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1. Computational identification of protein-protein interactions in model plant proteomes

   https://doi.org/10.1038/s41598-019-45072-8  

   Ding, Ziyun ; Kihara, Daisuke ( June 2019 , Scientific Reports)

   Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs ofArabidopsis thalianaand applied to three plants,Arabidopsis thaliana,Glycine max(soybean), andZea mays(maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets ofArabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs inArabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

    

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2. FRETting about the affinity of bimolecular protein–protein interactions

   https://doi.org/10.1002/pro.3482  

   Lin, Tao ; Scott, Brandon L. ; Hoppe, Adam D. ; Chakravarty, Suvobrata ( October 2018 , Protein Science)

   Fluorescenceresonanceenergytransfer (FRET) is a powerful tool to study macromolecular interactions such as protein–protein interactions (PPIs). Fluorescent protein (FP) fusions enable FRET‐based PPI analysis of signaling pathways and molecular structure in living cells. Despite FRET's importance in PPI studies, FRET has seen limited use in quantifying the affinities of PPIs in living cells. Here, we have explored the relationship between FRET efficiency and PPI affinity over a wide range when expressed from a single plasmid system inEscherichia coli.Using live‐cell microscopy and a set of 20 pairs of small interacting proteins, belonging to different structural folds and interaction affinities, we demonstrate that FRET efficiency can reliably measure the dissociation constant (K_D) over a range of mMto nM. A 10‐fold increase in the interaction affinity results in 0.05 unit increase in FRET efficiency, providing sufficient resolution to quantify large affinity differences (> 10‐fold) using live‐cell FRET. This approach provides a rapid and simple strategy for assessment of PPI affinities over a wide range and will have utility for high‐throughput analysis of protein interactions.

    

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3. Using machine learning to predict protein–protein interactions between a zombie ant fungus and its carpenter ant host

   https://doi.org/10.1038/s41598-023-40764-8  

   Will, Ian ; Beckerson, William C. ; de Bekker, Charissa ( December 2023 , Scientific Reports)

   Parasitic fungi produce proteins that modulate virulence, alter host physiology, and trigger host responses. These proteins, classified as a type of “effector,” often act via protein–protein interactions (PPIs). The fungal parasiteOphiocordyceps camponoti-floridani(zombie ant fungus) manipulatesCamponotus floridanus(carpenter ant) behavior to promote transmission. The most striking aspect of this behavioral change is a summit disease phenotype where infected hosts ascend and attach to an elevated position. Plausibly, interspecific PPIs drive aspects ofOphiocordycepsinfection and host manipulation. Machine learning PPI predictions offer high-throughput methods to produce mechanistic hypotheses on how this behavioral manipulation occurs. Using D-SCRIPT to predict host–parasite PPIs, we found ca. 6000 interactions involving 2083 host proteins and 129 parasite proteins, which are encoded by genes upregulated during manipulated behavior. We identified multiple overrepresentations of functional annotations among these proteins. The strongest signals in the host highlighted neuromodulatory G-protein coupled receptors and oxidation–reduction processes. We also detectedCamponotusstructural and gene-regulatory proteins. In the parasite, we found enrichment ofOphiocordycepsproteases and frequent involvement of novel small secreted proteins with unknown functions. From these results, we provide new hypotheses on potential parasite effectors and host targets underlying zombie ant behavioral manipulation.

    

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4. Electron‐deficient p‐benzoyl‐ l ‐phenylalanine derivatives increase covalent chemical capture yields for protein–protein interactions

   https://doi.org/10.1002/pro.3621  

   Joiner, Cassandra M. ; Breen, Meghan E. ; Mapp, Anna K. ( April 2019 , Protein Science)

   The photoactivatable amino acid p‐benzoyl‐l‐phenylalanine (pBpa) has been used for the covalent capture of protein–protein interactions (PPIs)in vitroand in living cells. However, this technique often suffers from poor photocrosslinking yields due to the low reactivity of the active species. Here we demonstrate that the incorporation of halogenated pBpa analogs into proteins leads to increased crosslinking yields for protein–protein interactions. The analogs can be incorporated into live yeast and upon irradiation capture endogenous PPIs. Halogenated pBpas will extend the scope of PPIs that can be captured and expand the toolbox for mapping PPIs in their native environment.

    

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5. Streamlined Protein‐Protein Interface Loop Mimicry

   https://doi.org/10.1002/ange.202307092  

   Mi, Tianxiong ; Siriwibool, Siriwalee ; Burgess, Kevin ( November 2023 , Angewandte Chemie)

   Cyclic peptides comprising endocyclic organic fragments,“cyclo‐organopeptides”, can be probes for perturbing protein‐protein interactions (PPIs). Finding loop mimics is difficult because of high conformational variability amongst targets. Backbone Matching (BM), introduced here, helps solve this problem in the illustrative cases by facilitating efficient evaluation of virtualcyclo‐organopeptide core‐structure libraries. Thus, 86 rigid organic fragments were selected to build a library of 602cyclo‐organopeptides comprising Ala and organic parts:“cyclo‐{‐(Ala)_n‐organo‐}”. The central hypothesis is “hit” library members have accessible low energy conformers corresponding to backbone structures of target protein loops, while library members whichcannotattain this conformation are probably unworthy of further evaluation. BM thereby prioritizes candidate loop mimics, so that less than 10 cyclo‐organopeptides are needed to be prepared to find leads for two illustrative PPIs: iNOS ⋅ SPSB2, and uPA ⋅ uPAR.

    

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