Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction

A highly sensitive fluorescence sensor for tobacco mosaic virus RNA based on DSN cycle and ARGET ATRP double signal amplification

Early and sensitive detection of tobacco mosaic virus (TMV) is of great significance for improving crop yield and protecting germplasm resources. Herein, we constructed a novel fluorescence sensor to detect TMV RNA (tRNA) through double strand specific nuclease (DSN) cycle and activator regenerative electron transfer atom transfer radical polymerization (ARGET ATRP) dual signal amplification strategy. The hairpin DNA complementarily paired with tRNA was used as a recognition unit to specifically capture tRNA. By the double-stranded DNA hydrolyzed with DSN, tRNA is released to open more hairpin DNA, and more complementary DNA (cDNA) is bound to the surface of the magnetic beads (MBs) to achieve the first amplification. After binding with the initiator, the cDNA employed ARGET ATRP to attach more fluorescent signal molecules to the surface of MBs, thus achieving the second signal amplification. Under the optimal experimental conditions, the logarithm of fluorescence intensity versus tRNA concentration showed a good linear relationship in the range of 0.01-100 pM, with a detection limit of 1.03 fM. The limit of detection (LOD) was calculated according to LOD = 3 N/S. Besides, the sensor showed good reproducibility and stability, which present provided new method for early and highly sensitive detection for plant viruses.

Myricetin derivatives containing the benzoxazinone moiety discovered as potential anti-tobacco mosaic virus agents

A series of myricetin derivatives containing benzoxazinone were designed and synthesized. The structures of all compounds were characterized by NMR and HRMS. The structure of Y4 had been confirmed by single-crystal X-ray diffraction analysis. The test results of EC50 values of tobacco mosaic virus (TMV) suggested that Y8 had the best curative and protective effects, with EC50 values of 236.8, 206.0 μg/mL, respectively, which were higher than that of ningnanmycin (372.4, 360.6 μg/mL). Microscale thermophoresis (MST) experiments demonstrated that Y8 possessed a strong binding affinity for tobacco mosaic virus coat protein (TMV-CP), with a dissociation constant (Kd) value of 0.045 μM, which was superior to the ningnanmycin (0.700 μM). The findings of molecular docking studies revealed that Y8 interacted with multiple amino acid residues of TMV-CP through the formation of non-covalent bonds, which had an effect on the self-assembly of TMV particles. The malondialdehyde (MDA) and superoxide dismutase assay (SOD) content assays also fully verified that Y8 could stimulate the plant immune system and enhance disease resistance by reducing MDA content and increasing SOD content. In summary, myricetin derivatives containing benzoxazinone could be considered to further research and development as novel antiviral agents.

Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information

Vesicle transport proteins not only play an important role in the transmembrane transport of molecules, but also have a place in the field of biomedicine, so the identification of vesicle transport proteins is particularly important. We propose a method based on ensemble learning and evolutionary information to identify vesicle transport proteins. Firstly, we preprocess the imbalanced dataset by random undersampling. Secondly, we extract position-specific scoring matrix (PSSM) from protein sequences, and then further extract AADP-PSSM and RPSSM features from PSSM, and use the Max-Relevance-Max-Distance (MRMD) algorithm to select the optimal feature subset. Finally, the optimal feature subset is fed into the stacked classifier for vesicle transport proteins identification. The experimental results show that the of accuracy (ACC), sensitivity (SN) and specificity (SP) of our method on the independent testing set are 82.53%, 0.774 and 0.836, respectively. The SN, SP and ACC of our proposed method are 0.013, 0.007 and 0.76% higher than the current state-of-the-art methods.