Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches
Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed th...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022002744 |
| _version_ | 1797978218778591232 |
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| author | Sakshi Kamboj Akanksha Rajput Amber Rastogi Anamika Thakur Manoj Kumar |
| author_facet | Sakshi Kamboj Akanksha Rajput Amber Rastogi Anamika Thakur Manoj Kumar |
| author_sort | Sakshi Kamboj |
| collection | DOAJ |
| description | Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the “Anti-HCV” platform using machine learning and quantitative structure–activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson’s correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The “Anti-HCV” predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV. |
| first_indexed | 2024-04-11T05:19:30Z |
| format | Article |
| id | doaj.art-60ad1fc5a3a7470e8548daa50c2f6de3 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:30Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-60ad1fc5a3a7470e8548daa50c2f6de32022-12-24T04:53:15ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012034223438Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approachesSakshi Kamboj0Akanksha Rajput1Amber Rastogi2Anamika Thakur3Manoj Kumar4Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, IndiaVirology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, IndiaVirology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, IndiaVirology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, IndiaVirology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Corresponding author at: Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India.Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5–10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the “Anti-HCV” platform using machine learning and quantitative structure–activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson’s correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The “Anti-HCV” predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.http://www.sciencedirect.com/science/article/pii/S2001037022002744Hepatitis C VirusAntiviralPredictionDrug repurposingMachine learningQSAR |
| spellingShingle | Sakshi Kamboj Akanksha Rajput Amber Rastogi Anamika Thakur Manoj Kumar Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches Computational and Structural Biotechnology Journal Hepatitis C Virus Antiviral Prediction Drug repurposing Machine learning QSAR |
| title | Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches |
| title_full | Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches |
| title_fullStr | Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches |
| title_full_unstemmed | Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches |
| title_short | Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches |
| title_sort | targeting non structural proteins of hepatitis c virus for predicting repurposed drugs using qsar and machine learning approaches |
| topic | Hepatitis C Virus Antiviral Prediction Drug repurposing Machine learning QSAR |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022002744 |
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