2022
DOI: 10.1080/07391102.2022.2139763
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In silicocharacterization, molecular docking, and dynamic simulation of a novel fungal cell-death suppressing effector, MoRlpA as potential cathepsin B-like cysteine protease inhibitor during rice blast infection

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Cited by 5 publications
(2 citation statements)
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“…Three C. cinerea cerato-platanin genes showed significant downregulation in Δsnb1 . Structurally, cerato-platanins show a double-Ψ-ꞵ-barrel (DPBB) topology of the RlpA superfamily, an architecture that is also typical to further proteins, like expansins (58, 59), certain endoglucanases (60) and virulence factors of plant pathogens (61, 62). An additional interesting example with this domain structure is the DEG ID: 421450, showing the highest upregulation (FC: 21.86) in Δsnb1 strain.…”
Section: Resultsmentioning
confidence: 99%
“…Three C. cinerea cerato-platanin genes showed significant downregulation in Δsnb1 . Structurally, cerato-platanins show a double-Ψ-ꞵ-barrel (DPBB) topology of the RlpA superfamily, an architecture that is also typical to further proteins, like expansins (58, 59), certain endoglucanases (60) and virulence factors of plant pathogens (61, 62). An additional interesting example with this domain structure is the DEG ID: 421450, showing the highest upregulation (FC: 21.86) in Δsnb1 strain.…”
Section: Resultsmentioning
confidence: 99%
“…The identification of M. oryzae effectors, their localization, function, and interactions requires researchers to take a multidisciplinary approach to examine potential effectors, with a large portion of initial work beginning in silico (Ray et al 2016;Sarkar et al 2022). With the rise of machine learning technologies, many programs can utilize gene sequences to predict secretion signals (SignalP) or overall effector characteristics such as protein size, charge, and amino acid compositions (EffectorP) (Almagro Armenteros et al 2019;Sperschneider et al 2015Sperschneider et al , 2016Sperschneider et al , 2018.…”
Section: Unlocking the Secrets Of True Identitymentioning
confidence: 99%