Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins

• Published in: International journal of molecular sciences Vol. 24; no. 7; p. 6262
• Main Authors: Rozano, Lina, Mukuka, Yvonne M, Hane, James K, Mancera, Ricardo L
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.03.2023; MDPI
• Subjects: Analysis; Antifungal agents; Ascomycota; Computer applications; Conserved sequence; Crop diseases; Cultivars; Disease Resistance; Effectors; Fungal diseases; fungal effector protein; Fungal Proteins - metabolism; Fungi; Health aspects; Infections; MAX effector family; MaxCluster; Modelling; NMR; Nuclear magnetic resonance; Plant Breeding; Plant diseases; Plant Diseases - microbiology; Protein families; Protein structure; Proteins; Rosetta; Similarity; Tertiary structure; TM-score; ToxA-like effector family; Virulence; TM-score; fungal effector protein; MaxCluster; ToxA-like effector family; ab initio modelling; MAX effector family; Rosetta
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24076262

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.