[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117398-en":3,"doc-seo-117398-105":30,"detail-sidebar-cat-0-en-105":91},{"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},117398,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Advancing Machine Learning-Guided Frameworks for Engineering Complex Proteins","Proteins function as essential molecular machines that underpin immunity, signaling, and other life-sustaining processes, and their natural diversification is driven by long-term selection of beneficial mutations. This dissertation investigates machine learning-guided protein engineering to accelerate the creation of novel protein functions for biotechnology and medicine. It introduces low-N protein engineering for Cas proteins and validates an LSR-based screening platform for building Cas protein datasets for model benchmarking. It further improves serine recombinase engineering and proposes an end-to-end machine learning framework for engineering hyperactive combinatorial protein variants.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nAdvancing Machine Learning-Guided Frameworks for Engineering Complex Proteins  \nPermalink  \n[https://escholarship.org/uc/item/19g7d04b](https://escholarship.org/uc/item/19g7d04b)  \nAuthor  \nTran, Vincent Quy  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAdvancing Machine Learning-Guided Frameworks for Engineering Complex Proteins  \nBy  \nVincent Quy Tran  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nChemistry  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Patrick D. Hsu, Co-Chair  \nProfessor Matthew B. Francis, Co-Chair  \nProfessor Michelle C. Chang  \nProfessor David F. Savage  \nFall 2024  \nAdvancing Machine Learning-Guided Frameworks for Engineering Complex Proteins  \n© Copyright 2024  \nby  \nVincent Quy Tran  \nAbstract  \nAdvancing Machine Learning-Guided Frameworks for Engineering Complex Proteins  \nby  \nVincent Quy Tran  \nDoctor of Philosophy in Chemistry  \nUniversity of California, Berkeley  \nProfessor Patrick D. Hsu, Co-Chair  \nProfessor Matthew B. Francis, Co-Chair  \nIn biological systems, proteins serve as the fundamental molecular machines driving key processes required for the survival and health of the host organism. Proteins carry out a wide range of functions, from immune defense to cellular signaling. These functions evolved over millions of years, refined by the gradual selection and accumulation of beneficial mutations. Artificial evolution techniques are often used to accelerate the development of novel functions for applications across biotechnology and medicine. The emergence of machine learning-guided engineering approaches promises accelerated and improved development of proteins.  \nIn this dissertation, I explore the use of machine learning-guided engineering approaches for proteins and ultimately, develop a more efficient and effective machine learning-guided protein engineering approach. In Chapter 2, proof-of-concept work demonstrates the utility of a novel approach called low-N protein engineering for Cas proteins and the feasibility of a LSR-based screening platform to build comprehensive Cas protein datasets for machine learning model benchmarking. In Chapter 3, machine learning-guided engineering is applied to further improve upon existing engineering for a large serine recombinase. Finally, Chapter 4 presents a novel machine learning-guided, end-to-end framework for engineering hyperactive combinatorial protein variants. Together, this dissertation provides key insights towards applying machine learning to protein engineering, exemplifies the potential for machine learning to significantly enhance protein function, and displays a journey of learning how to be an effective protein engineer in the age of artificial intelligence.  \nTable of contents  \nAcknowledgments......................................................................................................... iv  \nChapter 1: Enabling efficient protein engineering with machine learning................ 1  \n1.1 Introduction............................................................................................................ 1  \n1.2 Traditional protein engineering approaches.......................................................... 1  \n1.3 Machine learning in protein engineering............................................................... 3  \n1.3.1 Developing the initial library.......................................................................... 3  \n1.3.2 Preparing the data........................................................................................ 4  \n1.3.3 Choosing, training, and evaluating a model.................................................. 6  \n1.4 Applications of Machine Learning in ","cbCailWUo3FR4GVD","https://ap.wps.com/l/cbCailWUo3FR4GVD","pdf",20024023,1,179,"English","en",105,"# Chapter 1: Enabling efficient protein engineering with machine learning\n## Introduction\n## Traditional protein engineering approaches\n## Machine learning in protein engineering\n## Applications of Machine Learning in Protein Engineering\n## Fundamental principles of machine learning-guided protein engineering\n## Context and scope of this dissertation\n# Chapter 2: Developing platforms for machine learning-guided engineering of CRISPR-Cas proteins\n## Abstract\n## Introduction\n## Results\n## Discussion\n## Experimental methods\n## Cell lines and culture\n## Plasmids and constructs","[{\"question\":\"本研究关注的核心问题是什么？\",\"answer\":\"研究聚焦于如何使用机器学习引导蛋白质工程，以提升效率与效果，并最终形成更有效的机器学习驱动蛋白设计方法。\"},{\"question\":\"第2章研究了哪些平台与验证内容？\",\"answer\":\"第2章展示一种用于Cas蛋白的low-N蛋白工程概念，并验证基于LSR的筛选平台，可构建全面的Cas蛋白数据集用于机器学习模型基准测试。\"},{\"question\":\"第4章提出了什么样的端到端框架？\",\"answer\":\"第4章提出一种新型的机器学习引导端到端框架，用于工程化高活性的组合（combinatorial）蛋白变体。\"}]","Advancing Machine Learning-Guided Frameworks for Engineering Complex Proteins | 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