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Real or fake? Measuring the impact of protein annotation errors on estimates of domain gain and loss events

Kress, Arnaud, Poch, Olivier, Lecompte, Odile, Thompson, Julie D.·2023·Frontiers in Bioinformatics
DOI 10.3389/fbinf.2023.1178926PMC PMC10158824
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Abstract

Protein annotation errors can have significant consequences in a wide range of fields, ranging from protein structure and function prediction to biomedical research, drug discovery, and biotechnology. By comparing the domains of different proteins, scientists can identify common domains, classify proteins based on their domain architecture, and highlight proteins that have evolved differently in one or more species or clades. However, genome-wide identification of different protein domain architectures involves a complex error-prone pipeline that includes genome sequencing, prediction of gene exon/intron structures, and inference of protein sequences and domain annotations. Here we developed an automated fact-checking approach to distinguish true domain loss/gain events from false events caused by errors that occur during the annotation process. Using genome-wide ortholog sets and taking advantage of the high-quality human and Saccharomyces cerevisiae genome annotations, we analyzed the domain gain and loss events in the predicted proteomes of 9 non-human primates (NHP) and 20 non-S. cerevisiae fungi (NSF) as annotated in the Uniprot and Interpro databases. Our approach allowed us to quantify the impact of errors on estimates of protein domain gains and losses, and we show that domain losses are over-estimated ten-fold and three-fold in the NHP and NSF proteins respectively. This is in line with previous studies of gene-level losses, where issues with genome sequencing or gene annotation led to genes being falsely inferred as absent. In addition, we show that insistent protein domain annotations are a major factor contributing to the false events. For the first time, to our knowledge, we show that domain gains are also over-estimated by three-fold and two-fold respectively in NHP and NSF proteins. Based on our more accurate estimates, we infer that true domain losses and gains in NHP with respect to humans are observed at similar rates, while domain gains in the more divergent NSF are observed twice as frequently as domain losses with respect to S. cerevisiae. This study highlights the need to critically examine the scientific validity of protein annotations, and represents a significant step toward scalable computational fact-checking methods that may 1 day mitigate the propagation of wrong information in protein databases.

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Figures

FIGURE 1 Definition of different domain content used in this analysis. As an example, a reference protein is shown with a single copy of domain D1 and two copies of domain D2. Different domain content is defined as a change in the list of distinct domains, corresponding to either a missing domain (MD) or an additional domain (AD) in a given ortholog from a non-reference species with respect to the reference protein.
FIGURE 1 Definition of different domain content used in this analysis. As an example, a reference protein is shown with a single copy of domain D1 and two copies of domain D2. Different domain content is defined as a change in the list of distinct domains, corresponding to either a missing domain (MD) or an additional domain (AD) in a given ortholog from a non-reference species with respect to the reference protein.CC BY · PubMed Central
FIGURE 2 Fact checking protocol for potential missing domain (MD) and additional domain (AD) events in a given ortholog from a non-reference species with respect to the protein from the reference species. True domain events are classified as either domain switching (the ortholog sequence does not contain domain D2, but contains a domain D2′ belonging to the same Pfam clan as D2), or a MD/AD event.
FIGURE 2 Fact checking protocol for potential missing domain (MD) and additional domain (AD) events in a given ortholog from a non-reference species with respect to the protein from the reference species. True domain events are classified as either domain switching (the ortholog sequence does not contain domain D2, but contains a domain D2′ belonging to the same Pfam clan as D2), or a MD/AD event.CC BY · PubMed Central
FIGURE 3 Example of a MD event due to a gene prediction error. (A). Domain annotations extracted from the Interpro database, with a potential MD in the bushbaby sequence (B5FW94). (B). Part of the Tblastn alignment identifying an additional exon at the 3′ end of the corresponding genome region (Ensembl: ENSOGAG00000008310). Exons are shown in uppercase and introns in lowercase. The number in brackets indicates the length of the potential intron. (C). Part of the protein sequence alignment of the human sequence (Q15904), the B5FW94 bushbaby sequence, and the corrected bushbaby sequence with the C-terminal extension coded by the additional exon.
FIGURE 3 Example of a MD event due to a gene prediction error. (A). Domain annotations extracted from the Interpro database, with a potential MD in the bushbaby sequence (B5FW94). (B). Part of the Tblastn alignment identifying an additional exon at the 3′ end of the corresponding genome region (Ensembl: ENSOGAG00000008310). Exons are shown in uppercase and introns in lowercase. The number in brackets indicates the length of the potential intron. (C). Part of the protein sequence alignment of the human sequence (Q15904), the B5FW94 bushbaby sequence, and the corrected bushbaby sequence with the C-terminal extension coded by the additional exon.CC BY · PubMed Central
FIGURE 4 Results of error detection protocol for (A) NHP proteins and (B) NSF proteins with potential MD.
FIGURE 4 Results of error detection protocol for (A) NHP proteins and (B) NSF proteins with potential MD.CC BY · PubMed Central
FIGURE 5 Results of error detection protocol for (A) NHP proteins and (B) NSF proteins with potential AD events.
FIGURE 5 Results of error detection protocol for (A) NHP proteins and (B) NSF proteins with potential AD events.CC BY · PubMed Central
FIGURE 6 Rates of MD and AD events before and after filtering of false positives for (A) NHP and (B) NSF proteins.
FIGURE 6 Rates of MD and AD events before and after filtering of false positives for (A) NHP and (B) NSF proteins.CC BY · PubMed Central
FIGURE 7 Location of MD and AD events at the N/C-termini or in the middle of the protein for (A) NHP and (B) NSF proteins.
FIGURE 7 Location of MD and AD events at the N/C-termini or in the middle of the protein for (A) NHP and (B) NSF proteins.CC BY · PubMed Central
FIGURE 8 Schematic views of the example domain events observed in NHP proteomes. (A). Domain organizations for human protein NRP1_HUMAN and baboon protein A0A096P459, showing the shared domains CUB (PF00431) and FA58C (PF00754) and the missing MAM (PF00629) domain in baboon. (B). Domain organizations for human protein SP100_HUMAN and marmoset protein A0A2R8MEX0, showing the shared domains HSR (PF03172) and SAND (PF01342) and the missing HMG (PF00505) domain in marmoset. The marmoset protein contains 2 additional domains: PHD (PF00628) and bromodomain (PF00439). (C). Domain organizations for human protein CMAH_HUMAN and all NHP orthologs, showing the additional Rieske-type domain (PF00355) in NHP orthologs.
FIGURE 8 Schematic views of the example domain events observed in NHP proteomes. (A). Domain organizations for human protein NRP1_HUMAN and baboon protein A0A096P459, showing the shared domains CUB (PF00431) and FA58C (PF00754) and the missing MAM (PF00629) domain in baboon. (B). Domain organizations for human protein SP100_HUMAN and marmoset protein A0A2R8MEX0, showing the shared domains HSR (PF03172) and SAND (PF01342) and the missing HMG (PF00505) domain in marmoset. The marmoset protein contains 2 additional domains: PHD (PF00628) and bromodomain (PF00439). (C). Domain organizations for human protein CMAH_HUMAN and all NHP orthologs, showing the additional Rieske-type domain (PF00355) in NHP orthologs.CC BY · PubMed Central
FIGURE 9 Schematic views of the example domain events observed in NSF proteomes. (A). Domain organizations for Saccharomyces cerevisiae protein GAL10_YEAST, Schizosaccharomyces pombe protein GAL10_SCHPO and other NSF orthologs, showing the shared domain GDP-mannose 4,6 dehydratase (PF16363) and the missing Aldose 1-epimerase (PF01263) domain in NSF orthologs. (B). Domain organizations for Saccharomyces cerevisiae protein GAL4_YEAST, A. fumigatus protein Q4WWQ9 and Neurospora crassa protein Q7SAJ9, showing the shared Zn2C6 (PF00172) and Fungal_trans domains (PF04082) and the missing dimerization domain (Dimer_dom) (PF03902) in the Neurospora crassa protein. (C). (B). Domain organizations for Saccharomyces cerevisiae protein LDH1_YEAST, Y. lipolytica protein Q6CEG0 and A. oligospora protein G1XEC8, showing the shared AB hydrolase (PF12697) domain and the additional DSPc (PF00782) domain in Y. lipolytica and A. oligospora proteins.
FIGURE 9 Schematic views of the example domain events observed in NSF proteomes. (A). Domain organizations for Saccharomyces cerevisiae protein GAL10_YEAST, Schizosaccharomyces pombe protein GAL10_SCHPO and other NSF orthologs, showing the shared domain GDP-mannose 4,6 dehydratase (PF16363) and the missing Aldose 1-epimerase (PF01263) domain in NSF orthologs. (B). Domain organizations for Saccharomyces cerevisiae protein GAL4_YEAST, A. fumigatus protein Q4WWQ9 and Neurospora crassa protein Q7SAJ9, showing the shared Zn2C6 (PF00172) and Fungal_trans domains (PF04082) and the missing dimerization domain (Dimer_dom) (PF03902) in the Neurospora crassa protein. (C). (B). Domain organizations for Saccharomyces cerevisiae protein LDH1_YEAST, Y. lipolytica protein Q6CEG0 and A. oligospora protein G1XEC8, showing the shared AB hydrolase (PF12697) domain and the additional DSPc (PF00782) domain in Y. lipolytica and A. oligospora proteins.CC BY · PubMed Central

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Metadata

Authors
Kress, A., Poch, O., Lecompte, O., & Thompson, J. D.
Venue
Frontiers in Bioinformatics
Publisher
Frontiers Media SA
Published
2023-04-20
Volume
3
ISSN
2673-7647
DOI
10.3389/fbinf.2023.1178926
PMC
PMC10158824
Cited by
21
References
6
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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Kress, A., Poch, O., Lecompte, O., & Thompson, J. D. (2023). Real or fake? Measuring the impact of protein annotation errors on estimates of domain gain and loss events. Frontiers in Bioinformatics. https://doi.org/10.3389/fbinf.2023.1178926