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Kusuma WA, Fadli A, Fatriani R, Sofyantoro F, Yudha DS, Lischer K, Nuringtyas TR, Putri WA, Purwestri YA, Swasono RT. Prediction of the interaction between Calloselasma rhodostoma venom-derived peptides and cancer-associated hub proteins: A computational study. Heliyon 2023; 9:e21149. [PMID: 37954374 PMCID: PMC10637925 DOI: 10.1016/j.heliyon.2023.e21149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/04/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
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.
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Affiliation(s)
- Wisnu Ananta Kusuma
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, 16680, Indonesia
- Tropical Biopharmaca Research Center, IPB University, Bogor, 16128, Indonesia
| | - Aulia Fadli
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, 16680, Indonesia
| | - Rizka Fatriani
- Tropical Biopharmaca Research Center, IPB University, Bogor, 16128, Indonesia
| | - Fajar Sofyantoro
- Faculty of Biology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | - Donan Satria Yudha
- Faculty of Biology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | - Kenny Lischer
- Faculty of Engineering, University of Indonesia, Jakarta, 16424, Indonesia
| | - Tri Rini Nuringtyas
- Faculty of Biology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
- Research Center for Biotechnology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | | | - Yekti Asih Purwestri
- Faculty of Biology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
- Research Center for Biotechnology, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
| | - Respati Tri Swasono
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia
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Kusuma WA, Habibi ZI, Amir MF, Fadli A, Khotimah H, Dewanto V, Heryanto R. Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm. Front Pharmacol 2022; 13:978741. [PMID: 36034833 PMCID: PMC9403330 DOI: 10.3389/fphar.2022.978741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 07/14/2022] [Indexed: 11/26/2022] Open
Abstract
Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug–target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain Angelica Sinensis, Citrus aurantium, Glycyrrhiza uralensis, and Mangifera indica. This approach is expected to be an alternative way to discover the Jamu formula more accurately.
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Affiliation(s)
- Wisnu Ananta Kusuma
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
- Tropical Biopharmaca Research Center, IPB University, Bogor, Indonesia
- *Correspondence: Wisnu Ananta Kusuma,
| | - Zulfahmi Ibnu Habibi
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Muhammad Fahmi Amir
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Aulia Fadli
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Husnul Khotimah
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Vektor Dewanto
- Department of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
| | - Rudi Heryanto
- Tropical Biopharmaca Research Center, IPB University, Bogor, Indonesia
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, IPB University, Bogor, Indonesia
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