Computational Structural Bioinformatics

Summary

Despite being made up of only ~20 building blocks, proteins have an astounding diversity of shapes and molecular functions. This course is an introduction to Computational Structural Biology - computational methods for studying the link between the sequence, structure and function of biomolecules. After providing background on protein structures, we will present classical and recent algorithms for tackling major structural biology tasks, and demonstrate their applications. The objectives of the course are to:

• Study the scientific and algorithmic principles of key tools such as AlphaFold, learn how and when to use them and how to interpret their results.
• Deepen our knowledge of protein structures & function.
• Study some real-life applications of dynamic programming, deep learning & computer vision algorithms for solving biological problems.
• Apply the learned tools during a project on a chosen system of interest.

Syllabus

• Topic 1: Background on proteins & physics of protein folding and function.
• Topic 2: Visual analysis of protein structures (Representations of structures; ChimeraX).
• Topic 3: Protein Sequence Alignments (Pairwise & Multiple alignments; Substitution matrices; - Dynamic Programming).
• Topic 4: Protein Structural Alignments (Kabsch Algorithm; TM-Align; Order-independent alignments).
• Topic 5: Homology Search (BLAST/MMSEQS2, profile HMMs, FoldSeek).
• Topic 6: Prediction of Protein Structures (Homology modeling; Coevolution; AlphaFold1-2).
• Topic 7: Prediction of Biomolecular Interactions (Molecular Docking; AlphaFold3).
• Topic 8: Analysis of Protein Motion (Normal Mode Analysis; Molecular Dynamics).
• Topic 9: Protein Language Models & Prediction of Protein Function (GO Terms).
• Topic 10: Protein Design (Rosetta & Generative Models).
• Topic 11: RNA structure prediction & analysis.
• Topic 12: Selected research topics by the lecturers.

Course link

Course number: 0368.3105