<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Goldman AD</submitter><funding>NLM NIH HHS</funding><funding>NIH HHS</funding><pagination>e39912</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC3438178</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>7(9)</volume><pubmed_abstract>We introduce the concept of metaconsensus and employ it to make high confidence predictions of early enzyme functions and the metabolic properties that they may have produced. Several independent studies have used comparative bioinformatics methods to identify taxonomically broad features of genomic sequence data, protein structure data, and metabolic pathway data in order to predict physiological features that were present in early, ancestral life forms. But all such methods carry with them some level of technical bias. Here, we cross-reference the results of these previous studies to determine enzyme functions predicted to be ancient by multiple methods. We survey modern metabolic pathways to identify those that maintain the highest frequency of metaconsensus enzymes. Using the full set </pubmed_abstract><journal>PloS one</journal><pubmed_title>The enzymatic and metabolic capabilities of early life.</pubmed_title><pmcid>PMC3438178</pmcid><funding_grant_id>DP1 LM011509</funding_grant_id><funding_grant_id>DP1 OD006779</funding_grant_id><pubmed_authors>Samudrala R</pubmed_authors><pubmed_authors>Goldman AD</pubmed_authors><pubmed_authors>Baross JA</pubmed_authors></additional><is_claimable>false</is_claimable><name>The enzymatic and metabolic capabilities of early life.</name><description>We introduce the concept of metaconsensus and employ it to make high confidence predictions of early enzyme functions and the metabolic properties that they may have produced. Several independent studies have used comparative bioinformatics methods to identify taxonomically broad features of genomic sequence data, protein structure data, and metabolic pathway data in order to predict physiological features that were present in early, ancestral life forms. But all such methods carry with them some level of technical bias. Here, we cross-reference the results of these previous studies to determine enzyme functions predicted to be ancient by multiple methods. We survey modern metabolic pathways to identify those that maintain the highest frequency of metaconsensus enzymes. Using the full set </description><dates><release>2012-01-01T00:00:00Z</release><publication>2012</publication><modification>2026-04-29T11:48:32.095Z</modification><creation>2026-04-07T15:10:34.112Z</creation></dates><accession>S-EPMC3438178</accession><cross_references><pubmed>22970111</pubmed><doi>10.1371/journal.pone.0039912</doi></cross_references></HashMap>