Hongzhong Lu

Abstract Constructing high-quality genome-scale metabolic models (GEMs) for non-model organisms remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment, protein language models (PLMSearch), and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. AlphaGEM enhances homologous relationship identification compared to traditional sequence-based methods.

Nyckelord: MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Systems biology; transcriptomics; bulk RNA-seq; single cell RNA-seq; disease target identification; computational drug repositioning; chronic diseases; Biotechnology; Bioteknologi; Sammanfattning: Chronic diseases such as metabolic, renal, or liver disorders involve complex interactions of genes, cell types, and tissues.

Microorganisms have been increasingly engineered to produce a wide variety of chemicals in a more environmentally friendly manner (Lee et al., 2019; López et al., 2022; Volk et al., 2023). However, redirecting cellular metabolism toward the production of non-natively overproduced chemicals is challenging and often requires extensive metabolic engineering effort.