[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122863-en":3,"doc-seo-122863-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122863,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Molecular Modeling and Machine Learning for Protein Design - Dissertation","The dissertation develops and evaluates computational approaches for protein design by integrating molecular modeling with machine learning. It presents an assessment of protein engineering thermostability prediction tools using an independently generated dataset, analyzes how structural plasticity supports evolution and innovation of RuBisCO assemblies, and introduces a conditional generation framework for paired antibody chain sequences using an encoder-decoder language model. Across chapters, methods, results, and discussions connect model performance to biological relevance and guide future protein function and design workflows.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nMolecular Modeling and Machine Learning for Protein Design  \nPermalink  \n[https://escholarship.org/uc/item/15v2w04j](https://escholarship.org/uc/item/15v2w04j)  \nAuthor  \nChu, Kit Sang  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMolecular Modeling and Machine Learning for Protein Design  \nBy  \nSIMON KIT SANG CHU  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nin  \nBIOPHYSICS  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \n\n| Justin B. Siegel, Chair |\n| --- |\n| Vladimir Yarov-Yarovoy |\n\nPhilipp Zerbe Committee in Charge 2023  \nTo Emptiness  \nii  \nContents  \nAcknowledgments v  \nIntroduction vi  \nChapter 1 . Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset 1  \n1.1. Abstract 1  \n1.2. Introduction 1  \n1.3. Methods 3  \n1.4. Results 7  \n1.5. Discussion 12  \nChapter 2 . Structural Plasticity Enables Evolution and Innovation of RuBisCO Assemblies 14  \n2.1. Abstract 14  \n2.2. Introduction 14  \n2.3. Results 16  \n2.4. Discussion 27  \n2.5. Materials and Methods 30  \n2.6. Acknowledgments 36  \nChapter 3 . Conditional Generation of Paired Antibody Chain Sequences through Encoder-Decoder Language Model 38  \n3.1. Abstract 38  \n3.2. Introduction 38  \n3.3. Related Work 40  \n3.4. Method 41  \n3.5. Results 43  \n3.6. Discussion 50  \n3.7. Conclusion 51  \nAppendix A. Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset 52  \nA.1 . Supplementary Materials 52  \nAppendix B. Structural Plasticity Enables Evolution and Innovation of RuBisCO Assemblies 56  \nB.1 . Supplementary Materials 56  \nAppendix C. Conditional Generation of Paired Antibody Chain Sequences through Encoder-Decoder Language Model 67  \nC.1 . Supplementary Materials 67  \nC.2 . Sequence Clustering 86  \nBibliography 96  \nAcknowledgments  \nActions are worth more than a thousand words. While I wish to repay the kindness shown tome, I am left to express my gratitude in this way.  \nThroughout my five-year Ph.D. journey, Justin provided me with opportunities and immense patience for my personal and professional growth. His guidance illuminated a path for me at multiple crucial junctions in my life. I am grateful for his generosity, kind heart and dedication to supporting personal growth of his students.  \nI am also grateful for the companionship of Siegel lab members and colleagues in Biophysics program. Their technical knowledge and friendship were a beacon during my time in Davis. Even as we move on, I hope our hearts remain open to one another in fond memory.  \nLastly, my family’s love and support is a constant in my life and for that I am eternally grateful.  \nIntroduction  \nHumans have long possessed knowledge about the functions of proteins, even before the discovery of their sequences and structures. This ancient understanding is evident in various applications such as food, brewing, drugs, and more. However, it was not until the 20th century that biochemistry advanced significantly due to a deeper understanding of protein sequences and structures.  \nA widely accepted notion in the field is that a protein’s structure dictates its function. The precise orientation of amino acids, predetermined by the protein sequence, controls the biochemical properties of the protein. The study of protein function through sequence, structure, and biomolecular chemistry is known as protein function prediction, and the inverse problem in which the sequence is optimized for a target function and/or structure is named protein design.  \nThe history of protein evolution is encoded in sequences, and this information can be extracted through conservation analysis. Position-specific scoring matrices (PSSMs) can ","cbCaigsJWDhgeJ8w","https://ap.wps.com/l/cbCaigsJWDhgeJ8w","pdf",44785074,1,124,"English","en",105,"# Introduction\n## Protein function prediction and protein design\n## Molecular modeling with Rosetta\n## End-to-end learning and protein language models\n# Chapter 1. Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset\n## Abstract\n## Methods\n## Results\n## Discussion\n# Chapter 2. Structural Plasticity Enables Evolution and Innovation of RuBisCO Assemblies\n## Abstract\n## Results\n## Discussion\n## Materials and Methods\n# Chapter 3. Conditional Generation of Paired Antibody Chain Sequences through Encoder-Decoder Language Model\n## Abstract\n## Method\n## Results\n## Discussion\n# Appendices and Supplementary Materials","[{\"question\":\"What problem does the dissertation address in protein design?\",\"answer\":\"It targets how to computationally sample and navigate protein sequence and structure space, enabling protein engineering and design by predicting or generating biologically relevant properties and sequences.\"},{\"question\":\"How is thermostability prediction evaluated in the dissertation?\",\"answer\":\"Chapter 1 evaluates thermostability prediction tools using an independently generated dataset, with methods, results, and discussion focused on tool performance for protein engineering outcomes.\"},{\"question\":\"What model is used to generate paired antibody chain sequences?\",\"answer\":\"Chapter 3 proposes conditional generation of paired antibody chain sequences through an encoder-decoder language model, reporting methods, results, and discussion for the generated sequence pairs.\"}]","Molecular Modeling and Machine Learning for Protein Design - Dissertation | PDF",1785813410,312,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"molecular-modeling-and-machine-learning-for-protein-design-dissertation","",{"@graph":36,"@context":86},[37,54,69],{"@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/molecular-modeling-and-machine-learning-for-protein-design-dissertation/122863/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the dissertation address in protein design?","Question",{"text":76,"@type":77},"It targets how to computationally sample and navigate protein sequence and structure space, enabling protein engineering and design by predicting or generating biologically relevant properties and sequences.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is thermostability prediction evaluated in the dissertation?",{"text":81,"@type":77},"Chapter 1 evaluates thermostability prediction tools using an independently generated dataset, with methods, results, and discussion focused on tool performance for protein engineering outcomes.",{"name":83,"@type":74,"acceptedAnswer":84},"What model is used to generate paired antibody chain sequences?",{"text":85,"@type":77},"Chapter 3 proposes conditional generation of paired antibody chain sequences through an encoder-decoder language model, reporting methods, results, and discussion for the generated sequence pairs.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]