Sarkar, A. P. | Search bioRxiv

bioRxiv2026

A Curvature Guided Composite Kernel Framework for Differential Gene Selection in Cancer Transcriptomics

Identification of differentially expressed genes is a crucial step for downstream tasks on gene data such as biomarker discovery, drug target identification. Traditional Methods assume negative binomial distribution on RNA-sequence data and models the DEGs using either generalized linear models or by estimating dispersion and assumption of mean-variance rate. The proposed method uses axiomatic approach by using quantum mechanics principles to project transcript data onto a Hilbert space using a composite kernel. Using the curvature generated by the transcripts on the latent manifold within the Hilbert space, a gravitational search inspired mechanism is used to identify the optimal number of differentially expressed genes by minimizing a representational loss function, and a reduced gene feature space is constructed as the potential differentially expressed genes. The proposed method has been compared with existing empirical methods for validation using proper statistical and biological benchmark analysis.

bioinformatics↗

bioRxiv2024

Design of a novel epitope-based tetravalent subunit vaccine against dengue virus: an immunoinformatics approach

Dengue imposes a profound global impact, with millions affected annually. Its transmission by Aedes mosquitoes poses significant challenges to combat, aggravated by urbanization and climate change. Despite efforts, no impeccable antiviral treatment exists to date, highlighting the urgency for a vaccine. Developing one encounters hurdles like the four distinctive serotypes of the virus and complex immune responses. In this study, we employed an immunoinformatics approach to design an epitope-based tetravalent subunit vaccine aimed at confronting all DENV serotypes. The study contemplates epitopes prediction, toxicity assessment, molecular docking, molecular dynamics (MD) simulations, immune simulations. On sequence retrieval, the epitopes were predicted and prioritized. The sequence of the finally designed vaccine was reached after a broad analysis of the antigenicity scores, serotype coverage, and population coverage. Human {beta}-defensin 3 has been added as an adjuvant to the core vaccine sequence that comprises 23 epitopes and linkers. Notably, the vaccine has the highest antigenicity (0.9319) and 97.35% population coverage worldwide. The molecular docking operations of the vaccine with toll-like receptor 2 (TLR2) and TLR4 showed promising interactions with lowest energies of -1240.5 kJ/mol (76 members) and -1393.3 kJ/mol (40 members), respectively. Molecular dynamics (MD) simulations were run up to 200 nanoseconds, and the complexes of the vaccine with TLR2 and TLR4 were found to be very stable and flexible. Moreover, in immune simulations, the vaccine evoked robust immune responses. These findings suggest that our vaccine outperforms any other Dengue vaccine developed to date. However, since this study was conducted through in silico methods, in vitro and in vivo validations are required to confirm the vaccine as a potential candidate for clinical trials. Author summaryWe developed a novel epitope-based tetravalent subunit vaccine against all dengue virus (DENV) serotypes using immunoinformatics. Our vaccine demonstrated high antigenicity (0.9319) and wide population coverage (97.35%). Molecular docking and dynamics simulations indicated strong interactions with immune receptors which is crucial for the vaccines activity inside human body. Moreover, immune simulations showed robust responses indicating proper immunity against DENV. Therefore, our vaccine offers a promising solution to dengue fever, pending further in vitro and in vivo validations for clinical trials. Notably, this is the first ever dengue vaccine with such high efficacy in terms of immunoinformatics and vaccinomic approaches.

bioinformatics↗