VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

636-0125-00L 4 Credits DR , MSC D-BSSE , D-INFK
You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.

Protein Design

Lecturers & Examiners: Prof. Dr. Basile Wicky
VVZ CR n/a

Last Updated: 2026-06-01 11:30:44

Abstract

This course explores the principles and methods of protein design and structure prediction, spanning foundational concepts, historical physics-based techniques, and modern deep learning approaches.

Objective

By the end of this course, students will have developed an intuition for fundamental concepts in protein structure prediction and design while exploring core historical approaches. Students will gain familiarity with deep learning techniques used in protein design and learn to apply state-of-the-art methods.

Content

The course is a mix of lectures and code-based exercise sessions. Students wil gain both a theoretical foundation and practical insights into state-of-the-art tools and methods used for protein design. We will begin by reviewing some fundamental concepts of protein structures and folding, followed by a description of early approaches to structure prediction based on energy methods like Rosetta. We will then cover landmark results in protein design, from initial manual strategies to early computational approaches, and progress through key milestones in pre-deep learning protein design, including enzymes and nanomaterials. A major focus is placed on deep learning’s transformative role in protein design. We will study breakthroughs like AlphaFold, and RoseTTAFold for structure prediction and learn how these tools were repurposed for protein design. Current deep learning methods for sequence design, such as ProteinMPNN, will be introduced alongside approaches for backbone generation using diffusion models and inpainting techniques. Finally, we will cover fundamentals of protein language models and their applications to de novo protein design.

Resources

Lecture Notes

Lecture slides will be made available on Moodle.

Literature

Primary literature will be made available on Moodle.

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
None

Course Components

Type Title Time & Place Hours
lecture with exercise Protein Design
This lecture will take place in classroom in BASEL. Attention: the lecture will start in the second week of the semester.
  • Wed 08:15-11:00 (BSS E 23)
3 h semesterly

Offered In

  • Computational Biology and Bioinformatics Master (Weitere Informationen: )
    • Kernfächer (Die Liste der Kernfächer ist eine geschlossene Liste - es können keine anderen Kurse in dieser Kategorie hinzugefügt werden. Die Zuordnung der Kurse zu der jeweiligen Unterkategorie kann nicht geändert werden. Studierende müssen mindestens einen Kurs pro Unterkategorie bestehen. Insgesamt müssen 40 ECTS Kernfächer erworben werden, einschliesslich des obligatorischen CBB-Seminars.)
  • Doktorat Biosysteme (Mehr Informationen unter: Für Kurse der Kategorie "Integration in die wissenschaftliche Gemeinschaft" bitte die BSSE Webseite konsultieren: )
  • Biotechnologie Master (Weitere Informationen: )
    • Wahlfächer (Offene Liste - weitere Kurse (ETH oder UNIBAS) können nach Absegnung durch den:die Mentor:in als Wahlfächer gewählt werden.)
    • Vertiefungsfächer (Die Liste der Vertiefungsfächer ist eine geschlossene Liste - es können keine anderen Kurse in dieser Kategorie hinzugefügt werden.)