Template-Based Modelling of the Structure of Fungal Effector Proteins

• Published in: Molecular biotechnology Vol. 66; no. 4; pp. 784 - 813
• Main Authors: Rozano, Lina, Jones, Darcy A. B., Hane, James K., Mancera, Ricardo L.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Amino Acid Sequence; Biochemistry; Bioinformatics; Biological Techniques; Biotechnology; Cell Biology; Chemistry; Chemistry and Materials Science; Computational Biology - methods; Conserved sequence; Cultivars; Cytotoxicity; Defensins; Disease resistance; Effectors; Fungal Proteins - chemistry; Fungal Proteins - genetics; Fungal Proteins - metabolism; Fungi; Fungi - chemistry; Fungi - genetics; Fungi - metabolism; Human Genetics; Modelling; Models, Molecular; Molecular docking; Molecular Docking Simulation; Original Paper; Peptides; Predictions; Protein Conformation; Protein Science; Proteins; 3D structure; Cytotoxic peptides; Fungal effector proteins; Structural families; Template-based modelling
• ISSN: 1073-6085; 1559-0305
• DOI: 10.1007/s12033-023-00703-4

The discovery of new fungal effector proteins is necessary to enable the screening of cultivars for disease resistance. Sequence-based bioinformatics methods have been used for this purpose, but only a limited number of functional effector proteins have been successfully predicted and subsequently validated experimentally. A significant obstacle is that many fungal effector proteins discovered so far lack sequence similarity or conserved sequence motifs. The availability of experimentally determined three-dimensional (3D) structures of a number of effector proteins has recently highlighted structural similarities amongst groups of sequence-dissimilar fungal effectors, enabling the search for similar structural folds amongst effector sequence candidates. We have applied template-based modelling to predict the 3D structures of candidate effector sequences obtained from bioinformatics predictions and the PHI-BASE database. Structural matches were found not only with ToxA- and MAX-like effector candidates but also with non-fungal effector-like proteins—including plant defensins and animal venoms—suggesting the broad conservation of ancestral structural folds amongst cytotoxic peptides from a diverse range of distant species. Accurate modelling of fungal effectors were achieved using RaptorX. The utility of predicted structures of effector proteins lies in the prediction of their interactions with plant receptors through molecular docking, which will improve the understanding of effector–plant interactions.