Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

Comparative transcriptome profiling of chilling stress responsiveness in grafted watermelon seedlings

Rootstock grafting may improve the resistance of watermelon plants to low temperatures. However, information regarding the molecular responses of rootstock grafted plants to chilling stress is limited. To elucidate the molecular mechanisms of chilling tolerance in grafted plants, the transcriptomic responses of grafted watermelon under chilling stress were analyzed using RNA-seq analysis. Sequencing data were used for digital gene expression (DGE) analysis to characterize the transcriptomic responses in grafted watermelon seedlings. A total of 702 differentially-expressed genes (DEGs) were found in rootstock grafted (RG) watermelon relative to self-grafted (SG) watermelon; among these genes, 522 genes were up-regulated and 180 were down-regulated. Additionally, 164 and 953 genes were found to specifically expressed in RG and SG seedlings under chilling stress, respectively. Functional annotations revealed that up-regulated DEGs are involved in protein processing, plant-pathogen interaction and the spliceosome, whereas down-regulated DEGs are associated with photosynthesis. Moreover, 13 DEGs were randomly selected for quantitative real time PCR (qRT-PCR) analysis. The expression profiles of these 13 DEGs were consistent with those detected by the DGE analysis, supporting the reliability of the DGE data. This work provides additional insight into the molecular basis of grafted watermelon responses to chilling stress.

Automatic classification of protein structures using physicochemical parameters

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.