[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122296-en":3,"doc-seo-122296-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122296,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Development of automated computational methods for the redesign of protein dynamics using biomolecular simulations and machine learning - Dissertation - Doctor of Philosophy","Proteins govern nearly all biological mechanisms, with three-dimensional structures and dynamics determining their function. While protein design has advanced rapidly, redesigning protein dynamics to obtain desired properties or states remains difficult in computational settings. This dissertation closes the gap by presenting three toolkits—MDSubSampler, MDAutoMut, and MDAutoPredict—combining biomolecular simulations with machine learning to automate dynamic redesign workflows. The methods enable denoising and effective subsampling, mutation automation with impact assessment, and ML-based conformational state prediction. Results are demonstrated on adenylate kinase and benchmarked against prior literature.","Development of automated computational methods for the redesign of protein dynamics using biomolecular simulations and machine learning  \nDissertation  \nSubmitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy  \n(Ph.D)  \nof the  \nDepartment of Computer Science,  \nBrunel University London  \nSubmission date: 31 January 2025  \nAuthor:  \nNamir Oues  \nSupervisors:  \nDoctor Alessandro Pandini Doctor Sarath Dantu  \nRDA:  \nProfessor Yongmin Li  \nDeclaration  \nI declare that this thesis is my original work and has been completed solely by me, Namir Oues. The research contained within has not been submitted for any other degree or qualification award. Some parts of the work have been published previously and are explicitly acknowledged in the text where relevant. All material sources have been appropriately cited, and full references are provided.  \nPublications  \nThis is a list of publications lead-authored and co-authored by the author of this thesis during the PhD time frame:  \nOues N. , Dantu S.C. , Patel R.J. , Pandini A. , MDSubSampler: A posteriori sampling of important protein conformations from biomolecular simulations, Bioinformatics, Volume 39, Issue 7, July 2023, btad427, [https://doi.org/10.1093/bioinformatics/btad427](https://doi.org/10.1093/bioinformatics/btad427)  \n[Oues N.](Oues N.) , Pandini A. , MDAutoMut: A Toolkit for an automated workflow of redesigning protein dynamics through mutation engineering. Manuscript in preparation.  \nHossein Nezhad F. , Oues N. , Meli M. , Pandini A. , MDGraphEmb: A toolkit for encoding molecular dynamics simulations with graph embedding”. Manuscript under review with requested revisions.  \nAbstract  \nProteins are responsible for almost all biological mechanisms, and their three-dimensional structures and dynamics define their function. In recent years, outstanding advances have been made in protein design. However, redesigning protein dynamics to achieve desired properties or states remains a significant challenge in computational protein design. This thesis addresses this gap by introducing three novel toolkits—MDSubSampler, MDAutoMut, and MDAutoPredict—developed to integrate biomolecular simulations with machine learning for the automated redesign of protein dynamics.  \nMDSubSampler is designed to preprocess and a posteriori subsample molecular dynamics simulations, preserving critical dynamic information while reducing noise and data complexity. Its application demonstrates effective noise reduction and compatibility with machine learning workflows, validated using adenylate kinase as a model system. MDAutoMut automates mutation generation, simulation, and analysis, facilitating systematic identification of mutations that have a desired impact on protein dynamics. This toolkit successfully identifies mutations on adenylate kinase structure shifting dynamics towards a closed conformation, validated by literature benchmarks. MDAutoPredict extends the workflow by using machine learning models to predict conformational states from molecular dynamics data, offering an adaptable framework for dynamic state prediction.  \nThese contributions represent an advance in computational protein design, providing scalable, automated solutions for mutation engineering and dynamic prediction. The toolkits are modular, extensible, and integrated with well-consolidated libraries, ensuring broad applicability across protein engineering challenges. This research highlights the potential of combining biomolecular simulations with machine learning to redesign protein dynamics and sets the stage for future innovations in computational biology.  \nAcknowledgements  \nI would like to express my sincere gratitude to my supervisor, Dr. Alessandro Pandini, for giving me the opportunity to work on this project. I am deeply thankful for his continuous support, patience, and guidance throughout my PhD. His feedback and advice at every stage of the project were invaluable, and his encou","cbCaidC7RQxxM2PF","https://ap.wps.com/l/cbCaidC7RQxxM2PF","pdf",7458033,1,205,"English","en",105,"# Abstract\n# Toolkits and automated workflow\n## MDSubSampler\n## MDAutoMut\n## MDAutoPredict\n# Publications\n# Acknowledgements","[{\"question\":\"What problem does the dissertation address in computational protein design?\",\"answer\":\"It tackles the challenge of redesigning protein dynamics to reach desired properties or conformational states using computational methods.\"},{\"question\":\"What are the three main toolkits introduced in the thesis?\",\"answer\":\"The thesis presents MDSubSampler, MDAutoMut, and MDAutoPredict to integrate biomolecular simulations with machine learning for automated redesign of protein dynamics.\"},{\"question\":\"How does MDSubSampler contribute to the workflow?\",\"answer\":\"It preprocesses and performs a posteriori subsampling of molecular dynamics simulations to preserve critical dynamic information while reducing noise and data complexity.\"},{\"question\":\"How is mutation-based redesign automated in the thesis?\",\"answer\":\"MDAutoMut automates mutation generation, simulation, and analysis to systematically identify mutations that drive dynamics toward target protein states, demonstrated on adenylate kinase.\"}]","Development of automated computational methods for the redesign of protein dynamics using biomolecular simulations and machine learning - 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