iGEM ProgERASE Project De novo protein binders design: facing the complexity of Progerin intrinsically disordered regions

The protein folding problem has long represented one of the greatest challenges in structural biology. The advent of Artificial Intelligence-based platforms, such as AlphaFold, has revolutionized structure prediction and opened new frontiers in de novo protein design. Within this field, generative models such as RFdiffusion and ProteinMPNN – developed at the Institute for Protein Design (IPD), directed by David Baker – have become benchmarks for Reverse Protein Folding. In this work, we design a synthetic peptide for the selective binding of the intrinsically disordered C-terminal region of Progerin, a pathogenic variant of Lamin A (LMNA) responsible for Hutchinson-Gilford Progeria Syndrome (HGPS). Our computational approach integrates the AI-based models AlphaFold3, RFdiffusion, ProteinMPNN, as well as the novel NeuroBind – developed by NeuroSnap – in combination with the molecular docking platforms HADDOCK and ClusPro. This thesis is part of the 2025 UniPadua-IT iGEM Project: ProgERASE.