Dataset Information

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.

SUBMITTER: Kusuma WA

PROVIDER: S-EPMC10637925 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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

Kusuma Wisnu Ananta WA Fadli Aulia A Fatriani Rizka R Sofyantoro Fajar F Yudha Donan Satria DS Lischer Kenny K Nuringtyas Tri Rini TR Putri Wahyu Aristyaning WA Purwestri Yekti Asih YA Swasono Respati Tri RT

Heliyon 20231026 11

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 Rando ...[more]

PMID: 37954374

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Project description:The Malayan pit viper (Calloselasma rhodostoma) is a hemotoxic snake widely found in Southeast Asia and is responsible for the majority of poisoning cases in this region, including Thailand. However, a comprehensive knowledge of the venom protein profile and classification, as well as novel venom proteins, of this viper is still limited. Recently, the detailed composition of several snake venoms has been discovered through the use of transcriptome analysis. Therefore, the aim of this study was to employ a next-generation sequencing platform and bioinformatics analysis to undertake venom-gland de novo transcriptomics of Malayan pit vipers. Furthermore, 21,272 functional coding genes were identified from 36,577 transcripts, of which 314 transcripts were identified as toxin proteins, accounting for 61.41% of total FPKM, which can be categorized into 22 toxin gene families. The most abundant are snake venom metalloproteinase kistomin (P0CB14) and zinc metalloproteinase/disintegrin (P30403), which account for 60.47% of total toxin FPKM and belong to the SVMP toxin family, followed by snake venom serine protease 1 (O13059) and Snaclec rhodocetin subunit beta (P81398), which account for 6.84% and 5.50% of total toxin FPKM and belong to the snake venom serine protease (SVSP) and Snaclec toxin family, respectively. Amino acid sequences of the aforementioned toxins were compared with those identified in other important medical hemotoxic snakes from Southeast Asia, including the Siamese Russell's viper (Daboia siamensis) and green pit viper (Trimeresurus albolabris), in order to analyze their protein homology. The results demonstrated that ranges of 58%-62%, 31%-60%, and 48%-59% identity was observed among the SVMP, Snaclec, and SVSP toxin families, respectively. Understanding the venom protein profile and classification is essential in interpreting clinical symptoms during human envenomation and developing potential therapeutic applications. Moreover, the variability of toxin families and amino acid sequences among related hemotoxic snakes found in this study suggests the use and development of universal antivenom for the treatment of envenomating patients is still challenging.

Dataset Information

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

Publications

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

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