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Biology subjects

Kanji, A.

Publications and source records attributed to Kanji, A..

2 recordsLinked to original sources

Structural and Conformational Impact of Deleterious Spike Protein Mutations in SARS-CoV-2 Omicron Lineages

BackgroundA large number of mutations in the Spike (S) protein of the SARS-CoV-2 omicron variant have been noted to alter the receptor binding domain (RBD) and increase the binding surface and enhance the opening of the binding pocket. The cumulative effect of S1 and S2 subunit mutations can influence the conformational dynamics of the binding surface, facilitating the release of viral genome into host cells. AimThis study investigates the deleterious mutations across all Omicron lineages identified in our analysis and their effect on the conformational stability of RBD opening. MethodsWhole Genome Sequencing of 231 SARS-CoV-2 positive patients in Karachi, Pakistan, were performed using Illumina Miseq instrument and raw reads were analyzed using viralrecon pipeline. The mutational effects of omicron variant on the stability of S protein, including wild-type (7FG7), close (6VXX) and open (6VYB) states, were assessed through MD simulations. ResultsFour deleterious missense mutations (Tyr505His, Asn764Lys, Asp950Asn, Asn969Lys) were identified in the S1 and S2 subunit of the S protein of omicron variant. In the wildtype and open state mutant models, Tyr505His, Asp950Asn and Asn969Lys caused destabilizing effects, higher RMSDs vs. wild-type, and fluctuations in the RBD (438-510) region and S2 subunit (946-1010), compared to the native structure. These mutations increased the binding pocket propensity to open in mutant model compared to the native open conformation (6VYB). This structural change promoted trimer opening in the open state through -helix movement in the S2 subunit away from the RBD region. In the closed state, only S2 subunit mutations (Asp950Asn and Asn969Lys) lead to predicted destabilization through the movement of protomer C towards protomer B (RBD region). These S2 subunit mutations are predicted to stabilize the RBD "down" conformation potentially enhancing spike antigenic heterogeneity. ConclusionThis study highlighted the cumulative effect of S1 (Tyr505His) and S2 (Asp950Asn and Asn969Lys) subunits mutations on different S protein states, potentially controlling its conformational dynamics and presentation to host receptors. Future experimental studies are needed to elucidate the biological significance of these alterations, particularly by establishing a link between the identified mutations and their impact on viral biology.

bioinformatics↗

A Versatile Tool for Precise Variant Calling in Mycobacterium tuberculosis Genetic Polymorphisms

BackgroundWhole genome sequencing (WGS) facilitates the diagnosis of multidrug-resistant MDR-TB through the interpretation of sequence variations (SV) in Mycobacterium tuberculosis (MTB) genes. Information on phenotypic and genotypic resistance associations continues to evolve, it is important to identify SV within genes of interest. We developed an MTB-VCF variant calling pipeline that can compare against the reference genome for any gene of interest. We demonstrate its utility for calling SV in genes associated with Rifampicin (RIF), Isoniazid (INH), Ethambutol (EM), and Streptomycin (SM) resistance. MethodsMTB-VCF is a Python-based command line Variant Calling pipeline designed to streamline batch processing from raw reads (FastQ) files. SV called by MTB-VCF were compared with those identified by TBProfiler, KVARQ, CASTB, Mykrobe Predictor and Phy-ResSE pipelines. The sensitivity and Specificity of MTB-VCF SV calling were calculated against the drug susceptibility testing (DST) phenotype. ResultsMTB-VCF identified 868 SV present in 200 phenotypically resistant MDR-TB isolates. These were across rpsl, rrs, rpoB, inhA, katG, ahpC, gidB and embCAB genes. Of these, 684 SV were known to be associated with a resistance genotype, leading to a specificity of 97.75%. The SV called by the MTB-VCF was compared separately to resistance genotypes called by TB-Profiler, KvarQ, CASTB, Mykrobe Predictor, and PhyRes-SE pipelines, demonstrating a sensitivity of 99.5%. ConclusionThe MTB-VCF pipeline offers a rapid and accurate solution for identifying SV in target genes for interpretation later. It can be run in large batches, proving flexible computing that allows for the customization of core bioinformatic pipelines, enabling the analysis of WGS data from different technologies.

bioinformatics↗