Evidence map›Paper›PMID 36543797›Full record

ArticleNature communications2022

Proteome-wide 3D structure prediction provides insights into the ancestral metabolism of ancient archaea and bacteria.

Weishu Zhao, Bozitao Zhong, Lirong Zheng, Pan Tan, Yinzhao Wang, Hao Leng, Nicolas de Souza, Zhuo Liu, Liang Hong, Xiang Xiao

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

7 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. 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

10 authors at 2 institutions in 3 countries.

Weishu Zhao *State Key Laboratory of Microbial Metabolism, International Center for Deep Life Investigation (IC-DLI), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.ORCID 0000-0002-0753-8300
Bozitao Zhong *State Key Laboratory of Microbial Metabolism, International Center for Deep Life Investigation (IC-DLI), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.ORCID 0000-0001-9363-6099
Lirong ZhengInstitute of Natural Sciences, Shanghai National Center for Applied Mathematics (SJTU Center) and MOE-LSC, Shanghai Jiao Tong University, 200240, Shanghai, China.ORCID 0000-0001-6803-5048
Pan TanInstitute of Natural Sciences, Shanghai National Center for Applied Mathematics (SJTU Center) and MOE-LSC, Shanghai Jiao Tong University, 200240, Shanghai, China.ORCID 0000-0003-2086-0940
Yinzhao WangState Key Laboratory of Microbial Metabolism, International Center for Deep Life Investigation (IC-DLI), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.ORCID 0000-0001-8715-1386
Hao LengState Key Laboratory of Microbial Metabolism, International Center for Deep Life Investigation (IC-DLI), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China.
Nicolas de SouzaAustralian Nuclear Science and Technology (ANSTO), Locked Bag 2001, Kirrawee DC, Sydney, NSW, 2232, Australia.ORCID 0000-0003-4099-9544
Zhuo LiuInstitute of Natural Sciences, Shanghai National Center for Applied Mathematics (SJTU Center) and MOE-LSC, Shanghai Jiao Tong University, 200240, Shanghai, China.
Liang HongInstitute of Natural Sciences, Shanghai National Center for Applied Mathematics (SJTU Center) and MOE-LSC, Shanghai Jiao Tong University, 200240, Shanghai, China. hongl3liang@sjtu.edu.cn.ORCID 0000-0003-0107-336X
Xiang XiaoState Key Laboratory of Microbial Metabolism, International Center for Deep Life Investigation (IC-DLI), School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 200240, Shanghai, China. zjxiao2018@sjtu.edu.cn.ORCID 0000-0001-6785-7728
Shanghai Jiao Tong University · CNAustralian Nuclear Science and Technology Organisation · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ancestral metabolism has remained controversial due to a lack of evidence beyond sequence-based reconstructions. Although prebiotic chemists have provided hints that metabolism might originate from non-enzymatic protometabolic pathways, gaps between ancestral reconstruction and prebiotic processes mean there is much that is still unknown. Here, we apply proteome-wide 3D structure predictions and comparisons to investigate ancestorial metabolism of ancient bacteria and archaea, to provide information beyond sequence as a bridge to the prebiotic processes. We compare representative bacterial and archaeal strains, which reveal surprisingly similar physiological and metabolic characteristics via microbiological and biophysical experiments. Pairwise comparison of protein structures identify the conserved metabolic modules in bacteria and archaea, despite interference from overly variable sequences. The conserved modules (for example, middle of glycolysis, partial TCA, proton/sulfur respiration, building block biosynthesis) constitute the basic functions that possibly existed in the archaeal-bacterial common ancestor, which are remarkably consistent with the experimentally confirmed protometabolic pathways. These structure-based findings provide a new perspective to reconstructing the ancestral metabolism and understanding its origin, which suggests high-throughput protein 3D structure prediction is a promising approach, deserving broader application in future ancestral exploration.

Indexed as

ArchaeaProteomeBacteriaEvolution, MolecularPhylogenyProteome

Identifiers

PMID36543797
PMCPMC9772386
OpenAlexW4313340197

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.