral activity and mechanism, and the findings provide a basis for further optimization of the coumarin‐triazole scaffold toward potential non‐nucleoside CHPV RdRp inhibitors.

Many kinases are known to homo- or hetero-merize and phosphorylate themselves or each other. Phosphorylation by itself (autophosphorylation) is mechanistically perceived as either cis- or trans-interactions. Identification of autophosphorylation sites in kinases provides an opportunity to assess the activity of the kinome in phosphoproteome datasets as an efficient indicator of kinase activity. In order to predict kinase activity-associated autophosphorylation sites, we first encompassed the currently reported homodimeric interactions between kinases from various sources. Subsequently, we accounted for the autophosphorylation sites in kinases that are predicted by phosphomotif screening approaches, in vitro kinase assays, and those perceived based on phosphomotif mutant analysis. Together, we identified 4184 autophosphorylation sites in 386 kinases, and among them, 315 kinases were reported with homodimeric protein–protein interactions. Furthermore, we analysed 361 kinases that harbor multiple autophosphorylation sites and 25 kinases with a single autophosphosite based on contemporary kinase–substrate interactions. Among the 386 kinases, autophosphosites for 17 kinases were derived through high-throughput or low-throughput approaches, and for 80 kinases, they were predicted based on their phosphomotif analysis. The remaining 289 were based on predictions by multiple tools or were reported in more than one dataset. There were many instances in which, although one auto-phosphorylation site was reported, multiple other sites were also predicted. In this regard, we analysed the instances of phosphosites in kinases that are already associated with the induction or inhibition of kinase activity and identified 223 kinases with one or more sites with functional association. To evaluate their potential as auto-phosphorylation sites, we systematically assembled 1572 profiles and 978 differential cellular phosphoproteome datasets. Subsequently, we accounted for the frequency of co-detection or co-differential regulation of these autophosphosites with multiple kinase activity-associated sites within each kinase and further accounted for their known/predicted substrate phosphorylation sites. Together, we provide a reference compendium of autophosphorylation sites secluded in the human kinome and their co-regulation with substrates based on global phosphoproteome datasets, AutoPhosDb. As indicators of kinase activity, these phosphosites in kinases would serve as a surrogate to enrich the active kinases and their potential substrate-specificity in phosphoproteome datasets.