Evidence map›Paper›PMID 39431545›Full record

ArticleMolecular biology and evolution2024

Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution.

Dinara R Usmanova, Germán Plata, Dennis Vitkup

Abstract read
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Article in Molecular biology and evolution, 2024. 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

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.

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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

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Dinara R UsmanovaDepartment of Systems Biology, Columbia University, New York, NY 10032, USA.ORCID 0000-0001-5031-0013
Germán PlataDepartment of Systems Biology, Columbia University, New York, NY 10032, USA.ORCID 0000-0002-6470-7748
Dennis VitkupDepartment of Systems Biology, Columbia University, New York, NY 10032, USA.ORCID 0000-0003-4259-8162

Funding

Discovery and analysis of brain circuits and cell types affected in autism and schizophreniaR01MH124923 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GOGOS, JOSEPH A, VITKUP, DENNIS · 2020 to 2024
$3.8M
Systems Biology of Protein and Phenotypic EvolutionR35GM131884 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI VITKUP, DENNIS · 2019 to 2023
$2.2M
NIGMS NIH HHS R35 GM131884NIGMS NIH HHS R35GM131884NIMH NIH HHS R01 MH124923
6 · The paper itself

Abstract

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

Indexed as

Evolution, MolecularAnimalsHumansProteinsProteinsexpression costfunctional optimizationmolecular clockprotein evolutionprotein function

Identifiers

PMID39431545
PMCPMC11523136

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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.