iGem ProgERASE project Application of AI-Powered Pipelines in Synthetic Biology to Intrinsically Disordered Protein Targets

Artificial intelligence is revolutionizing synthetic biology by enabling rapid analysis, modeling, and design of complex biological systems. AI-powered tools, such as AlphaFold and advanced docking algorithms, now allow for accurate structure prediction and ligand discovery even for elusive targets. In this work, I will provide an in-depth exploration of how specific AI-driven software was integrated into a custom bioinformatics pipeline developed by our team to compete in the iGEM international competition. This pipeline was designed for the de novo prediction of a ligand specifically targeting progerin, a mutant form of lamin A characterized by the presence of a disordered region and associated with Hutchinson-Gilford Progeria Syndrome. By examining these AI methodologies within our workflow, this thesis highlights both the opportunities and challenges of applying artificial intelligence to ligand discovery in synthetic biology.