ECE Senior Capstone Project 2018 Tech NotesGraph Clustering Toolkit for Biological Data Analytics

Protein-Protein InteractionNetworks: Predicting ProteinFunctionalityBy Brian Rappaport, ECE ‘18

Introduction

Proteins are present in all forms of life, from bacteriaup on to humans, and a human cell can contain morethan a billion of them. Proteins are responsible for allwork within the cell, from breaking down glucose andwater to provide energy to managing DNA and RNAtranscription to create templates for new proteins. Theyare made up of chains of amino acids (basic biologicalmolecules); since there are 20 amino acids and proteinscan be hundreds of acids long, there are a huge numberof possible proteins, even though many combinationsare impossible.

Although individual proteins are important and wor-thy of study, it is in the sequence of proteins working to-gether that true complexity achieved, so protein-proteininteractions and their networks are studied in many dif-ferent areas of cell biology. In our work this semester,we used the interaction networks for a basic task: clus-tering different proteins by their function within the cell,from respiration to transportation.

Proteins

Structure

Protein structure is described at four distinct levels. Atthe first level, called the primary structure, this refersto the chain of amino acids itself. The next level, thesecondary structure, is made up of common structuresformed by the strand in the course of folding, such asα-helixes or β-strands. Tertiary structure describes the

arrangement of the protein itself as a whole, and quater-nary structure determines how several proteins interactwith each other. The fourth level is mainly what inter-ests us in this project, since it determines which proteinswill bind together and which will not, which should im-ply a similar function.

FunctionThe sheer number of ways proteins can be put togetherand interact with each other means the study of thestructure is a daunting task, so for practical results pro-teins are often studied by their functions instead.

Figure 1: Section of MIPS Functional Catalogue annotations.This example shows labels four levels deep, to the differenttypes of meiosis.

The large number of possible uses has been carefullystudied and many classifications are available in dif-ferent databases; for example, the MIPS (Munich In-formation center for Protein Sequencing) FunctionalCatalogue provides annotations at up to six levels ofspecificity, with more than 1300 different annotations.These databases are used to verify predicted results.

Department of Electrical and Computer EngineeringSenior Project Handbook: https://sites.tufts.edu/eeseniordesignhandbook/

1

InteractionsProteins can function paired in many different ways; forinstance, a protein can carry another protein througha membrane, or multiple enzymes (proteins which actas biological catalysts) may work together to producemacromolecules for use in cellular respiration or manyother domains. Quaternary protein structures often takethe form of protein complexes, which are groups of as-sociated chains of the polypeptides (amino acids) mak-ing up a larger structure with a particular utility. Thesecan be assembled by protein subunits, and these sub-units are commonly used to determine interactions: if aprotein subunit can assemble two proteins together intoa complex, there exists an interaction between them.Recent research has shown that essential genes (thoserequired for an organism to stay alive) are dispropor-tionally found in protein complexes; these are also oftenstrongly correlated in interaction mechanisms.

Figure 2: An example of a protein complex found in snakevenom. Note that 6 different peptide chains are involved in asingle complex.

Testing for Protein InteractionsSeveral tests are commonly employed to identifyprotein-protein iteractions. One of the most frequentlyused is a test known as two-hybrid screening. In thistest, two proteins that might interact are each boundtogether, which form a transcription factor (a protein

which will write a particular type of DNA) only if thetwo proteins interact. Then the resulting cells are testedfor the presence of that type of DNA. If the two proteinsinteracted, the transcription factor is created, and theDNA will be produced, but if it is not present, then thetwo proteins must not have interacted. Another test, tan-dem affinity purification, involves modifying the cellsto give a “tag” to a particular protein, letting the pro-tein bind to anything, then separating out everything notbound to the protein and seeing which proteins are leftin the system, all of which must have been bound to theoriginal protein.

Figure 3: Protein-protein interaction network, representingproteins found in patients with schizophrenia.

Both of these methods have obvious disadvantages:they require lab work and a lot of time to perform theexperiments correctly. Each of these tests also onlytest a single protein or a single interaction. Given theastronomical number of possible interactions, or eventhe much-less-but-still-large number of actual proteinsin a human cell (20,000 - 100,000), these tests are in-feasible on the scale required. Other work, includingour work, uses the (relatively) small number of knowninteractions to predict others, using various algebraicand machine learning methods. In this way, interactionscan be predicted much more fully and comprehensivelythan would be possible in labwork.

2

Challenges

Many problems in biology are hard because there is noknown ground truth to go off of, and clustering protein-protein interaction networks is such a problem. Severaldatabases exist containing labels, but all are incompleteand some contradict each other. Moreover, the majorityof testing has been done on simple organisms, primarilyyeast, but many connections that we hope to detect aremore complex than perhaps yeast would exemplify atall. In clustering, we did not take structure into account,but rather treated the interactions as black boxes: in re-ality, there is much more that we could get from know-ing the structure in addition, at the cost of increasedcomplexity. Finally, just because two proteins interactdoes not necessarily mean they are complementary infunction.

Conclusion

Protein-protein interaction networks are an importantfactor in cell biology. A full map of these interactionswould allow biologists to have a much better under-standing of many diseases and how to cure them, alongwith many other benefits. It is our hope that our workwill help to facilitate the greater understanding of theseinfluential networks.

References[1] M. Cao, “New Graph Metrics Improve Network-

based Protein Function Prediction.” pp. 1–137,2016.

[2] Ganapathiraju, Madhavi K. et al. “Schizophre-nia Interactome with 504 Novel Protein–protein In-teractions.” NPJ Schizophrenia 2 (2016): 16012.PMC. Web. 2017.

[3] Braun, P. and Gingras, A.-C. “History of protein–protein interactions: From egg-white to complexnetworks.” Proteomics, 12: 14781498. 2012.

[4] Ruepp, A. et al. “The FunCat, a functional annota-tion scheme for systematic classification of proteinsfrom whole genomes.” Nucleic Acids Res 32, 5539-5545. 2004.

[5] Mizuno H, Fujimoto Z, Koizumi M, Kano H, AtodaH, et al. “Structure of coagulation factors IX/X-binding protein, a heterodimer of C-type lectin do-mains.” Nat. Struct. Biol. pp. 438–41. 1997.

[6] Hart, G Traver, Insuk Lee, and Edward R Marcotte.“A High-Accuracy Consensus Map of Yeast ProteinComplexes Reveals Modular Nature of Gene Essen-tiality.” BMC Bioinformatics 8 (2007): 236. PMC.Web. 17 Apr. 2018.

3