Evidence map›Paper›PMID 35666290›Full record

ArticleJournal of molecular evolution2022

A Closer Look at Non-random Patterns Within Chemistry Space for a Smaller, Earlier Amino Acid Alphabet.

Christopher Mayer-Bacon, Markus Meringer, Riley Havel, José C Aponte, Stephen Freeland

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Article in Journal of molecular evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Christopher Mayer-BaconDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, USA. cmayerb1@umbc.edu.ORCID 0000-0002-5863-7712
Markus MeringerEarth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen-Wessling, Germany.
Riley HavelDepartment of Physics, University of Central Florida, Orlando, FL, 32816, USA.
José C AponteSolar System Exploration Division, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA.
Stephen FreelandDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent findings, in vitro and in silico, are strengthening the idea of a simpler, earlier stage of genetically encoded proteins which used amino acids produced by prebiotic chemistry. These findings motivate a re-examination of prior work which has identified unusual properties of the set of twenty amino acids found within the full genetic code, while leaving it unclear whether similar patterns also characterize the subset of prebiotically plausible amino acids. We have suggested previously that this ambiguity may result from the low number of amino acids recognized by the definition of prebiotic plausibility used for the analysis. Here, we test this hypothesis using significantly updated data for organic material detected within meteorites, which contain several coded and non-coded amino acids absent from prior studies. In addition to confirming the well-established idea that "late" arriving amino acids expanded the chemistry space encoded by genetic material, we find that a prebiotically plausible subset of coded amino acids generally emulates the patterns found in the full set of 20, namely an exceptionally broad and even distribution of volumes and an exceptionally even distribution of hydrophobicities (quantified as logP) over a narrow range. However, the strength of this pattern varies depending on both the size and composition the library used to create a background (null model) for a random alphabet, and the precise definition of exactly which amino acids were present in a simpler, earlier code. Findings support the idea that a small sample size of amino acids caused previous ambiguous results, and further improvements in meteorite analysis, and/or prebiotic simulations will further clarify the nature and extent of unusual properties. We discuss the case of sulfur-containing amino acids as a specific and clear example and conclude by reviewing the potential impact of better understanding the chemical "logic" of a smaller forerunner to the standard amino acid alphabet.

Indexed as

Amino AcidsProteinsGenetic CodeHumansAmino AcidsProteinsAbiogenesis Chemistry spaceAmino acidsComputational chemistryEvolution

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.