Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Background

Protein-protein interactions (PPIs) are ubiquitous in biological systems and play a crucial role in various biological processes such as cell proliferation, growth, and differentiation, as well as triggering downstream responses through cellular signalling (Westermarck et al., 2013). They are necessary to form complexes, transfer electrons (McMillan et al., 2013), regulate processes and transport molecules between different compartments. Proteins bind to each other through multiple mechanisms including hydrophobic bonding, van der Waals forces, and salt bridges (Xie et al., 2019). The binding specificity and strength (transient – permanent) varies greatly as there are promiscuous proteins unspecifically interacting with many other proteins as well as proteins that have exactly one partner. Hence there is a need for quantitative interaction measurements over several orders of magnitudes when performing proteome-wide studies.

As aberrant PPIs have been associated with numerous human diseases, including inter alia cancer, infectious diseases, and neurodegenerative diseases such as Alzheimer’s disease and Creutzfeldt–Jakob disease (Lu et al., 2020), targeting PPIs has become an important focus for novel treatments (Corbi-Verge et al., 2016 & Modell et al., 2016). However, most existing approaches for analysing PPIs are limited by scalability, a lack of knowledge of interaction sites, or the need for tedious laboratory work. Therefore, many existing methods have been improved and many new methods have been developed to better identify what proteins interact and how they interact.

In this study, Baryshev, La Fleur et al. (BioRxiv, 2023) developed a new Yeast Two-Hybrid approach with direct measurement of interaction strength by DNA sequencing (MP3-seq) for high-throughput study of protein-protein interactions.

Current Approaches to Study Protein-Protein Interactions

There are multiple high-throughput experimental and computational methods to characterise and quantity PPIs with yeast two-hybrid (Y2H) and tandem affinity purification-mass spectrometry (MS) being the most commonly used methods. Depending on the aim of a study, different factors (e.g. in vivo or in vitro, qualitative or quantitative approach, transient and/or stable PPIs, indirect or direct identification) should be considered when deciding on the method to address the research question. A more detailed overview of genetic, biochemical, biophysical and computational methods to study protein-protein interactions can be found below as supplementary comment. Additionally, I found the review from Stynen et al. (2012) and Rao et al. (2014) quite helpful even though they are not covering recent advances in the field.

Key Findings

New scalable Yeast Two-Hybrid approach (MP3-seq)

Baryshev, La Fleur et al. (BioRxiv, 2023) introduced a new scalable Yeast Two-Hybrid approach that can rapidly identify more than 100,000 pairwise protein-protein heterodimer interactions and quantify interaction strengths over several orders of magnitude. The workflow involves transforming all necessary DNA fragments into yeast and assembly in histidine auxotroph yeast. This is followed by positive plasmid selection after which the cells are split. While some cells are taken aside, the rest of the cells subjected to a His selection where cells can only grow when the proteins of interest interact, triggering the gene expression of an essential enzyme in the histidine biosynthesis pathway.

In more detail, yeast cells (histidine and tryptophan auxotroph) are transformed with all necessary components via electroporation. To reduce unwanted variability which could distort subsequent quantification, a centromere sequence is added as it ensures the expression of one pair of hybrid proteins per cell on average. As all elements can assemble to a plasmid in yeast through homologous recombination, no additional plasmid cloning (E. coli) or mating (yeast) step is required. Subsequently, the cells are grown in a selective media lacking Tryptophan (Trp), only allowing growth of yeast that have successfully transformed and assembled the plasmid (positive selection).

As the quantification method relies on comparing pre- and post-His-comparison, some cells were frozen, before the remaining cells were transferred to a selective media without histidine (His). If the two proteins to be screened interact with each other, the growth essential enzyme his3 is expressed allowing cells to grow in absence of  histidine (His). his3 encodes the IGP dehydratase which catalyses an essential step in the histidine biosynthesis pathway. This means that cells that carry two strongly/permanently interacting proteins on their plasmid are expressing more IGP dehydratase allowing more histidine to be produced and thereby maximal growth. On the other hand, cells with transiently interacting proteins grow slower given the lower amount of available histidine.

Finally, cells from both pre- and post-His selection were lysed and the plasmid DNA extracted for subsequent amplification and sequencing. Based on the barcode counts before and after his-selection, one can compute the relative enrichment which serves as a proxy for the interaction strength.

Development of a Computational Data Analysis Workflow

The authors have developed a computational data analysis workflow to analyse Mp3-seq data and quantify interaction strength (https://github.com/Seeliglab/MP3-DUET-AUTOTUNE). Following calculation of the enrichment, multiple corrections are performed including autoactivator removal, undetected PPI filtering, and accounting for experimental replicates.

In detail, the authors first computed the enrichment between the pre- and the post-His selection normalised by library size. Next, they removed proteins, which act as autoactivators meaning that they can non-specifically activate the expression of the reporter gene (here his3) in absence of a protein-protein interaction. They were identified by screening for proteins that displayed a high enrichment value for all interaction partners, which means that the his3 expression was unspecific. Subsequently, the authors screened for protein-protein combinations for which no barcode could be detected in the pre- and the post-His selection sequencing results and replaced them by the minimum value of detected pre-His reads. The corrected reads from both fusion orders (P1-DBD + P2-AD and P2-DBD + P1-AD, taken as pseudoreplicates) were then analysed by DESeq2 to determine the log2 fold changes which serves as proxy for the interaction strength.

Benchmark Results

The authors performed multiple benchmarking routines to make sure that their method is feasible for large-scale screening and that their results are in agreement with other state-of-the-art methods. These benchmarks included a large-scale assay of designed heterodimers, an approach with orthogonal coiled-coil dimers and an experiment with BCL family binders (control cell death primarily by direct binding interactions). Overall, their results demonstrated a good quantitative agreement with previously published results.

Perspective

Obtaining unbiased high-quality protein-protein interaction data is crucial for multiple fields including synthetic biology, drug development and molecular biology. Recent advances have paved the way for the design of novel drugs targeting PPIs resulting in multiple small molecules, peptides and antibodies currently being in clinical trials as PPI modulators. MP3-seq might be used as a complementary analysis to fragment-based drug discovery (FBDD) by building large-scale libraries of modular interaction domains.

Moreover, having more reliable experimental quantitative high-throughput protein-protein interaction data through MP3-seq as prior knowledge may allow for more sophisticated computational predictions of protein-protein interactions. I would assume that by combining it with AlphaFold-based docking methodologies like the protocol from Bryant et al. (2022) or AlphaFold-multimer which use optimised multiple sequence alignments, one could improve prediction results and filter out more false-positives further improving PPI predictions.

Finally, it would be interesting to see whether it is possible to tune the approach to specific cellular environments and other two-hybrid screening techniques, which would allow for context-specific and tailored analysis of protein-protein interactions.

What I liked about this preprint

What I really liked about this preprint is that the authors not only developed a very promising experimental method, but also a custom workflow to analyse their high-throughput Y2H data.

Collaborations between experimental and computational researchers are crucial in ensuring that the statistical and filtering analysis tools used downstream are specifically tailored to meet the demands of the experimental method. This leads to a more efficient, robust, and accurate interpretation of the results. Additionally, such collaborations foster innovation and interdisciplinary thinking, as both sides learn from and influence each other.

References

Bryant, P., Pozzati, G., & Elofsson, A. (2022). Improved prediction of protein-protein interactions using AlphaFold2. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28865-w

Corbi-Verge, C., Kim, P.M. Motif mediated protein-protein interactions as drug targets. Cell Commun Signal 14, 8 (2016). https://doi.org/10.1186/s12964-016-0131-4

Lu, H., Zhou, Q., He, J. et al. (2020) Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials. Sig Transduct Target Ther 5, 213 . https://doi.org/10.1038/s41392-020-00315-3

McMillan DG, Marritt SJ, Firer-Sherwood MA, et al. (2013) Protein-protein interaction regulates the direction of catalysis and electron transfer in a redox enzyme complex. J Am Chem Soc., 135(28), 10550-10556. https://doi.org/10.1021/ja405072z

Modell, A. E., Blosser, S. L., & Arora, P. S. (2016). Systematic Targeting of Protein-Protein Interactions. Trends in pharmacological sciences, 37(8), 702–713. https://doi.org10.1016/j.tips.2016.05.008

Rao, V. S., Srinivas, K., Sujini, G. N., & Kumar, G. N. S. (2014). Protein-Protein Interaction Detection: Methods and Analysis. International Journal of Proteomics, 2014, 147648. https://doi.org/10.1155/2014/147648

Stynen Bram, Tournu Hélène, Tavernier Jan, & Van Dijck Patrick. (2012). Diversity in Genetic In Vivo Methods for Protein-Protein Interaction Studies: from the Yeast Two-Hybrid System to the Mammalian Split-Luciferase System. Microbiology and Molecular Biology Reviews, 76(2), 331–382. https://doi.org/10.1128/MMBR.05021-11

Westermarck, J., Ivaska, J., & Corthals, G. L. (2013). Identification of Protein Interactions Involved in Cellular Signaling. Molecular & Cellular Proteomics, 12(7), 1752–1763. https://doi.org/10.1074/mcp.R113.027771

Xie, N. Z., Du, Q. S., Li, J. X., & Huang, R. B. (2015). Exploring Strong Interactions in Proteins with Quantum Chemistry and Examples of Their Applications in Drug Design. PloS one, 10(9), e0137113. https://doi.org/10.1371/journal.pone.0137113

Tags: protein-protein interactions, sequencing, yeast-two-hybrid

doi: https://doi.org/10.1242/prelights.34245

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