Use of tree-based machine learning methods to screen affinitive peptides based on docking data

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

A Random Forest Model for Peptide Classification Based on Virtual Docking Data

The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.

Proteomics analysis reveals that the proto-oncogene eIF-5A indirectly influences the growth, invasion and replication of Toxoplasma gondii tachyzoite

BACKGROUND

The proliferative stage (tachyzoite) of Toxoplasma gondii (T. gondii) is critical for its transmission and pathogenesis, and a proto-oncogene eukaryotic translation initiation factor (eIF-5A) plays an important role in various cellular processes such as cell multiplication.

METHODS

We performed a proteomic study to evaluate the specific roles of eIF-5A involved in invasion and replication of T. gondii, and both in vivo and in vitro trials using eIF-5A-interfered and wild tachyzoites were performed to verify the proteomic results.

RESULTS

The results of our study showed that T. gondii eIF-5A affected tachyzoite growth and also participated in the synthesis of proteins through regulation of both ribosomal and splicing pathways. Inhibition of eIF-5A in T. gondii resulted in the downregulated expression of soluble adhesions, such as microneme protein 1 (MIC1) and MIC4, which in turn decreased the parasite population that adhered to the surface of host cells. The reduced attachment, combined with lower expression of some rhoptry proteins (ROPs) and dense granule antigens (GRAs) involved in different stages of T. gondii invasion such as ROP4 and GRA3, ultimately reduce the invasion efficiency. These processes regulated by eIF-5A eventually affect the replication of tachyzoites.

CONCLUSIONS

Our findings showed that eIF-5A influenced tachyzoite survival and was also involved in the process of parasite invasion and replication. These results will provide new clues for further development of targeted drugs to control T. gondii infection.