Prediction of the interaction between Calloselasma rhodostoma venom-derived peptides and cancer-associated hub proteins: A computational study

Abstract

The use of peptide drugs to treat cancer is gaining popularity because of their efficacy, fewer side effects, and several advantages over other properties. Identifying the peptides that interact with cancer proteins is crucial in drug discovery. Several approaches related to predicting peptide-protein interactions have been conducted. However, problems arise due to the high costs of resources and time and the smaller number of studies. This study predicts peptide-protein interactions using Random Forest, XGBoost, and SAE-DNN. Feature extraction is also performed on proteins and peptides using intrinsic disorder, amino acid sequences, physicochemical properties, position-specific assessment matrices, amino acid composition, and dipeptide composition. Results show that all algorithms perform equally well in predicting interactions between peptides derived from venoms and target proteins associated with cancer. However, XGBoost produces the best results with accuracy, precision, and area under the receiver operating characteristic curve of 0.859, 0.663, and 0.697, respectively. The enrichment analysis revealed that peptides from the Calloselasma rhodostoma venom targeted several proteins (ESR1, GOPC, and BRD4) related to cancer.

Keywords: Bioinformatics; Biomedical; Cancer; Deep learning; Peptide; Venom.

Fig. 1a

ROC curve for model 1–5. Fig. 1b. ROC curve for model 6-13.

Fig. 2a

Precision-Recall (PR) curve for model 1–5. Fig. 2b. Precision-Recall (PR) curve for model 6-13.

Fig. 3

Comparison results for dataset versions 1 and 2 using models 5, 10, and 11.

Fig. 4

ROC curve comparison for dataset versions 1 and 2 using models 5, 10, and 11.

Fig. 5

Precision-Recall curve comparison for dataset versions 1 and 2 using models 5, 10, and 11.

Fig. 6

GO Biological Processes: a. Top 10 terms with the lowest p-value; b. network of protein-GO Biological Processes, darker colors indicate the terms with a p-value of <0.01(enrichment terms explanation can be seen in Supplementary File 5). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7

GO Cellular Component: a. Top 10 terms with the lowest p-value; b. network of protein-GO Cellular Component, darker colors indicate the terms with a p-value of <0.01 (enrichment terms explanation can be seen in Supplementary File 5). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 8

GO Molecular Function: a. Top 10 terms with the lowest p-value; b. network of protein-GO Molecular Function, darker colors indicate the terms with a p-value of <0.01 (enrichment terms explanation can be seen in Supplementary File 5). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9

KEGG Pathway: a. Top 10 terms with the lowest p-value; b. network of protein-KEGG Pathway, darker colors indicate the terms with a p-value of <0.01 (enrichment terms explanation can be seen in Supplementary File 5). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 10

Topology of protein-peptide-enrichment network: round, triangular, and hexagonal shaped nodes indicate protein, peptide, and enrichment; the enrichment terms have a p-value of <0.01 (terms explanation can be seen in Supplementary File 1–5).