Reconstructing ancient protein alphabets: what can we truly infer?

The canonical amino acid alphabet has remained remarkably stable since life's early stages, yet the factors that shaped its emergence remain debated. Early views emphasized prebiotic availability and the expansion of metabolic pathways, but recent advances (particularly from protein biophysics and deep phylogenetics) have brought new perspectives to this question. Together, these views agree that the canonical alphabet emerged from a chemically restricted repertoire and was gradually tightened by metabolic innovation and selection for foldable, functional proteins. However, these approaches can yield inconsistent trajectories, underscoring how much remains unresolved. Here, we compare insights from four lines of evidence, highlight their limitations, and argue that our chronology of amino acid recruitment should be based on where the approaches converge.

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Publication Details

Journal
Trends in Genetics
Published
2026-03-01
DOI
https://doi.org/10.1016/j.tig.2026.03.002
Primary Topic
Machine Learning in Bioinformatics
Type
article
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article

Reconstructing ancient protein alphabets: what can we truly infer?

Stephen D. Fried, Georg Hochberg, Klara Hlouchova
Trends in Genetics
Machine Learning in Bioinformatics
article

Reconstructing ancient protein alphabets: what can we truly infer?

Stephen D. Fried, Georg Hochberg, Klara Hlouchova
article en

Abstract

The canonical amino acid alphabet has remained remarkably stable since life's early stages, yet the factors that shaped its emergence remain debated. Early views emphasized prebiotic availability and the expansion of metabolic pathways, but recent advances (particularly from protein biophysics and deep phylogenetics) have brought new perspectives to this question. Together, these views agree that the canonical alphabet emerged from a chemically restricted repertoire and was gradually tightened by metabolic innovation and selection for foldable, functional proteins. However, these approaches can yield inconsistent trajectories, underscoring how much remains unresolved. Here, we compare insights from four lines of evidence, highlight their limitations, and argue that our chronology of amino acid recruitment should be based on where the approaches converge.

Trends in Genetics
Johns Hopkins University (US), Philipps University of Marburg (DE), Charles University (CZ), Johns Hopkins Medicine (US), Loewe Center for Synthetic Microbiology (DE)
Openalex Percentile: Top 6%
Machine Learning in Bioinformatics
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Reconstructing ancient protein alphabets: what can we truly infer? — Stephen D. Fried, Georg Hochberg, et al. · Trends in Genetics (2026) | TGRS Research Map | TGRS