{"id":"doi:10.1016/j.tibs.2026.08.002","kind":"review","title":"Minimal-alphabet proteins by evolution, engineering, and design","authors":["Buchel F","Giacobelli VG","Hlouchová K"],"author_count":3,"year":2026,"published":"2026-08-29","venue":"Trends in biochemical sciences","cited_by":0,"doi":"10.1016/j.tibs.2026.08.002","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.tibs.2026.08.002","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.tibs.2026.08.002","updated":null,"pmid":"42665456","pmcid":null,"openalex_id":null,"pdf_url":null,"abstract":"Research on the structural and functional potential of minimal amino acid alphabets is shifting from reductive 'pruning' of extant proteins to bottom-up exploration of combinatorial sequence space. This review highlights the experimental and computational toolkits driving this transition. We discuss how solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering. Integrating these approaches with generative machine learning and de novo protein design allows researchers to move beyond observing what evolution produced toward exploring what chemistry permits. Together, these advances are redirecting the field from simplifying extant proteins to uncovering the physicochemical principles governing protein foldability, while expanding the design space beyond the constraints imposed by biological evolution.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Minimal-alphabet proteins by evolution, engineering, and design\" by Buchel F, Giacobelli VG, Hlouchová K, https://doi.org/10.1016/j.tibs.2026.08.002, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"protein-design","name":"AI Protein Design & Structure Prediction","domain_slug":"biotech"}],"cited_in_dossiers":[]}