[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123833-en":3,"doc-seo-123833-105":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},123833,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Accelerating the Biotechnology Revolution with Machine Learning - Guided Protein Engineering - Dissertation","Protein engineering relies on introducing mutations that reshape folded structure and function, yet traditional directed-evolution pipelines remain laborious and create bottlenecks that limit broad biotechnology adoption. Machine-learning-guided protein engineering is positioned as a transformative path, requiring both computational advances and experimental validation. The dissertation develops 3D-CNN and protein large language model approaches for predicting mutational effects and stability changes, then validates ML-designed mutations through PET bioremediation, COVID-19 diagnostic-enabling polymerase stabilization, and ML-guided active-site engineering via protein-ligand complex generation.","Copyright by  \nDaniel Jesus Diaz 2023  \n1  \nThe Dissertation Committee for Daniel Jesus Diaz Certifies that this is the approved  \nversion of the following Dissertation:  \nAccelerating the Biotechnology Revolution with Machine Learning  \nGuided Protein Engineering  \nCommittee:  \nEric Anslyn, Supervisor  \nAndrew Ellington, Co-Supervisor  \nAdam Klivans  \nClaus Wilke  \nEdward Marcotte  \nGraeme Henkelman  \nAccelerating the Biotechnology Revolution with Machine Learning  \nGuided Protein Engineering  \nby  \nDaniel Jesus Diaz  \nDissertation  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDoctor of Philosophy  \nThe University of Texas at Austin  \nMay 2023  \nDedication  \nI would like to dedicate this dissertation to my cat Smokes OG. One step closer to getting you that diamond chain Smokes! And to my wife, Maritza Sirven Diaz, you are the reason I have made it this far. Thank you.  \nAcknowledgements  \nI would first like to thank Andy Ellington and Eric Anslyn for letting me pursue my interests and giving me tremendous amount of intellectual freedom throughout my PhD. Without this freedom, I would have never come into my own and discovered the niche Iam very passionate about.  \nI would also like to acknowledge several of my peers that I have worked with throughout my graduate degree. Raghav Shroff was a great mentor to me, and his work enabled me to have a successful PhD. Simon d’Oelsnitz was both an amazing lab mate and friend. His passion for engineering proteins and having an impact on the bioeconomy matches mine and I love working with him on protein engineering projects. James Loy for teaching me how to be software engineer and dedicating so much time and effort to AI protein engineering with me.  \nI would like to thank Maritza Sirven Diaz, I came to graduate school only because you pushed me, and I am only at the point of graduating because of you. You are the reason I passed my entrance exam, qualifying exam, and now completing my dissertation. They say behind every great man there is an even greater women and, even from San Francisco, I couldn’t agree more. Thank you.  \nAbstract  \nAccelerating the Biotechnology Revolution with Machine Learning  \nGuided Protein Engineering  \nDaniel Jesus Diaz, PhD  \nThe University of Texas at Austin, 2023  \nSupervisors: Andrew Ellington, Eric Anslyn  \nAn extremely important task in biotechnology is the ability to engineer proteins by introducing mutations into their sequences, which ultimately alters their folded structure and function. In nature, this process occurs via random mutation and selection, also known as evolution. Protein engineers have learned to limit the randomness and “direct” evolution, but this process is still too laborious and bottlenecks the application of biotechnology across all sectors of society.  \nMachine learning (ML) guided protein engineering has the potential to revolutionize the development of protein-based biotechnology and enabling this future is the underlying theme of this thesis. To make meaningful advancements and enable MLguided protein engineering both computational advancement and experimental validation are required. This dissertation presents studies that explore the capabilities of ML frameworks to protein data and experimental validation of structure-based ML frameworks.  \nThe first computational study examines the mutational landscape of proteins through the lens of 3D convolutional neural networks (3DCNNs) and evolution. The  \nsecond study explores how to leverage recent advancements made in protein large language models (pLLMs) for supervised learning on protein stability. In this study, a supervised dataset that uses organism growth temperatures as coarse-grained label is curated and several machine learning techniques invented by the natural language and computer vision community are applied to fine-tune the pLLM, ESM-1b, to predict changes in","cbCaii8X83ezjiqA","https://ap.wps.com/l/cbCaii8X83ezjiqA","pdf",32221333,1,322,"English","en",105,"# List of Tables\n## List of Figures\n# Chapter 1\n## Introduction\n## Supervised Learning\n## Unsupervised learning and zero-shot prediction","[{\"question\":\"为什么蛋白工程领域仍然存在效率瓶颈？\",\"answer\":\"尽管蛋白工程已经学会通过“定向进化”减少随机性，但该过程仍然过于费时且容易形成瓶颈，限制了生物技术在各行业的应用扩展。\"},{\"question\":\"本论文如何理解机器学习在蛋白工程中的作用？\",\"answer\":\"论文以“机器学习引导的蛋白工程”为核心主题，强调既需要计算方法的进展，也需要实验验证来证明模型生成或筛选结果的可用性。\"},{\"question\":\"论文在计算与实验两方面分别做了哪些工作？\",\"answer\":\"计算方面包含基于3D卷积神经网络的突变景观分析，以及利用蛋白大语言模型进行稳定性预测。实验方面通过MutCompute与MutComputeX等方法进行稳定突变验证，并将ML管线用于PET相关生物修复、低资源COVID-19诊断以及用于药物关键酶的活性位点工程。\"}]","Accelerating the Biotechnology Revolution with Machine Learning - Guided Protein Engineering - Dissertation | PDF",1785818798,811,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"accelerating-the-biotechnology-revolution-with-machine-learning-guided-protein-engineering-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/accelerating-the-biotechnology-revolution-with-machine-learning-guided-protein-engineering-dissertation/123833/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么蛋白工程领域仍然存在效率瓶颈？","Question",{"text":75,"@type":76},"尽管蛋白工程已经学会通过“定向进化”减少随机性，但该过程仍然过于费时且容易形成瓶颈，限制了生物技术在各行业的应用扩展。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"本论文如何理解机器学习在蛋白工程中的作用？",{"text":80,"@type":76},"论文以“机器学习引导的蛋白工程”为核心主题，强调既需要计算方法的进展，也需要实验验证来证明模型生成或筛选结果的可用性。",{"name":82,"@type":73,"acceptedAnswer":83},"论文在计算与实验两方面分别做了哪些工作？",{"text":84,"@type":76},"计算方面包含基于3D卷积神经网络的突变景观分析，以及利用蛋白大语言模型进行稳定性预测。实验方面通过MutCompute与MutComputeX等方法进行稳定突变验证，并将ML管线用于PET相关生物修复、低资源COVID-19诊断以及用于药物关键酶的活性位点工程。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]