Insect resistance to insecticides
is an increasingly serious problem,
and the resistant mechanisms are complicated. The resistance research
based on the chemosensory pathway is one of the hot problems at present,
but the specific binding mechanism of chemosensory genes and insecticides
remains elusive. The binding mechanism of AlepGOBP2
(belong to insect chemosensory gene) with two insecticides was investigated
by computational and experimental approaches. Our calculation results
indicated that four key residues (Phe12, Ile52, Ile94, and Phe118)
could steadily interact with these two insecticides and be assigned
as hotspot sites responsible for their binding affinities. The significant
alkyl−π and hydrophobic interactions involved by these
four hotspot residues were found to be the driving forces for their
binding affinities, especially for two residues (Phe12 and Ile94)
that significantly contribute to the binding of chlorpyrifos, which
were also validated by our binding assay results. Furthermore, we
also found that the AlepGOBP2–chlorpyrifos/phoxim
complexes can be more efficiently converged in the residue-specific
force field-(RSFF2C) and its higher accuracy and repeatability in
protein dynamics simulation, per-residue free energy decomposition,
and computational alanine scanning calculations have also been achieved
in this paper. These findings provided useful insights for efficient
and reliable calculation of the binding mechanism of relevant AlepGOBPs with other insecticides, facilitating to develop
new and efficient insecticides targeting the key sites of AlepGOBP2.
Background. MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement. Methodology/Principal Findings. Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods. Conclusions. MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.
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