Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it’s very urgent to develop efficient computational approaches for predicting PSS based on sequence information alone. Moreover, the feature matrix of a protein contains two dimensions: the amino-acid residue dimension and the feature vector dimension. Existing deep learning based methods have achieved remarkable performances of PSS prediction, but the methods often utilize the features from the amino-acid dimension. Thus, there is still room to improve computational methods of PSS prediction. We propose a novel deep neural network method, called DeepACLSTM, to predict 8-category PSS from protein sequence features and profile features. Our method efficiently applies asymmetric convolutional neural networks (ACNNs) combined with bidirectional long short-term memory (BLSTM) neural networks to predict PSS, leveraging the feature vector dimension of the protein feature matrix. In DeepACLSTM, the ACNNs extract the complex local contexts of amino-acids; the BLSTM neural networks capture the long-distance interdependencies between amino-acids. Furthermore, the prediction module predicts the category of each amino-acid residue based on both local contexts and long-distance interdependencies. To evaluate performances of DeepACLSTM, we conduct experiments on three publicly available datasets: CB513, CASP10 and CASP12. Results indicate that the performance of our method is superior to the state-of-the-art baselines on three publicly datasets. Experiments demonstrate that DeepACLSTM is an efficient predication method for predicting 8-category PSS and has the ability to extract more complex sequence-structure relationships between amino-acid residues. Moreover, experiments also indicate the feature vector dimension contains the useful information for improving PSS prediction.

In the domain of metabolomics, the accurate identification of compounds is paramount. However, this process is hindered by the vast number of metabolites, which poses a significant challenge. In this study, a novel approach to compound identification is proposed, namely a molecular-fingerprint prediction method based on the graph attention network (GAT) model. The method involves the processing of fragmentation-tree data derived from tandem mass spectrometry (MS/MS) data computation and the subsequent processing of fragmentation-tree graph data with a technique inspired by natural language processing. The model is then trained using a 3-layer GAT model and a 2-layer linear layer. The results demonstrate the method’s efficacy in molecular-fingerprint prediction, with the prediction of molecular fingerprints from MS/MS spectra exhibiting a high degree of accuracy. Firstly, this model achieves excellent performance in receiver operating characteristic (ROC) and precision–recall curves. The factors that have the most influence on the resultant performance are identified as edge features using different training parameters. Then, better performance is achieved for accuracy and F₁ score in comparison with MetFID. Secondly, the model performance was validated by querying the molecular libraries through methods commonly used in related studies. In the results based on precursor mass querying, the proposed model achieves comparable performance with CFM-ID; in the results based on molecular formula querying, the model achieves better performance than MetFID. This study demonstrates the potential of the GAT model for compound identification tasks and provides directions for further research.

The chemical constituents of ethyl acetate extracts from heartwood and sapwood of different ages of Dalbergia sissoowere studied by gas chromatography-mass spectrometry. The results showed that the chemical composition of wood heartwood and sapwood is significantly different. Inthe vertical direction, the type of the ethyl acetate extract from Dalbergia sissootends to decrease from the base to the upper portion; in the horizontal direction, the type of extract gradually decreases from the center to the periphery. And it showed an increasing trend with the age of the trees. The experiment also revealed that there were significant differences in chemical components between heartwood and sapwood. We speculated that the main chemical component trismethoxyresveratrol of heartwood extract may be related to the formation of heartwood, and the specific correlation needs to be further verified.