Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and immune suppression, thus making it a potential targe...
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2024-12-01
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| author | Baerlike Wujieti Mingtian Hao Erxia Liu Luqi Zhou Huanchao Wang Yu Zhang Wei Cui Bozhen Chen |
| author_facet | Baerlike Wujieti Mingtian Hao Erxia Liu Luqi Zhou Huanchao Wang Yu Zhang Wei Cui Bozhen Chen |
| author_sort | Baerlike Wujieti |
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| description | The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and immune suppression, thus making it a potential target for cancer therapy. Initially, researchers sought to develop inhibitors targeting SHP2’s catalytic site (protein tyrosine phosphatase domain, PTP). Due to limitations such as conservativeness and poor membrane permeability, SHP2 was once considered a challenging drug target. Nevertheless, with the in-depth investigations into the conformational switch mechanism from SHP2’s inactive to active state and the emergence of various SHP2 allosteric inhibitors, new hope has been brought to this target. In this study, we investigated the interaction models of various allosteric inhibitors with SHP2 using molecular dynamics simulations. Meanwhile, we explored the free energy landscape of SHP2 activation using enhanced sampling technique (meta-dynamics simulations), which provides insights into its conformational changes and activation mechanism. Furthermore, to biophysically interpret high-dimensional simulation trajectories, we employed interpretable machine learning methods, specifically extreme gradient boosting (XGBoost) with Shapley additive explanations (SHAP), to comprehensively analyze the simulation data. This approach allowed us to identify and highlight key structural features driving SHP2 conformational dynamics and regulating the activity of the allosteric inhibitor. These studies not only enhance our understanding of SHP2’s conformational switch mechanism but also offer crucial insights for designing potent allosteric SHP2 inhibitors and addressing drug resistance issues. |
| format | Article |
| id | doaj-art-a835065c50044eada310018f83b91e34 |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-a835065c50044eada310018f83b91e342025-01-10T13:18:36ZengMDPI AGMolecules1420-30492024-12-013011410.3390/molecules30010014Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine LearningBaerlike Wujieti0Mingtian Hao1Erxia Liu2Luqi Zhou3Huanchao Wang4Yu Zhang5Wei Cui6Bozhen Chen7School of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaSchool of Chemical Sciences, University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, ChinaThe src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and immune suppression, thus making it a potential target for cancer therapy. Initially, researchers sought to develop inhibitors targeting SHP2’s catalytic site (protein tyrosine phosphatase domain, PTP). Due to limitations such as conservativeness and poor membrane permeability, SHP2 was once considered a challenging drug target. Nevertheless, with the in-depth investigations into the conformational switch mechanism from SHP2’s inactive to active state and the emergence of various SHP2 allosteric inhibitors, new hope has been brought to this target. In this study, we investigated the interaction models of various allosteric inhibitors with SHP2 using molecular dynamics simulations. Meanwhile, we explored the free energy landscape of SHP2 activation using enhanced sampling technique (meta-dynamics simulations), which provides insights into its conformational changes and activation mechanism. Furthermore, to biophysically interpret high-dimensional simulation trajectories, we employed interpretable machine learning methods, specifically extreme gradient boosting (XGBoost) with Shapley additive explanations (SHAP), to comprehensively analyze the simulation data. This approach allowed us to identify and highlight key structural features driving SHP2 conformational dynamics and regulating the activity of the allosteric inhibitor. These studies not only enhance our understanding of SHP2’s conformational switch mechanism but also offer crucial insights for designing potent allosteric SHP2 inhibitors and addressing drug resistance issues.https://www.mdpi.com/1420-3049/30/1/14molecular dockingmolecular dynamics simulationsMM/GBSA binding free energy calculationsenhanced samplingmachine learning |
| spellingShingle | Baerlike Wujieti Mingtian Hao Erxia Liu Luqi Zhou Huanchao Wang Yu Zhang Wei Cui Bozhen Chen Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning Molecules molecular docking molecular dynamics simulations MM/GBSA binding free energy calculations enhanced sampling machine learning |
| title | Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning |
| title_full | Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning |
| title_fullStr | Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning |
| title_full_unstemmed | Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning |
| title_short | Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning |
| title_sort | study on shp2 conformational transition and structural characterization of its high potency allosteric inhibitors by molecular dynamics simulations combined with machine learning |
| topic | molecular docking molecular dynamics simulations MM/GBSA binding free energy calculations enhanced sampling machine learning |
| url | https://www.mdpi.com/1420-3049/30/1/14 |
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