Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization. Graphical Abstract
<abstract> <p>Protein interactions are the foundation of all metabolic activities of cells, such as apoptosis, the immune response, and metabolic pathways. In order to optimize the performance of protein interaction prediction, a coding method based on normalized difference sequence characteristics (NDSF) of amino acid sequences is proposed. By using the positional relationships between amino acids in the sequences and the correlation characteristics between sequence pairs, NDSF is jointly encoded. Using principal component analysis (PCA) and local linear embedding (LLE) dimensionality reduction methods, the coded 174-dimensional human protein sequence vector is extracted using sequence features. This study compares the classification performance of four ensemble learning methods (AdaBoost, Extra trees, LightGBM, XGBoost) applied to PCA and LLE features. Cross-validation and grid search methods are used to find the best combination of parameters. The results show that the accuracy of NDSF is generally higher than that of the sequence matrix-based coding method (MOS) coding method, and the loss and coding time can be greatly reduced. The bar chart of feature extraction shows that the classification accuracy is significantly higher when using the linear dimensionality reduction method, PCA, compared to the nonlinear dimensionality reduction method, LLE. After classification with XGBoost, the model accuracy reaches 99.2%, which provides the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.</p> </abstract>
Protein-protein interaction (PPIs) is an important part of many life activities in organisms, and the prediction of protein-protein interactions is closely related to protein function, disease occurrence, and disease treatment. In order to optimize the prediction performance of protein interactions, here a RT-MOS model was constructed based on Random Forest (RF) and Matrix of Sequence (MOS) to predict protein-protein interactions. Firstly, MOS is used to encode the protein sequences into a 29-dimensional feature vector; Then, a prediction model RT-MOS is build based on random forest, and the RT-MOS model is optimized and evaluated using the test set; Finally, the optimized model RT-MOS is used for prediction. The experimental results show that the accuracy rates of the RT-MOS model on the benchmark dataset and the non-redundant dataset are 97.18% and 91.34%, respectively, and the accuracies on four external datasets of C.elegans, Drosophila, E.coli and H.sapiens are 96.21%, 97.86%, 97.54% and 97.75%, respectively. Compared with the existing methods, it is found that it is superior to the existing methods. The experimental results show that the model RT-MOS has the advantages of saving time, preventing overfitting and high accuracy, and is suitable for large-scale PPIs prediction.
Background Protein interactions is the foundation of all metabolic activities of cells, such as apoptosis, immune response and metabolic pathway. In order to optimize the performance of protein interaction prediction, a coding method based on normalized difference sequence characteristics (NDSF) of amino acid sequences is proposed. Methods By using the positional relationships between amino acids in the sequences and the correlation characteristics between sequence pairs, NDSF is jointly encoded. Using principal component analysis (PCA) and local linear embedding (LLE) dimensionality reduction methods, the coded 174-dimensional human protein sequence vector is extracted by using sequence features. This study compares the classification performance of four ensemble learning methods (AdaBoost, Extra trees, LightGBM, XGBoost) applied to PCA and LLE features, and uses cross-validation and grid search methods to find the best combination of parameters. Results The accuracy of NDSF is generally higher than that of MOS coding method, and the loss and coding time can be greatly reduced. The bar chart of feature extraction shows that the classification accuracy is significantly higher when using the linear dimensionality reduction method PCA than the nonlinear dimensionality reduction method LLE. After classification with XGBoost, the model accuracy reaches 99.2%, which is the best performance among all models. Conclusions NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.
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