Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures
Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/22/7/1119 |
| _version_ | 1818478122319740928 |
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| author | Fan-Rong Meng Zhu-Hong You Xing Chen Yong Zhou Ji-Yong An |
| author_facet | Fan-Rong Meng Zhu-Hong You Xing Chen Yong Zhou Ji-Yong An |
| author_sort | Fan-Rong Meng |
| collection | DOAJ |
| description | Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks. |
| first_indexed | 2024-12-10T09:44:21Z |
| format | Article |
| id | doaj.art-f5d8f0dd153b4c51a004b6e05164a524 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-10T09:44:21Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-f5d8f0dd153b4c51a004b6e05164a5242022-12-22T01:53:54ZengMDPI AGMolecules1420-30492017-07-01227111910.3390/molecules22071119molecules22071119Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical StructuresFan-Rong Meng0Zhu-Hong You1Xing Chen2Yong Zhou3Ji-Yong An4School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, ChinaXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 21116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, ChinaKnowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.https://www.mdpi.com/1420-3049/22/7/1119DTIRVMBIGPPCA |
| spellingShingle | Fan-Rong Meng Zhu-Hong You Xing Chen Yong Zhou Ji-Yong An Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures Molecules DTI RVM BIGP PCA |
| title | Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures |
| title_full | Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures |
| title_fullStr | Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures |
| title_full_unstemmed | Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures |
| title_short | Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures |
| title_sort | prediction of drug target interaction networks from the integration of protein sequences and drug chemical structures |
| topic | DTI RVM BIGP PCA |
| url | https://www.mdpi.com/1420-3049/22/7/1119 |
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