Arquitectura e Modelação de Sistemas Macromoleculares

Objectives

This curricular unit introduces students to protein structure analysis through data-driven approaches. It covers fundamentals of macromolecular architecture, molecular visualization tools, and classical structure prediction methods such as homology modeling and ab initio techniques. Emphasis is given to advanced AI-based methods, including deep learning models like AlphaFold. Students will learn to access and interpret structural data from public databases, critically evaluate prediction tools, and integrate structural insights into biological research and engineering applications.

Syllabus

1. Revisions of the main fundamentals of Macromolecular Architecture 2. Protein Visualization and Analysis – Molecular graphics tools (PyMOL, Chimera) – 3D structure analysis 3. Databases and Structural Resources – Accessing and using the Protein Data Bank (PDB) – Sequence/structure alignment tools – UniProt integration 4. Classical Prediction Methods – Homology and Ab initio modeling – Threading and fold recognition 5. Artificial Intelligence in Structural Biology – machine learning basics – AlphaFold and state-of-the-art prediction tools – Models evaluation 6. Applications and Case Studies – Structural data in biomedical research and bioengineering – Engineering proteins with predicted structures – Ethical considerations 7. Practical Exercises – Hands-on sessions with visualization and modeling tools – Prediction workflows using public web servers and AI tools

Prerequisites

Fundamental knowledge of chemistry, molecular biology and biochemistry

Cross Competence Component

The cross-competence component focuses on developing transversal skills essential for bioengineering professionals. This includes critical thinking, problem-solving, teamwork, scientific communication, and ethical reasoning. Students engage in collaborative projects and discussions that foster interdisciplinary understanding and the ability to apply structural biology concepts within broader biological and engineering contexts. These competencies ensure that students will be prepared to adapt to evolving scientific challenges and collaborate effectively across diverse research and industrial environments.

Laboratorial Component

No laboratorial component

Programming And Computing Component

Students develop practical proficiency in molecular visualization using PyMOL and UCSF Chimera, perform sequence and structural analyses with tools such as BLAST and Clustal Omega, and execute computational modeling protocols employing MODELLER and Rosetta. The curriculum emphasizes the automation of structural prediction pipelines and the integration of advanced AI-driven platforms, including AlphaFold, for enhanced macromolecular modeling accuracy.

Ethical Principles

All members of a group are responsible for the group’s work In any assessment every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be alble to present and answer questions about the entire assignment and solution.