Abstract
Proteins rarely exert their function by themselves. Protein–protein interactions (PPIs) regulate virtually every biological process that takes place in a cell. Such interactions are targets for new therapeutic agents against all sorts of diseases, through the screening and design of a variety of inhibitors. Here we discuss several aspects of PPIs that contribute to prediction of protein function and drug discovery. As the high-throughput techniques continue to release biological data, targets for fungal therapeutics that rely on PPIs are being proposed worldwide. Computational approaches have reduced the time taken to develop new therapeutic approaches. The near future brings the possibility of developing new PPI and interaction network inhibitors and a revolution in the way we treat fungal diseases.
Papers of special note have been highlighted as: • of interest; •• of considerable interest
References
- 1. . Small-molecule inhibitors of protein–protein interactions: progressing towards the dream. Nat. Rev. Drug Discov. 3(4), 301–317 (2004).
- 2. . A single tool to monitor multiple protein–protein interactions of the Escherichia coli acyl carrier protein. ACS Infect. Dis. 5(9), 1518–1523 (2019).
- 3. Protein interactions of the mechanosensory proteins Wsc2 and Wsc3 for stress resistance in Saccharomyces cerevisiae. G3 (Bethesda) 10(9), 3121–3135 (2020).
- 4. . Analysis of protein–protein Interaction by co-IP in human cells. Methods Mol. Biol. 1794, 289–296 (2018).
- 5. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32(8), 822–828 (2014).
- 6. . Breakthrough invasive fungal infections: who is at risk? Mycoses 63(10), 1021–1032 (2020).
- 7. . Visualization and targeted disruption of protein interactions in living cells. Nat. Commun. 4, 2660 (2013).
- 8. . Predicting permanent and transient protein–protein interfaces. Proteins 81(5), 805–818 (2013). • Investigates the prediction of protein–protein interactions (PPIs) based on their interfaces.
- 9. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430(6995), 88–93 (2004).
- 10. . A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinformatics 10(1), 169 (2009).
- 11. . Biological Data Mining in Protein Interaction Networks. IGI Global, Singapore (2009).
- 12. . Protein–protein interaction detection: methods and analysis. Int. J. Proteomics 2014, 1–12 (2014).
- 13. . Interactive proteomics research technologies: recent applications and advances. Curr. Opin. Biotechnol. 22(1), 50–58 (2011).
- 14. Interactome of glyceraldehyde-3-phosphate dehydrogenase points to the existence of metabolons in Paracoccidioides lutzii. Front. Microbiol. 10, 1537 (2019). •• Exploits the identification of PPIs in a non-model pathogenic species.
- 15. Screening for potential interaction partners with surface plasmon resonance imaging coupled to MALDI mass spectrometry. Anal. Biochem. 624, 114195 (2021).
- 16. . Introducing fluorescence resonance energy transfer-based biosensors for the analysis of cAMP-PKA signalling in the fungal pathogen Candida glabrata. Cell. Microbiol. 20(10), e12863 (2018).
- 17. . Protein–protein interaction assays. In: Proteomics. MR Ansari (Ed.). InTechOpen (2018).
- 18. The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 24(3), 218–229 (2001).
- 19. . A protein expression system for tandem affinity purification in Xanthomonas citri subsp. citri. Braz. J. Microbiol. 47(2), 518–526 (2016).
- 20. . Affinity chromatography for the purification of therapeutic proteins from transgenic maize using immobilized histamine. J. Sep. Sci. 31(4), 636–645 (2008).
- 21. Affinity purification of mammalian RNA polymerase I. Identification of an associated kinase. J. Biol. Chem. 273(2), 1257–1267 (1998).
- 22. . Identification of protein complexes from filamentous fungi with tandem affinity purification. Methods Mol. Biol. 944, 191–205 (2012).
- 23. Proteomic profile response of Paracoccidioides lutzii to the antifungal argentilactone. Front. Microbiol. 6, 616 (2015).
- 24. . Protein–protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput. Biol. 6(6), e1000807 (2010).
- 25. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006). •• Exploits a methodology of large-scale identification of PPIs and protein complexes in yeast.
- 26. . Tcc1p, a novel protein containing the tetratricopeptide repeat motif, interacts with Tup1p to regulate morphological transition and virulence in Candida albicans. Eukaryot. Cell 5(11), 1894–1905 (2006).
- 27. . Cyclin-dependent kinases control septin phosphorylation in Candida albicans hyphal development. Dev. Cell 13(3), 421–432 (2007).
- 28. . Septins focus cellular growth for host infection by pathogenic fungi. Front. Cell Dev. Biol. 5, 33 (2017).
- 29. . The application of tandem-affinity purification to Candida albicans. Methods Mol. Biol. 499, 133–148 (2009).
- 30. Affinity purification of Candida albicans CaCdc4-associated proteins reveals the presence of novel proteins involved in morphogenesis. Biochem. Biophys. Res. Commun. 395(1), 152–157 (2010).
- 31. The mitochondrial protein Mcu1 plays important roles in carbon source utilization, filamentation, and virulence in Candida albicans. Fungal Genet. Biol. 81, 150–159 (2015).
- 32. . Extensive functional redundancy in the regulation of Candida albicans drug resistance and morphogenesis by lysine deacetylases Hos2, Hda1, Rpd3 and Rpd31. Mol. Microbiol. 103(4), 635–656 (2017).
- 33. . Protein–protein interactions: co-immunoprecipitation. Methods Mol. Biol. 1615, 211–219 (2017).
- 34. . Use of immunomatrix methods to improve protein–protein interaction detection. J. Biomed. Biotechnol. 2003(5), 291–298 (2003). •• Proposes an immunoprecipitation assay to improve the detection of PPIs.
- 35. . Current experimental methods for characterizing protein–protein interactions. ChemMedChem 11(8), 738–756 (2016).
- 36. . Coimmunoprecipitation of proteins from yeast. Methods Enzymol. 541, 13–26 (2014).
- 37. . Studying protein–protein interactions in budding yeast using co-immunoprecipitation. Methods Mol. Biol. 1369, 239–256 (2016).
- 38. . Characterization of additional components of the environmental pH-sensing complex in the pathogenic fungus Cryptococcus neoformans. J. Biol. Chem. 293(26), 9995–10008 (2018).
- 39. The Rim pathway mediates antifungal tolerance in Candida albicans through newly identified Rim101 transcriptional targets, including Hsp90 and Ipt1. Antimicrob. Agents Chemother. 62(3), e01785–17 (2018).
- 40. Interaction of isocitrate lyase with proteins involved in the energetic metabolism in Paracoccidioides lutzii. J. Fungi 6(4), E309 (2020). • Investigates the interactome of ICL, an enzyme related to pathogenesis in several infectious fungi species.
- 41. . How complete are current yeast and human protein-interaction networks? Genome Biol. 7(11), 120 (2006).
- 42. Intermolecular interactions of the malate synthase of Paracoccidioides spp. BMC Microbiol. 13, 107 (2013).
- 43. . A high-throughput Candida albicans two-hybrid system. mSphere 3(4), e00391–18 (2018).
- 44. . Neurospora crassa mat A-2 and mat A-3 proteins weakly interact in the yeast two-hybrid system and affect yeast growth. Genet. Mol. Biol. 32, 354–361 (2009).
- 45. . Regulation of mating type switching by the mating type genes and RME1 in Ogataea polymorpha. Sci. Rep. 7(1), 16318 (2017).
- 46. Detection of a homotetrameric structure and protein–protein interactions of Paracoccidioides brasiliensis formamidase lead to new functional insights. FEMS Yeast Res. 10(1), 104–113 (2010).
- 47. . Fungal lifestyle reflected in serine protease repertoire. Sci. Rep. 7(1), 9147 (2017).
- 48. A secreted serine protease of Paracoccidioides brasiliensis and its interactions with fungal proteins. BMC Microbiol. 10(1), 292 (2010).
- 49. . Evolutionary reshaping of fungal mating pathway scaffold proteins. mBio 2(1), e00230–00210 (2011).
- 50. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49(D1), D605–D612 (2021).
- 51. . Investigation of stable and transient protein–protein interactions: past, present, and future. Proteomics 13(3–4), 538–557 (2013).
- 52. . Computational methods for predicting protein–protein interactions using various protein features. Curr. Protoc. Protein Sci. 93(1), e62 (2018).
- 53. . Prediction of potential protein–protein interaction sites from amino acid sequence. Identification of a fibrin polymerization site. FEBS Lett. 385(1–2), 81–86 (1996).
- 54. . Correlated sequence-signatures as markers of protein–protein interaction. J. Mol. Biol. 311(4), 681–692 (2001).
- 55. GPS-SNO: computational prediction of protein S-nitrosylation sites with a modified GPS algorithm. PLoS ONE 5(6), e11290 (2010).
- 56. The ClusPro web server for protein–protein docking. Nat. Protoc. 12(2), 255–278 (2017). • Describes a robust online protein–protein docking server.
- 57. . KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins 79(9), 2671–2683 (2011). • Describes a server largely used to predict hotspot residues within the interface of interaction.
- 58. . COCOMAPS: a web application to analyze and visualize contacts at the interface of biomolecular complexes. Bioinformatics 27(20), 2915–2916 (2011).
- 59. . Principles that govern the folding of protein chains. Science 181(4096), 223–230 (1973).
- 60. . Predicting protein–protein interactions from primary structure. Bioinformatics 17(5), 455–460 (2001).
- 61. Predicting protein–protein interactions based only on sequences information. Proc. Natl Acad. Sci. USA 104(11), 4337–4341 (2007).
- 62. . Towards the prediction of protein interaction partners using physical docking. Mol. Syst. Biol. 7, 469 (2011).
- 63. . High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc. Natl Acad. Sci. USA 105(3), 934–939 (2008).
- 64. . Prediction of protein–protein interactions using random decision forest framework. Bioinformatics 21(24), 4394–4400 (2005).
- 65. . Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature 450(7172), 1001–1009 (2007).
- 66. . PIRSF family classification system for protein functional and evolutionary analysis. Evol. Bioinform. Online 2, 197–209 (2007).
- 67. . Protein structure annotation resources. In: Structural Proteomics. Owens RJ (Ed.). Springer, NY, USA, 3–20 (2014).
- 68. . Gene ontology functional annotations at the structural domain level. Proteins 76(3), 598–607 (2009).
- 69. . Prediction of protein function using protein–protein interaction data. Proc. IEEE Comput. Soc. Bioinform. Conf. 1, 197–206 (2002).
- 70. . Some remarks on prediction of protein–protein interaction with machine learning. Med. Chem. 11(3), 254–264 (2015).
- 71. . What do we learn from high-throughput protein interaction data? Expert Rev. Proteomics 1(1), 111–121 (2004).
- 72. . A network of protein–protein interactions in yeast. Nat. Biotechnol. 18(12), 1257–1261 (2000).
- 73. . Assessment of prediction accuracy of protein function from protein–protein interaction data. Yeast 18(6), 523–531 (2001).
- 74. . Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions. Bioinformatics 22(13), 1623–1630 (2006).
- 75. . Protein function prediction using guilty by association from interaction networks. Amino Acids 47(12), 2583–2592 (2015).
- 76. . Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21(Suppl. 1), i302–i310 (2005).
- 77. . Detection of functional modules from protein interaction networks. Proteins 54(1), 49–57 (2003).
- 78. . Protein function prediction using neighbor relativity in protein–protein interaction network. Comput. Biol. Chem. 43, 11–16 (2013).
- 79. . An efficient method for protein function annotation based on multilayer protein networks. Hum. Genomics 10(1), 33 (2016).
- 80. . Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae. OMICS 8(4), 322–333 (2004).
- 81. . Modulating protein–protein interactions: the potential of peptides. Chem. Commun. 51(16), 3302–3315 (2015).
- 82. . Protein interactions: is seeing believing? Trends Biochem. Sci. 32(12), 530–531 (2007).
- 83. . Protein interactions: integration leads to belief. Trends Biochem. Sci. 33(6), 241–242 (2008).
- 84. . Principles of protein–protein recognition. Nature 256(5520), 705–708 (1975).
- 85. . Characterization of protein–protein interaction interfaces from a single species. PLoS ONE 6(6), e21053 (2011).
- 86. Integrating fragment assembly and biophysical methods in the chemical advancement of small-molecule antagonists of IL-2: an approach for inhibiting protein–protein interactions. J. Med. Chem. 47(12), 3111–3130 (2004).
- 87. Crystal structure of the E2 transactivation domain of human papillomavirus type 11 bound to a protein interaction inhibitor. J. Biol. Chem. 279(8), 6976–6985 (2004).
- 88. . Effects of conformational dynamics on predicted protein druggability. ChemMedChem 1(1), 70–72 (2006).
- 89. . Protein–protein interactions as druggable targets: recent technological advances. Curr. Opin. Pharmacol. 13(5), 791–796 (2013).
- 90. . Synthetic therapeutic peptides: science and market. Drug Discov. Today 15(1), 40–56 (2010).
- 91. A structure-based approach for the discovery of inhibitors against methylcitrate synthase of Paracoccidioides lutzii. J. Biomol. Struct. Dyn. 40(19), 9361–9373 (2022).
- 92. Interaction of isocitrate lyase with proteins involved in the energetic metabolism in Paracoccidioides lutzii. J. Fungi 6(4), 309 (2020).
- 93. Identification of functional and druggable sites in Aspergillus fumigatus essential phosphatases by virtual screening. Int. J. Mol. Sci. 20(18), 4636 (2019).
- 94. . Inhibition of pathogenically-related morphologic transition in Candida albicans by disrupting Cdc42 binding to its effectors. Adv. Exp. Med. Biol. 611, 471–472 (2009).
- 95. Crystal structures of the Gon7/Pcc1 and Bud32/Cgi121 complexes provide a model for the complete yeast KEOPS complex. Nucleic Acids Res. 43(6), 3358–3372 (2015). • Shows an example of a peptide that inhibits fungal PPIs.