Biology subjects

Alawi, K. M.

Publications and source records attributed to Alawi, K. M..

3 recordsLinked to original sources

bioRxiv2024

Designing a multi-serotype Dengue virus vaccine: an in silico approach to broad-spectrum immunity

Dengue virus infection represents a major global health issue, with four distinct serotypes complicating the challenge of developing a vaccine due to the need for balanced, long-lasting immunity against all serotypes. Current vaccines have limitations, including an increased risk of severe dengue in seronegative individuals and moderate efficacy, highlighting the need for more effective solutions. Our study aimed to design a multi-serotype Dengue virus vaccine using a computational approach to achieve broad-spectrum immunity. We employed advanced computational tools and algorithms to predict B-cell and T-cell epitopes, ensuring the selection of antigenic targets that provide comprehensive protection against all four serotypes. The methodology included tools for B-cell epitope prediction, tools for MHC class II and I peptide predictions, and tools for toxicity and allergenicity screening to ensure the safety of the vaccine candidates. Our results identified 21 B-cell epitopes, 15 CTL peptides, and 12 HTL peptides, validated for safety regarding toxicity and allergenic potential. The vaccine construct incorporated the adjuvant {beta}-defensin-3 and specific linkers to enhance immunogenicity and stability. Tertiary structure prediction, Ramachandran plot analysis, and stereochemical examination confirmed the stability and quality of the vaccine model. These findings demonstrate the potential of computational methods in addressing the complex challenges of Dengue virus vaccine development. Our computational approach offers a novel pathway for vaccine design, potentially accelerating the development of effective multi-serotype vaccines. This study provides a promising foundation for future research and clinical validation, marking a significant step forward in dengue vaccine development.

immunology↗

bioRxiv2024

Comparative analysis of Endoxifen, Tamoxifen and Fulvestrant: A Bioinformatics Approach to Uncover Mechanisms of Action in Breast Cancer

Breast cancer remains a significant health challenge, with estrogen receptor positive (ER+) subtypes being particularly prevalent forms of breast cancer. Current anti-estrogen therapies, such as tamoxifen and fulvestrant, have limitations, including partial agonist activity and resistance development, which evidence the need for more potent alternatives. Endoxifen, a metabolite of tamoxifen, has emerged as a promising breast cancer therapeutic candidate due to its superior anti-estrogenic effects and side effect profile. The omics signatures for endoxifen, tamoxifen and fulvestrant, obtained from publicly available datasets, were aggregated and harmonized by means of the PandaOmics platform, a commercially available target-discovery platform using multiple AI engines including generative pretrained transformers. Pathway enrichment analyses provided insight into these agents mechanisms of action (MOA) in breast cancer. The analyses revealed unexpected variances in several key pathways from expected interactions via estrogen-dependent and independent effects. All three drugs downregulated estrogen signaling and cell cycle-related pathways, such as E2F targets, G2-M checkpoints, Myc targets, and mitotic spindle, and stimulated apoptosis. Fulvestrant and tamoxifen activated pro-inflammatory and immune pathways and perturbed epithelial-mesenchymal transition (EMT). Endoxifen perturbed the PI3K/Akt/mTORC1 pathway, pursuant to distinct molecular mechanisms compared to its parent compound, tamoxifen, and fulvestrant. In summary, advanced AI-driven methodologies demonstrate the capacity to analyze multi-omics data in a comparative way to advance the understanding of endocrine therapy mechanisms in breast cancer. This insight into the distinct effects of endoxifen, tamoxifen, and fulvestrant may aid in selecting the most effective therapies for specific indications and in identifying drug-specific biomarkers.

cancer biology↗

bioRxiv2024

Precious3GPT: Multimodal Multi-Species Multi-Omics Multi-Tissue Transformer for Aging Research and Drug Discovery

We present a multimodal multi-species multi-omics multi-tissue transformer for aging research and drug discovery capable of performing multiple tasks such as age prediction across species, target discovery, tissue, sex, and disease sample classification, drug sensitivity prediction, replication of omics response and prediction of biological and phenotypic response to compound treatment. This model combines textual, tabular, and knowledge graph-derived representations of biological experiments to provide insights into molecular-level biological processes. We demonstrate that P3GPT has developed an intuition for the interactions between compounds, pathologies, and gene regulation in the context of multiple species and tissues. In these areas, it outperforms existing LLMs and we highlight its utility in diverse case studies. P3GPT is a general model that may be used as a target identification tool, aging clock, digital laboratory, and scientific assistant. The model is intended as a community resource available open source as well as via a Discord server.

bioinformatics↗