I am a computational biologist who enjoys exploring cell signalling through data-driven mathematical modelling. I am especially interested in understanding the dynamic interplay of systems components and how information is specifically processed and linked to cell-fate decisions despite cross-talk and noise. Recently graduated from the joint Master’s programme in Molecular Techniques in Life Science in Stockholm, Sweden, I am a PhD candidate in Evangelia Petsalaki’s research group (Whole Cell Signalling) at the European Bioinformatics Institute and the University of Cambridge as part of the EMBL International PhD Programme.
My PhD project involves the development of context-specific executable models of signalling processes. For this the current plan is to optimise an existing method in my group that derives phenotype/context-specific networks from omics data and develop a method that can translate these into executable models. Besides science, I am addicted to all sorts of ballsports (tennis, badminton, table tennis, foosball) and enjoy multi-day hiking and kayaking tours .
Biologically informed NeuralODEs for genome-wide regulatory dynamics
Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells
A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion
Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers
Supplement: Current Approaches to Study Protein-Protein Interactions
– Yeast Two-Hybrid (Y2H) System involves fusing one protein of interest to a DNA-binding domain (BDB) and another protein to an activation domain (AD) (Fields and Song, 1989). If the two proteins interact, the DNA-binding and activation domains come into close proximity, leading to the expression of a reporter gene. Newer versions of the assay (Weile et al., 2017; Luck et al. 2020) allow for screening of entire proteomes or random peptide libraries, which can enable unbiased identification of novel interactions.
– Protein fragment complementation assay (PCA) involves splitting a protein of interest into two fragments and fusing each fragment with a complementary protein fragment (Michnick et al., 2007). The resulting fusion proteins can only reconstitute the original protein activity if the two proteins interact and bring the two fragments together. This approach can be used to study protein-protein interactions in vivo and has been used for high-throughput screening to identify potential drug targets.
– Protein-array based methods involve immobilising large numbers of purified proteins on a solid surface (e.g. glass slide). The arrays can then be probed with (fluorescently) labelled proteins of interest to identify potential interaction partners. This approach enables the simultaneous screening of thousands of protein pairs, but it does necessitate laborious protein purification steps.
– Fluorescence/Bioluminescence Resonance Energy Transfer (FRET/BRET) involves labelling one protein with a donor fluorophore/luminescence and the other protein with an acceptor fluorophore (Sun et al., 2016). When the two proteins interact, the donor fluorophore transfers energy to the acceptor fluorophore, resulting in fluorescence, which can be measured by a detector.
– Mass spectrometry-based methods involve the fractionation of cell lysates by size or other physicochemical properties, followed by mass spectrometry analysis to identify co-eluting proteins (Richards et al., 2021). Although this approach can identify numerous potential interaction partners in an unbiased manner, it can be quite laborious since it requires protein purification steps.
– Immunoprecipitation-based methods such as Co-Immunoprecipitation (Co-IP) or Tandem Affinity Purification (TAP) involve the isolation of protein-protein interactions by adsorbing the protein complex onto beads using a combination of protein tags and specific antibodies (Lin & Lai, 2017), and subsequently identifying them by mass spectrometry or Western blotting.
– Surface Plasmon Resonance (SPR) involves immobilising one protein on a chip and flowing the other protein over it. The binding between the two proteins is measured in real-time based on the changes in the refractive index of the chip.
– Proximity-dependent labelling methods such as BioID and APEX enable identification of interacting proteins within a certain distance of the protein of interest and can be used to identify both stable and transient interactions in an unbiased way (Chen & Perrimon, 2017).
– Co-evolution Analysis makes inferences about protein-protein interactions using alignments (both sequence and structure) and phylogenetic distances. The approach can be used to predict functional residues and domains involved in the PPI.
– Molecular Docking Analysis uses structural templates of individual proteins to predict the structure of a complex. The approach is commonly used to screen large libraries of small molecule compounds for potential drug candidates that can target PPIs.
While most models require experimentally determined interactions as prior knowledge, there are also some approaches to predict interactions de novo.
Chen, C. L., & Perrimon, N. (2017). Proximity‐dependent labeling methods for proteomic profiling in living cells. WIREs Developmental Biology, 6(4). https://doi.org/10.1002/wdev.272
Fields, S., & Song, O. (1989). A novel genetic system to detect protein-protein interactions. Nature, 340(6230), 245–246. https://doi.org/10.1038/340245a0
Lin, J. S., & Lai, E. M. (2017). Protein-Protein Interactions: Co-Immunoprecipitation. Methods in molecular biology (Clifton, N.J.), 1615, 211–219. https://doi.org/10.1007/978-1-4939-7033-9_17
Luck, K., Kim, DK., Lambourne, L. et al. A reference map of the human binary protein interactome. Nature 580, 402–408 (2020). https://doi.org/10.1038/s41586-020-2188-x
Michnick, S., Ear, P., Manderson, E. et al. (2007) Universal strategies in research and drug discovery based on protein-fragment complementation assays. Nat Rev Drug Discov, 6, 569–582. https://doi.org/10.1038/nrd2311
Richards, A.L., Eckhardt, M. and Krogan, N.J. (2021) Mass spectrometry‐based protein–protein interaction networks for the study of human diseases, Molecular Systems Biology, 17(1). https://doi.org/10.15252/msb.20188792
Sun, S., Yang, X., Wang, Y., & Shen, X. (2016). In Vivo Analysis of Protein–Protein Interactions with Bioluminescence Resonance Energy Transfer (BRET): Progress and Prospects. International Journal of Molecular Sciences, 17(10), 1704. https://doi.org/10.3390/ijms17101704
Weile, J., Sun, S., Cote, A. G., Knapp, J., Verby, M., Mellor, J. C., … Roth, F. P. (2017). A framework for exhaustively mapping functional missense variants. Molecular Systems Biology, 13(12), 957. https://www.doi.org/10.15252/msb.20177908
Benjamin Dominik Maier
Intekhab Hossain (firstname.lastname@example.org), the first author of this study, is happy to answer questions and guide users through the setup process of PHOENIX, so feel free to send him a message or comment here 🙂