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Dihydrouridine (D) is an abundant modified base found in the tRNAs of most living organisms and was recently detected in eukaryotic mRNAs. This base confers significant conformational plasticity to RNA molecules. The dihydrouridine biosynthetic reaction is catalyzed by a large family of flavoenzymes, the dihydrouridine synthases (Dus). So far, only bacterial Dus enzymes and their complexes with tRNAs have been structurally characterized. Understanding the structure-function relationships of eukaryotic Dus proteins has been hampered by the paucity of structural data. Here, we combined extensive phylogenetic analysis with high-precision 3D molecular modeling of more than 30 Dus2 enzymes selected along the tree of life to determine the evolutionary molecular basis of D biosynthesis by these enzymes. Dus2 is the eukaryotic enzyme responsible for the synthesis of D20 in tRNAs and is involved in some human cancers and in the detoxification of β-amyloid peptides in Alzheimer's disease. In addition to the domains forming the canonical structure of all Dus, i.e., the catalytic TIM-barrel domain and the helical domain, both participating in RNA recognition in the bacterial Dus, a majority of Dus2 proteins harbor extensions at both ends. While these are mainly unstructured extensions on the N-terminal side, the C-terminal side extensions can adopt well-defined structures such as helices and beta-sheets or even form additional domains such as zinc finger domains. 3D models of Dus2/tRNA complexes were also generated. This study suggests that eukaryotic Dus2 proteins may have an advantage in tRNA recognition over their bacterial counterparts due to their modularity. [ABSTRACT FROM AUTHOR]

Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model. [ABSTRACT FROM AUTHOR]

Transfer RNAs (tRNAs) are central players in translation, functioning as adapter molecules between the informational level of nucleic acids and the functional level of proteins. They show a highly conserved secondary and tertiary structure and the highest density of post-transcriptional modifications among all RNAs. These modifications concentrate in two hotspots-the anticodon loop and the tRNA core region, where the D- and T-loop interact with each other, stabilizing the overall structure of the molecule. These modifications can cause large rearrangements as well as local fine-tuning in the 3D structure of a tRNA. The highly conserved tRNA shape is crucial for the interaction with a variety of proteins and other RNA molecules, but also needs a certain flexibility for a correct interplay. In this context, it was shown that tRNA modifications are important for temperature adaptation in thermophilic as well as psychrophilic organisms, as they modulate rigidity and flexibility of the transcripts, respectively. Here, we give an overview on the impact of modifications on tRNA structure and their importance in thermal adaptation. [ABSTRACT FROM AUTHOR]

As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is still limited. Accurate identification of the position of D sites is essential for understanding their functions. Therefore, it is desirable to develop novel methods to identify D sites. In this study, an ensemble classifier was proposed for the detection of D modification sites in the Saccharomyces cerevisiae transcriptome by using heterogeneous features. The jackknife test results demonstrate that the proposed predictor is promising for the identification of D modification sites. It is anticipated that the proposed method can be widely used for identifying D modification sites in tRNA. [ABSTRACT FROM AUTHOR]