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Small protein folds at the root of an ancient metabolic network.

ABSTRACT: Life on Earth is driven by electron transfer reactions catalyzed by a suite of enzymes that comprise the superfamily of oxidoreductases (Enzyme Classification EC1). Most modern oxidoreductases are complex in their structure and chemistry and must have evolved from a small set of ancient folds. Ancient oxidoreductases from the Archean Eon between ca. 3.5 and 2.5 billion years ago have been long extinct, making it challenging to retrace evolution by sequence-based phylogeny or ancestral sequence reconstruction. However, three-dimensional topologies of proteins change more slowly than sequences. Using comparative structure and sequence profile-profile alignments, we quantify the similarity between proximal cofactor-binding folds and show that they are derived from a common ancestor. We discovered that two recurring folds were central to the origin of metabolism: ferredoxin and Rossmann-like folds. In turn, these two folds likely shared a common ancestor that, through duplication, recruitment, and diversification, evolved to facilitate electron transfer and catalysis at a very early stage in the origin of metabolism.

SUBMITTER: Raanan H

PROVIDER: S-EPMC7132300 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Small protein folds at the root of an ancient metabolic network.

Raanan Hagai H Poudel Saroj S Pike Douglas H DH Nanda Vikas V Falkowski Paul G PG

Proceedings of the National Academy of Sciences of the United States of America 20200318 13

Life on Earth is driven by electron transfer reactions catalyzed by a suite of enzymes that comprise the superfamily of oxidoreductases (Enzyme Classification EC1). Most modern oxidoreductases are complex in their structure and chemistry and must have evolved from a small set of ancient folds. Ancient oxidoreductases from the Archean Eon between ca. 3.5 and 2.5 billion years ago have been long extinct, making it challenging to retrace evolution by sequence-based phylogeny or ancestral sequence r ...[more]

PMID: 32188785

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Project description:Motivation:Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results:We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. We compare our method with a top profile-profile alignment method-HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 12.63-26.32% higher than HHSearch on template-free modeling targets and 3.39-17.09% higher on hard template-based modeling targets for top 1, 5 and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking. Availability and implementation:The DeepSF server is publicly available at: http://iris.rnet.missouri.edu/DeepSF/. Contact:chengji@missouri.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Small protein folds at the root of an ancient metabolic network.

Publications

Small protein folds at the root of an ancient metabolic network.

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