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Advances in Infrared (IR) spectroscopies have entered a new era of research with applications in phytobiome, plant microbiome and health. Fusarium graminearum 3-ADON is the most aggressive mycotoxigenic chemotype causing Fusarium head blight (FHB) in cereals, while Sphaerodes mycoparasitica is the specific Fusarium mycoparasite with biotrophic lifestyle discovered in cereal seeds and roots. Fourier transform infrared (FTIR) spectroscopy analyses depicted shifts in the spectral peaks related to mycoparasitism mainly within the region of proteins, lipids, also indicating a link between carbohydrates and protein regions, involving potential phenolic compounds. Especially, S. mycoparasitica contributes to significant changes in lipid region 3050–2800 cm−1, while in the protein region, an increasing trend was observed for the peaks 1655–1638 cm−1 (amide I) and 1549–1548 cm−1 (amide II) with changes in indicative protein secondary structures. Besides, the peak extending on the region 1520–1500 cm−1 insinuates a presence of aromatic compounds in presence of mycoparasite on the F. graminearum root sample. Monitoring shift in improved seed germination, fungus-fungus interface through scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM), and FTIR molecular signatures combined with principal component analysis (PCA) proved useful tools to detect an early mycoparasitism as a vital asset of the preventive biocontrol strategy against plant pathogens.

Accurate prediction of intrinsically disordered proteins/regions is one of the most important tasks in bioinformatics, and some computational predictors have been proposed to solve this problem. How to efficiently incorporate the sequence-order effect is critical for constructing an accurate predictor because disordered region distributions show global sequence patterns. In order to capture these sequence patterns, several sequence labelling models have been applied to this field, such as conditional random fields (CRFs). However, these methods suffer from certain disadvantages. In this study, we proposed a new computational predictor called IDP&ndash, CRF, which is trained on an updated benchmark dataset based on the MobiDB database and the DisProt database, and incorporates more comprehensive sequence-based features, including PSSMs (position-specific scoring matrices), kmer, predicted secondary structures, and relative solvent accessibilities. Experimental results on the benchmark dataset and two independent datasets show that IDP&ndash, CRF outperforms 25 existing state-of-the-art methods in this field, demonstrating that IDP&ndash, CRF is a very useful tool for identifying IDPs/IDRs (intrinsically disordered proteins/regions). We anticipate that IDP&ndash, CRF will facilitate the development of protein sequence analysis.