bioRxivBiochemistryPreprint
Altered stereostructures of the DNA-binding domains in the mutant mating proteins of Ophiocordyceps sinensis and the Cordyceps sinensis insect-fungal complex
Abstract
The MAT_HMGbox and HMG-box_ROX1-like domains of the MAT1-1-1 and MAT1-2-1 proteins, respectively, play essential roles in DNA binding and the downstream regulation of gene transcription that controls sexual reproduction in Ophiocordyceps sinensis. Alternative splicing, differential occurrence and transcription of the MAT1-1-1 and MAT1-2-1 genes have been revealed in Hirsutella sinensis (Genotype #1 of the 17 O. sinensis genotypes), suggesting the occurrence of self-sterility under heterothallic or hybrid outcrossing. This study demonstrates that the MAT_HMGbox domains of MAT1-1-1 proteins in wild-type Cordyceps sinensis isolates cluster into 5 clades with or without branches in the Bayesian clustering tree and belong to diverse 3D structural morphs under 19 AlphaFold codes. The HMG-box_ROX1-like domains of MAT1-2-1 proteins cluster into 2 clades with branches in the Bayesian clustering tree and belong to diverse 3D structural morphs under 25 AlphaFold codes. Correlation analysis reveals that 1-3 amino acid substitutions at various sites in the DNA-binding domains of the mutant mating proteins result in alterations in the hydrophobicity and the secondary and tertiary structures of the DNA-binding domains of the proteins. Fungal origin analysis reveals heterospecific fungal sources or genome-independent, genotypic sources of the mating proteins with stereostructure variations in the wild-type C. sinensis isolates that represent the changed functionalities of the proteins in regulating and ensuring the accuracy and genetic diversity of the heterothallic and hybrid sexual reproductive processes of O. sinensis during the lifecycle of the C. sinensis insect-fungi complex.
- organism: fungi (other)
- assay: protein structure
- research focus: chromatin & epigenetic regulation
- research focus: protein folding & dynamics
- approach: statistical model
- approach: deep learning
- Citations
- 0
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