查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-2 356477 蛋白質醣類學特論 1 3.0 3 ★ 華國媛高分所 2 0 　

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	356477	蛋白質醣類學特論	1	3.0	3	★	華國媛	高分所	2	0

教學大綱與進度：

教師姓名華國媛　教師諮商時間(Office Hours)

Email kyhwa@ntut.edu.tw

最後更新時間 2025-12-15 17:19:59

課程大綱 The course is designed to teach upper level undergraduate students and graduate students about the recent advances in protein science and glycobiology with specific application in medical and biotechnological industries. This course provides a comprehensive understanding of proteomics, emphasizing the revolutionary impact of AlphaFold on protein structure prediction. Students will explore proteomics techniques, data analysis, and practical applications, with hands-on experience using AlphaFold. Course Goals includes: 1. Understand the principles of proteomics and protein biochemistry. 2. Gain proficiency in experimental and computational proteomics techniques. 3. Explore the functionalities and applications of AlphaFold in predicting protein structures. 4. Apply knowledge to real-world biological problems.

課程進度 Week 1: Introduction to Proteins and Proteomics- Nobel 2024 Week 2: Protein Biochemistry I Week 3: Protein Biochemistry II Week 4: Hierarchical structure of proteins I Week 5: Hierarchical structure of proteins II Week 6: Protein domains and motifs. Week 7: Functional roles of proteins: Enzymes, transport, signaling, and structural proteins. Week 8: Introduction to AlphaFold Week 9: Midterm (30%) Week 10: AlphaFold: History and development (key achievements) Week 11: Overview of machine learning in structural biology Week 12: Limitations of AlphaFold Week 13: Unix Basic, PDB database, Structural analysis tools Week 14 Training data and methods I. Week 15: EMBL platform introduction Week 16-17: Training data and methods II. Week 18: Final exam (30%)

評量方式與標準 Evaluation: Homework (20%), Mid-term (30 %), Final case report (30%), Class attendance (20 %) ------------------------------------------------------- Mid-term presentation: PPT file Final exam is a summary report on using AlphaFold for protein structure prediction

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Varki A, Cummings RD, Esko JD, et al., editors. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2022. Website: https://www.ncbi.nlm.nih.gov/books/NBK579918/

課程諮詢管道 Students are welcome during my office hour
星期三(Wed):12:00 ~ 13:00
星期四(Thu): 09:00 ~ 12:00
備　註(Note) email:kyhwa@mail.ntut.edu.tw
聯絡電話 (ext): 2419
Students can also use the Line or TEAM platform (online platform).

課程對應SDGs指標 SDG3：良好健康和福祉（Good Health and Well-being）
SDG4：優質教育（Quality Education）
SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

備註
We will use Microsoft Team for the class.

Register and download the software:
https://www.microsoft.com/zh-tw/microsoft-teams/group-chat-software

教師姓名	華國媛　教師諮商時間(Office Hours)
Email	kyhwa@ntut.edu.tw
最後更新時間	2025-12-15 17:19:59
課程大綱	The course is designed to teach upper level undergraduate students and graduate students about the recent advances in protein science and glycobiology with specific application in medical and biotechnological industries. This course provides a comprehensive understanding of proteomics, emphasizing the revolutionary impact of AlphaFold on protein structure prediction. Students will explore proteomics techniques, data analysis, and practical applications, with hands-on experience using AlphaFold. Course Goals includes: 1. Understand the principles of proteomics and protein biochemistry. 2. Gain proficiency in experimental and computational proteomics techniques. 3. Explore the functionalities and applications of AlphaFold in predicting protein structures. 4. Apply knowledge to real-world biological problems.
課程進度	Week 1: Introduction to Proteins and Proteomics- Nobel 2024 Week 2: Protein Biochemistry I Week 3: Protein Biochemistry II Week 4: Hierarchical structure of proteins I Week 5: Hierarchical structure of proteins II Week 6: Protein domains and motifs. Week 7: Functional roles of proteins: Enzymes, transport, signaling, and structural proteins. Week 8: Introduction to AlphaFold Week 9: Midterm (30%) Week 10: AlphaFold: History and development (key achievements) Week 11: Overview of machine learning in structural biology Week 12: Limitations of AlphaFold Week 13: Unix Basic, PDB database, Structural analysis tools Week 14 Training data and methods I. Week 15: EMBL platform introduction Week 16-17: Training data and methods II. Week 18: Final exam (30%)
評量方式與標準	Evaluation: Homework (20%), Mid-term (30 %), Final case report (30%), Class attendance (20 %) ------------------------------------------------------- Mid-term presentation: PPT file Final exam is a summary report on using AlphaFold for protein structure prediction
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Varki A, Cummings RD, Esko JD, et al., editors. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2022. Website: https://www.ncbi.nlm.nih.gov/books/NBK579918/
課程諮詢管道	Students are welcome during my office hour 星期三(Wed):12:00 ~ 13:00 星期四(Thu): 09:00 ~ 12:00 備　註(Note) email:kyhwa@mail.ntut.edu.tw 聯絡電話 (ext): 2419 Students can also use the Line or TEAM platform (online platform).
課程對應SDGs指標	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
備註	We will use Microsoft Team for the class. Register and download the software: https://www.microsoft.com/zh-tw/microsoft-teams/group-chat-software