IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000867.html
   My bibliography  Save this article

More Than 1,001 Problems with Protein Domain Databases: Transmembrane Regions, Signal Peptides and the Issue of Sequence Homology

Author

Listed:
  • Wing-Cheong Wong
  • Sebastian Maurer-Stroh
  • Frank Eisenhaber

Abstract

Large-scale genome sequencing gained general importance for life science because functional annotation of otherwise experimentally uncharacterized sequences is made possible by the theory of biomolecular sequence homology. Historically, the paradigm of similarity of protein sequences implying common structure, function and ancestry was generalized based on studies of globular domains. Having the same fold imposes strict conditions over the packing in the hydrophobic core requiring similarity of hydrophobic patterns. The implications of sequence similarity among non-globular protein segments have not been studied to the same extent; nevertheless, homology considerations are silently extended for them. This appears especially detrimental in the case of transmembrane helices (TMs) and signal peptides (SPs) where sequence similarity is necessarily a consequence of physical requirements rather than common ancestry. Thus, matching of SPs/TMs creates the illusion of matching hydrophobic cores. Therefore, inclusion of SPs/TMs into domain models can give rise to wrong annotations. More than 1001 domains among the 10,340 models of Pfam release 23 and 18 domains of SMART version 6 (out of 809) contain SP/TM regions. As expected, fragment-mode HMM searches generate promiscuous hits limited to solely the SP/TM part among clearly unrelated proteins. More worryingly, we show explicit examples that the scores of clearly false-positive hits, even in global-mode searches, can be elevated into the significance range just by matching the hydrophobic runs. In the PIR iProClass database v3.74 using conservative criteria, we find that at least between 2.1% and 13.6% of its annotated Pfam hits appear unjustified for a set of validated domain models. Thus, false-positive domain hits enforced by SP/TM regions can lead to dramatic annotation errors where the hit has nothing in common with the problematic domain model except the SP/TM region itself. We suggest a workflow of flagging problematic hits arising from SP/TM-containing models for critical reconsideration by annotation users.Author Summary: Sequence homology is a fundamental principle of biology. It implies common phylogenetic ancestry of genes and, subsequently, similarity of their protein products with regard to amino acid sequence, three-dimensional structure and molecular and cellular function. Originally an esoteric concept, homology with the proxy of sequence similarity is used to justify the transfer of functional annotation from well-studied protein examples to new sequences. Yet, functional annotation via sequence similarity seems to have hit a plateau in recent years since relentless annotation transfer led to error propagation across sequence databases; thus, leading experimental follow-up work astray. It must be emphasized that the trinity of sequence, 3D structural and functional similarity has only been proven for globular segments of proteins. For non-globular regions, similarity of sequence is not necessarily a result of divergent evolution from a common ancestor but the consequence of amino acid sequence bias. In our investigation, we found that protein domain databases contain many domain models with transmembrane regions and signal peptides, non-globular segments of proteins having hydrophobic bias. Many proteins have inherited completely wrong function assignments from these domain models. We fear that future function predictions will turn out futile if this issue is not immediately addressed.

Suggested Citation

  • Wing-Cheong Wong & Sebastian Maurer-Stroh & Frank Eisenhaber, 2010. "More Than 1,001 Problems with Protein Domain Databases: Transmembrane Regions, Signal Peptides and the Issue of Sequence Homology," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-19, July.
  • Handle: RePEc:plo:pcbi00:1000867
    DOI: 10.1371/journal.pcbi.1000867
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000867
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000867&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1000867?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alexandra M Schnoes & Shoshana D Brown & Igor Dodevski & Patricia C Babbitt, 2009. "Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-13, December.
    2. Sean R Eddy, 2008. "A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation," PLOS Computational Biology, Public Library of Science, vol. 4(5), pages 1-14, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alejandro Ochoa & John D Storey & Manuel Llinás & Mona Singh, 2015. "Beyond the E-Value: Stratified Statistics for Protein Domain Prediction," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-21, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rui Fa & Domenico Cozzetto & Cen Wan & David T Jones, 2018. "Predicting human protein function with multi-task deep neural networks," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    2. Alejandro Ochoa & John D Storey & Manuel Llinás & Mona Singh, 2015. "Beyond the E-Value: Stratified Statistics for Protein Domain Prediction," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-21, November.
    3. Michal Brylinski & Daswanth Lingam, 2012. "eThread: A Highly Optimized Machine Learning-Based Approach to Meta-Threading and the Modeling of Protein Tertiary Structures," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-12, November.
    4. Thomas J Sharpton & Samantha J Riesenfeld & Steven W Kembel & Joshua Ladau & James P O'Dwyer & Jessica L Green & Jonathan A Eisen & Katherine S Pollard, 2011. "PhylOTU: A High-Throughput Procedure Quantifies Microbial Community Diversity and Resolves Novel Taxa from Metagenomic Data," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-13, January.
    5. Sean R Eddy, 2011. "Accelerated Profile HMM Searches," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-16, October.
    6. Friedrich Torben & Koetschan Christian & Müller Tobias, 2010. "Optimisation of HMM Topologies Enhances DNA and Protein Sequence Modelling," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-27, January.
    7. Akira R Kinjo & Haruki Nakamura, 2012. "Composite Structural Motifs of Binding Sites for Delineating Biological Functions of Proteins," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    8. Angelina Beavogui & Auriane Lacroix & Nicolas Wiart & Julie Poulain & Tom O. Delmont & Lucas Paoli & Patrick Wincker & Pedro H. Oliveira, 2024. "The defensome of complex bacterial communities," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Elisa Boari de Lima & Wagner Meira Júnior & Raquel Cardoso de Melo-Minardi, 2016. "Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-32, June.
    10. Matthew N Benedict & Michael B Mundy & Christopher S Henry & Nicholas Chia & Nathan D Price, 2014. "Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-14, October.
    11. Yuval Bussi & Ruti Kapon & Ziv Reich, 2021. "Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-27, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1000867. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.