Computational Discovery of Angiotensin-(1-7)-like Peptides Targeting the MAS Receptor from the Genomic Dark Matter of

Non-coding regions of eukaryotic genomes, once considered transcriptionally inert, are increasingly recognized as potential sources of bioactive peptides. Here, we mined the intergenic "genomic dark matter" of Saccharomyces cerevisiae to identify peptide ligands targeting the angiotensin-(1-7)/Mas receptor axis. Intergenic sequences were computationally translated in all reading frames and screened for short peptides with sequence and structural similarity to angiotensin-(1-7), a key modulator of the renin-angiotensin system. Two candidates showing 72-86% similarity were identified and structurally modeled. A homology-independent model of the human Mas receptor was generated, followed by molecular docking and 100-ns membrane-embedded molecular dynamics simulations to assess binding modes and complex stability. Both peptides adopted angiotensin-(1-7)-like conformations and engaged conserved receptor residues involved in ligand recognition. Docking and dynamics analyses indicated stable binding and sustained receptor engagement, with one peptide exhibiting enhanced interaction density and conformational stability relative to the native ligand. Overall, our results support the concept that non-coding genomic regions can encode functional peptide ligands and provide a scalable in silico framework for discovering peptide modulators of clinically relevant G protein-coupled receptors (GPCRs).