Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning
Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disorder marked by cerebellar dysfunction, ataxic gait, and progressive motor impairments. SCA2 is caused by the pathologic expansion of CAG repeats in the ataxin-2 (<i>ATXN2</i>) gene, leading to a toxic gai...
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2025-05-01
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| author | Smita Sahay Jingran Wen Daniel R. Scoles Anton Simeonov Thomas S. Dexheimer Ajit Jadhav Stephen C. Kales Hongmao Sun Stefan M. Pulst Julio C. Facelli David E. Jones |
| author_facet | Smita Sahay Jingran Wen Daniel R. Scoles Anton Simeonov Thomas S. Dexheimer Ajit Jadhav Stephen C. Kales Hongmao Sun Stefan M. Pulst Julio C. Facelli David E. Jones |
| author_sort | Smita Sahay |
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| description | Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disorder marked by cerebellar dysfunction, ataxic gait, and progressive motor impairments. SCA2 is caused by the pathologic expansion of CAG repeats in the ataxin-2 (<i>ATXN2</i>) gene, leading to a toxic gain-of-function mutation of the ataxin-2 protein. Currently, SCA2 therapeutic efforts are expanding beyond symptomatic relief to include disease-modifying approaches such as antisense oligonucleotides (ASOs), high-throughput screening (HTS) for small molecule inhibitors, and gene therapy aimed at reducing <i>ATXN2</i> expression. In the present study, data mining and machine learning techniques were employed to analyze HTS data and identify robust molecular properties of potential inhibitors of <i>ATXN2</i>. Three HTS datasets were selected for analysis: <i>ATXN2</i> gene expression, CMV promoter expression, and biochemical control (luciferase) gene expression. Compounds displaying significant <i>ATXN2</i> inhibition with minimal impact on control assays were deciphered based on effectiveness (E) values (<i>n</i> = 1321). Molecular descriptors associated with these compounds were calculated using MarvinSketch (<i>n</i> = 82). The molecular descriptor data (MD model) was analyzed separately from the experimentally determined screening data (S model) as well as together (MD-S model). Compounds were clustered based on structural similarity independently for the three models using the SimpleKMeans algorithm into the optimal number of clusters (<i>n</i> = 26). For each model, the maximum response assay values were analyzed, and E values and total rank values were applied. The S clusters were further subclustered, and the molecular properties of compounds in the top candidate subcluster were compared to those from the bottom candidate subcluster. Six compounds with high <i>ATXN2</i> inhibiting potential and 16 molecular descriptors were identified as significantly unique to those compounds (<i>p</i> < 0.05). These results are consistent with a quantitative HTS study that identified and validated similar small-molecule compounds, like cardiac glycosides, that reduce endogenous ATXN2 in a dose-dependent manner. Overall, these findings demonstrate that the integration of HTS analysis with data mining and machine learning is a promising approach for discovering chemical properties of candidate drugs for SCA2. |
| format | Article |
| id | doaj-art-3215876be4cc41a7aa2cbba632b01712 |
| institution | OA Journals |
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| publishDate | 2025-05-01 |
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| spelling | doaj-art-3215876be4cc41a7aa2cbba632b017122025-08-20T01:56:20ZengMDPI AGBiology2079-77372025-05-0114552210.3390/biology14050522Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine LearningSmita Sahay0Jingran Wen1Daniel R. Scoles2Anton Simeonov3Thomas S. Dexheimer4Ajit Jadhav5Stephen C. Kales6Hongmao Sun7Stefan M. Pulst8Julio C. Facelli9David E. Jones10Department of Neurosciences and Psychiatry, University of Toledo College of Medicine and Life Sciences, Toledo, OH 43606, USADepartment of Biomedical Informatics and Utah Clinical and Translational Science Institute, University of Utah Spencer Fox Eccles School of Medicine, Salt Lake City, UT 84115, USADepartment of Neurology, University of Utah Spencer Fox Eccles School of Medicine, Salt Lake City, UT 84115, USANational Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20892, USANational Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20892, USANational Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20892, USANational Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20892, USANational Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20892, USADepartment of Neurology, University of Utah Spencer Fox Eccles School of Medicine, Salt Lake City, UT 84115, USADepartment of Biomedical Informatics and Utah Clinical and Translational Science Institute, University of Utah Spencer Fox Eccles School of Medicine, Salt Lake City, UT 84115, USADepartment of Biomedical Informatics and Utah Clinical and Translational Science Institute, University of Utah Spencer Fox Eccles School of Medicine, Salt Lake City, UT 84115, USASpinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disorder marked by cerebellar dysfunction, ataxic gait, and progressive motor impairments. SCA2 is caused by the pathologic expansion of CAG repeats in the ataxin-2 (<i>ATXN2</i>) gene, leading to a toxic gain-of-function mutation of the ataxin-2 protein. Currently, SCA2 therapeutic efforts are expanding beyond symptomatic relief to include disease-modifying approaches such as antisense oligonucleotides (ASOs), high-throughput screening (HTS) for small molecule inhibitors, and gene therapy aimed at reducing <i>ATXN2</i> expression. In the present study, data mining and machine learning techniques were employed to analyze HTS data and identify robust molecular properties of potential inhibitors of <i>ATXN2</i>. Three HTS datasets were selected for analysis: <i>ATXN2</i> gene expression, CMV promoter expression, and biochemical control (luciferase) gene expression. Compounds displaying significant <i>ATXN2</i> inhibition with minimal impact on control assays were deciphered based on effectiveness (E) values (<i>n</i> = 1321). Molecular descriptors associated with these compounds were calculated using MarvinSketch (<i>n</i> = 82). The molecular descriptor data (MD model) was analyzed separately from the experimentally determined screening data (S model) as well as together (MD-S model). Compounds were clustered based on structural similarity independently for the three models using the SimpleKMeans algorithm into the optimal number of clusters (<i>n</i> = 26). For each model, the maximum response assay values were analyzed, and E values and total rank values were applied. The S clusters were further subclustered, and the molecular properties of compounds in the top candidate subcluster were compared to those from the bottom candidate subcluster. Six compounds with high <i>ATXN2</i> inhibiting potential and 16 molecular descriptors were identified as significantly unique to those compounds (<i>p</i> < 0.05). These results are consistent with a quantitative HTS study that identified and validated similar small-molecule compounds, like cardiac glycosides, that reduce endogenous ATXN2 in a dose-dependent manner. Overall, these findings demonstrate that the integration of HTS analysis with data mining and machine learning is a promising approach for discovering chemical properties of candidate drugs for SCA2.https://www.mdpi.com/2079-7737/14/5/522spinocerebellar ataxia type 2<i>ATXN2</i> genehigh-throughput screeningmachine learningsmall molecule drug discovery |
| spellingShingle | Smita Sahay Jingran Wen Daniel R. Scoles Anton Simeonov Thomas S. Dexheimer Ajit Jadhav Stephen C. Kales Hongmao Sun Stefan M. Pulst Julio C. Facelli David E. Jones Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning Biology spinocerebellar ataxia type 2 <i>ATXN2</i> gene high-throughput screening machine learning small molecule drug discovery |
| title | Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning |
| title_full | Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning |
| title_fullStr | Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning |
| title_full_unstemmed | Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning |
| title_short | Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning |
| title_sort | identifying molecular properties of ataxin 2 inhibitors for spinocerebellar ataxia type 2 utilizing high throughput screening and machine learning |
| topic | spinocerebellar ataxia type 2 <i>ATXN2</i> gene high-throughput screening machine learning small molecule drug discovery |
| url | https://www.mdpi.com/2079-7737/14/5/522 |
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