Expected Preparations:

  Biomolecules:
The molecules of life; The genetic code; Nucleic acids; Amino acids; Protein folding; Post-translational modifications and protein biochemistry; Membrane proteins; Biological function.
   [BIN-PPI]
Concepts 		 
  If you are not already familiar with the prior knowledge listed above, you need to prepare yourself from other information sources.   The units listed above are part of this course and contain important preparatory material.  
Keywords: IntAct; iRef
Objectives:

This unit will …

  • … introduce issues surrounding the collection and curation of protein-protein interactions in databases;

  • … explore the Web interfaces to IntAct and BioGRID;

  • … discuss the limitations of interaction predictions based on homology ;

 Outcomes:

After working through this unit you …

  • … can access IntAct and BioGRID and discover interactions with a protein of interest.

Deliverables:

Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.

Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don’t overlook these.

Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

Evaluation:

NA: This unit is not evaluated for course marks.

Contents

Exploring IntAct and BioGRID PPI databases. NA

 

In high-throughput biology, the genome was the beginning. As Sydney Brenner has phrased it: we have now written the “white-pages” of the cell, fulfilling the “CAP-criterion” (Comprehensive, Accurate and Permanent). The next level is figuring out the way the parts work - if you will, the “Yellow Pages” - and many of us expect that substantial progress can be made by mapping their interactions. After all, physiological function can be described to a large part as the result of physical interaction.

Please note that there are different types of physical interactions. We most often think of complexes, either stable or transient homo- or heterooligomers when we speak of physical interactions. But there are also interactions between substrates and products and not all of them correspond to classical enzymatic pathways. Phosphorylation and dephosphorylation are processes of key importance in signal transduction and acetylation/deacetylation plays a critical role in regulatory pathways. Here, the substrates are proteins and the interaction with the modifying enzyme is of course a physical interaction.

Genetic interactions on the other hand are another story. Here the word interaction is used in an entirely different sense: it is not synonymous with contact it is synonymous with influence. In fact, most proteins that display genetic interactions would not be expected to interact physically as well. See the FND-PPI-Physical_vs_genetic unit for details.

 

Task…

  • Read the introductory notes on protein-protein interaction databasesPDF.

  • Read

    Licata, Luana and Sandra Orchard. (2016). “The MIntAct Project and Molecular Interaction Databases”. Methods in Molecular Biology (Clifton, N.j.) 1415:55–69 .
    [PMID: 27115627] [DOI: 10.1007/978-1-4939-3572-7_3]

    Molecular interaction databases collect, organize, and enable the analysis of the increasing amounts of molecular interaction data being produced and published as we move towards a more complete understanding of the interactomes of key model organisms. The organization of these data in a structured format supports analyses such as the modeling of pairwise relationships between interactors into interaction networks and is a powerful tool for understanding the complex molecular machinery of the cell. This chapter gives an overview of the principal molecular interaction databases, in particular the IMEx databases, and their curation policies, use of standardized data formats and quality control rules. Special attention is given to the MIntAct project, in which IntAct and MINT joined forces to create a single resource to improve curation and software development efforts. This is exemplified as a model for the future of molecular interaction data collation and dissemination.

Oughtred, Rose et al.. (2019). “The BioGRID interaction database: 2019 update”. Nucleic Acids Research 47(D1):D529–D541 .
[PMID: 30476227] [DOI: 10.1093/nar/gky1079]

The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical-protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene-phenotype and gene-gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

 

Data Sources

Interaction databases have similar problems as sequence databases: the need for standards for abstracting biological concepts into computable objects, data integrity, search and retrieval, and the metrics of comparison. There is however an added complication: interactions are rarely all-or-none, and the high-throughput experimental methods have large false-positive and false-negative rates. This makes it necessary to define confidence scores for interactions. On top of experimental methods, there are also a variety of methods for computational interaction prediction(W). However, even though the “gold standard” are careful, small-scale laboratory experiments, different curated efforts on the same experimental publication usually lead to different results - with as little as 42% overlap between databases being reported.

Currently, likely the best integrated protein-protein interaction database is IntAct, at the EBI, which, besides curating interactions from the literature, hosts interactions from the IMEx consortium = an extensive data-sharing agreement between a number of general and specialized source databases.

 

Task…

But now what?

If you are like me, you would like to be able to link expression profiles, information about known complexes, GO annotations, knock-out phenotypes etc. etc. Not on the Web.

 

Next, we explore the BioGRID interaction database. BioGrid stores physical and genetic interactions.

 

Task…

You will note that some, but not all physical interactions listed by BioGRID and IntAct are the same according to a restrictive interpretation: same organism, same proteins, same experiment, same publication.

 

Now, what about MYSPE? Could you infer interactions between proteins whose orthologs interact in another species? Such predictions are called interologs (inter_acting homo_logs). Unfortunately, that does not appear to be the case. Confident prediction of interologs can only be achieved in cases of >80% joint sequence identity of both pairs1, a level of similarity that (I believe) none of our Mbp1 proteins achieves. Does this mean the pathways and interactions are not conserved? Certainly not. We expect a very high degree of conservation of the system’s function, but we can’t say for sure whether any two specific proteins interact in a different species the same way they interact in yeast. All we can do is to use annotation transfer for hypothesis generation. But that is a useful starting point.

 

Further Reading

Lewis, Anna C F et al.. (2012). “What evidence is there for the homology of protein-protein interactions?”. Plos Computational Biology 8(9):e1002645 .
[PMID: 23028270] [DOI: 10.1371/journal.pcbi.1002645]

The notion that sequence homology implies functional similarity underlies much of computational biology. In the case of protein-protein interactions, an interaction can be inferred between two proteins on the basis that sequence-similar proteins have been observed to interact. The use of transferred interactions is common, but the legitimacy of such inferred interactions is not clear. Here we investigate transferred interactions and whether data incompleteness explains the lack of evidence found for them. Using definitions of homology associated with functional annotation transfer, we estimate that conservation rates of interactions are low even after taking interactome incompleteness into account. For example, at a blastp E-value threshold of 10(-70), we estimate the conservation rate to be about 11 % between S. cerevisiae and H. sapiens. Our method also produces estimates of interactome sizes (which are similar to those previously proposed). Using our estimates of interaction conservation we estimate the rate at which protein-protein interactions are lost across species. To our knowledge, this is the first such study based on large-scale data. Previous work has suggested that interactions transferred within species are more reliable than interactions transferred across species. By controlling for factors that are specific to within-species interaction prediction, we propose that the transfer of interactions within species might be less reliable than transfers between species. Protein-protein interactions appear to be very rarely conserved unless very high sequence similarity is observed. Consequently, inferred interactions should be used with care.

Garcia-Garcia, Javier et al.. (2012). “BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference”. Nucleic Acids Research 40(Web Server issue):W147–51 .
[PMID: 22689642] [DOI: 10.1093/nar/gks553]

Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

Keskin, Ozlem, Nurcan Tuncbag, and Attila Gursoy. (2016). “Predicting Protein-Protein Interactions from the Molecular to the Proteome Level”. Chemical Reviews 116(8):4884–909 .
[PMID: 27074302] [DOI: 10.1021/acs.chemrev.5b00683]

Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction.

Questions, comments

If in doubt, ask! If anything about this contents is not clear to you, do not proceed but ask for clarification. If you have ideas about how to make this material better, let’s hear them. We are aiming to compile a list of FAQs for all learning units, and your contributions will count towards your participation marks.

Improve this page! If you have questions or comments, please post them on the Quercus Discussion board with a subject line that includes the name of the unit.

References

Page ID: BIN-PPI-Databases

Author:
Boris Steipe ( <boris.steipe@utoronto.ca> )
Created:
2017-08-05
Last modified:
2022-09-14
Version:
1.1
Version History:
–  1.1 2020 Update
–  1.0 First live
–  0.1 First stub
Tagged with:
–  Unit
–  Live
–  Has lecture slides
–  Has further reading

 

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