Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 356477 Protein Science and Glyciology 1 3.0 3 ★ KUO-YUAN HWA GOPM 2 0 英文授課

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356477	Protein Science and Glyciology	1	3.0	3	★	KUO-YUAN HWA	GOPM	2	0	英文授課

Course Plan：

Instructor KUO-YUAN HWA　Office Hours

Email kyhwa@ntut.edu.tw

Last Updated 2025-12-15 17:19:59

Course Objective 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.

Course Schedule 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%)

Evaluation and grading policy 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

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
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/

The access to curricular consultation 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).

The course corresponds to the SDGs 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）

Does the course incorporate 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）

Note
We will use Microsoft Team for the class.

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

Instructor	KUO-YUAN HWA　Office Hours
Email	kyhwa@ntut.edu.tw
Last Updated	2025-12-15 17:19:59
Course Objective	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.
Course Schedule	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%)
Evaluation and grading policy	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
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 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/
The access to curricular consultation	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).
The course corresponds to the SDGs	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）
Does the course incorporate 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）
Note	We will use Microsoft Team for the class. Register and download the software: https://www.microsoft.com/zh-tw/microsoft-teams/group-chat-software