Evidence map›Paper›PMID 22750993›Full record

ArticleJournal of biosciences2012

A possible molecular metric for biological evolvability.

Aditya Mittal, B Jayaram

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of biosciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

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

2 authors at 1 institution in 1 country.

Aditya MittalKusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi 110 016, India. amittal@bioschool.iitd.ac.in
B Jayaram
Indian Institute of Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteins manifest themselves as phenotypic traits, retained or lost in living systems via evolutionary pressures. Simply put, survival is essentially the ability of a living system to synthesize a functional protein that allows for a response to environmental perturbations (adaptation). Loss of functional proteins leads to extinction. Currently there are no universally applicable quantitative metrics at the molecular level for either measuring 'evolvability' of life or for assessing the conditions under which a living system would go extinct and why. In this work, we show emergence of the first such metric by utilizing the recently discovered stoichiometric margin of life for all known naturally occurring (and functional) proteins. The constraint of having well-defined stoichiometries of the 20 amino acids in naturally occurring protein sequences requires utilization of the full scope of degeneracy in the genetic code, i.e. usage of all codons coding for an amino acid, by only 11 of the 20 amino acids. This shows that the non-availability of individual codons for these 11 amino acids would disturb the fine stoichiometric balance resulting in non-functional proteins and hence extinction. Remarkably, these amino acids are found in close proximity of any given amino acid in the backbones of thousands of known crystal structures of folded proteins. On the other hand, stoichiometry of the remaining 9 amino acids, found to be farther/distal from any given amino acid in backbones of folded proteins, is maintained independent of the number of codons available to synthesize them, thereby providing some robustness and hence survivability.

Indexed as

Evolution, MolecularModels, GeneticAmino AcidsAmino Acid SequenceCodonGenetic CodeMutationProteinsAmino AcidsCodonProteins

Identifiers

PMID22750993
OpenAlexW2001279021

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.