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Ranjbar, M. M.

Publications and source records attributed to Ranjbar, M. M..

2 recordsLinked to original sources

In Silico Vaccine Design: Targeting Highly Epitopic Regions of Clostridium perfringens Type D Epsilon Toxin and Clostridium novyi Type B Alpha Toxin for Optimal Immunogenicity

Livestock infections caused by highly toxic bacteria pose significant challenges in veterinary medicine, often requiring complex and elusive treatment regimens. Developing effective vaccines tailored to combat these specific pathogens remains a pressing need within the field. Among the most formidable culprits are Clostridium perfringens type D and Clostridium novyi type B, notorious for their extreme toxicity and the difficulty in culturing them for vaccine production. In response to this challenge, our study endeavors to engineer a vaccine candidate capable of concurrently neutralizing the virulence of both bacterial strains. Leveraging computational techniques, we meticulously identified highly epitopic regions within C. perfringens Epsilon Toxin (ETX) and C. novyi Alpha Toxin (ATX), crucial targets for effective immunization. Through innovative fusion gene design, we integrated these epitopic regions alongside the PADRE-peptide sequence, serving as a universal adjuvant to bolster immune response. The culmination of our efforts materialized in the creation of Recombinant Fusion Protein D (rFPD), a novel vaccine construct poised to elicit robust and specific immune defenses against both bacterial species. By harnessing the power of in silico design and molecular engineering, our study heralds a promising stride towards mitigating the deleterious impact of livestock infections caused by these formidable pathogens.

bioinformatics↗

Optimizing Consensus Generation Algorithms for Highly Variable Amino Acid Sequence Clusters

Producing a functional consensus sequence is a preliminary bioinformatics task, which is a necessity for many research purposes. However, the existence of hypervariable regions in the input multiple sequence alignment files causes complications in generating a useful consensus sequence. The current methods for consensus generation, Threshold, and majority algorithms, have several problems, which exclude them as applicable algorithms for such highly variable sequence clusters. Hence, we designed a novel alternative algorithm for the same purpose. The algorithm was explained both using a mathematical formula and a practical implementation in Python programming language. A sequence set from HCV genotype 1b E2 protein has been utilized as a practical example for evaluating the algorithms performance. A few in silico tests have been performed on the output sequence and the results have been compared to results from other algorithms. Epitope-mapping analysis indicates the functionality of this algorithm, by preserving the hotspot residues in the consensus sequence, and the antigenicity index shows significant antigenicity of the consensus sequence. Moreover, phylogenetic analysis shows no significant change in the placement of the new consensus sequence on the phylogenetic tree compared to other algorithms. This approach will have several implications in designing a new vaccine for highly variable viruses such as HIV-1, Influenza, and Hepatitis C Viruses (HCV).

bioinformatics↗