Computational Study of Peptide Plane Stacking with Polar and Ionizable Amino Acid Side Chains

Parallel and T-shaped stacking interactions of the peptide plane with polar and ionizable amino acid side chains (including aspartic/glutamic acid, asparagine/glutamine, and arginine) are investigated using the quantum mechanical MP2 and CCSD computational methods. It is found that the electrostatic interaction plays an essential role in determining the optimal stacking configurations for all investigated stacking models. For certain complexes, the dispersion interaction also contributes considerably to stacking. In the gas phase, the stacking interaction of the charged system is stronger than that of the neutral system, and T-shaped stacking is generally more preferred than parallel stacking, with the stacking energy in the range of −4 to −18 kcal/mol. The solvation effect overall weakens stacking, especially for the charged system and the T-shaped stacking configurations. In water, the interaction energies of different stacking models are comparable

Summary of Illumina transcriptome assembly for <i>Bactrocera minax</i>.

<p>Summary of Illumina transcriptome assembly for <i>Bactrocera minax</i>.</p

Homology search against Nr database for <i>Bactrocera minax</i> transcriptome unigenes.

<p>(A) Distribution of species of top BLAST hit. (B) Distribution of E-value of top BLAST hit with a cut-off E-value of 1.0E^-5. (C) Distribution of similarity of top BLAST hit.</p

Neighbour-joining phylogenetic analysis of heat shock protein (Hsps) genes from <i>Bactrocera minax</i> (●) and other insects.

<p>BD, <i>Bactrocera dorsalis</i>. BT, <i>Bemisia tabaci</i>. CC, <i>Ceratitis capitata</i>. DM, <i>Drosophila melanogaster</i>. LM, <i>Locusta migratoria</i>. Numbers at each branch node represent the values given by bootstrap analysis.</p

Neighbour-joining phylogenetic analysis of two ferritin subunit genes, heavy chain homologs (HCH) and light chain homologs (LCH), from Bactrocera minax (●) and other insects.

<p>AT, <i>Asobara tabida</i>. BD, <i>Bactrocera dorsalis</i>. CC, <i>Ceratitis capitata</i>. DM, <i>Drosophila melanogaster</i>. Numbers at each branch node represent the values given by bootstrap analysis.</p

Gene Ontology (GO) classification of <i>Bactrocera minax</i> transcriptome unigenes.

<p>Gene Ontology (GO) classification of <i>Bactrocera minax</i> transcriptome unigenes.</p

DataSheet1_A self-training subspace clustering algorithm based on adaptive confidence for gene expression data.PDF

Gene clustering is one of the important techniques to identify co-expressed gene groups from gene expression data, which provides a powerful tool for investigating functional relationships of genes in biological process. Self-training is a kind of important semi-supervised learning method and has exhibited good performance on gene clustering problem. However, the self-training process inevitably suffers from mislabeling, the accumulation of which will lead to the degradation of semi-supervised learning performance of gene expression data. To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC), which combines the low-rank representation of gene expression data and adaptive adjustment of label confidence to better guide the partition of unlabeled data. The superiority of the proposed SSCAC algorithm is mainly reflected in the following aspects. 1) In order to improve the discriminative property of gene expression data, the low-rank representation with distance penalty is used to mine the potential subspace structure of data. 2) Considering the problem of mislabeling in self-training, a semi-supervised clustering objective function with label confidence is proposed, and a self-training subspace clustering framework is constructed on this basis. 3) In order to mitigate the negative impact of mislabeled data, an adaptive adjustment strategy based on gravitational search algorithm is proposed for label confidence. Compared with a variety of state-of-the-art unsupervised and semi-supervised learning algorithms, the SSCAC algorithm has demonstrated its superiority through extensive experiments on two benchmark gene expression datasets.</p

Summary of simple sequence repeat (SSRs) types identified in <i>Bactrocera minax</i> transcriptome unigenes.

<p>Summary of simple sequence repeat (SSRs) types identified in <i>Bactrocera minax</i> transcriptome unigenes.</p

Neighbour-joining phylogenetic analysis of Glutathione S-transferase (GSTs) genes from <i>Bactrocera minax</i> (●) and other insects.

<p>BD, <i>Bactrocera dorsalis</i>. CC, <i>Ceratitis capitata</i>. DM, <i>Drosophila melanogaster</i>. Numbers at each branch node represent the values given by bootstrap analysis.</p

Relative expression of 20E-related genes across the developmental stages of <i>Bactrocera minax</i>.

<p>L2, second-instar larvae; L3, third-instar larvae; PreD, pre-diapause; ED, early diapause; MD, middle diapause; LD, late diapause; PD, post-diapause; AD, adult. Bars represent means ± SEM. Different letters above the bars indicate significant differences (P < 0.05, Tukey’s test)</p