C8700 - MOLECULAR STRUCTURAL BIOLOGY

Learning outcomes

At the end of the course the student knows the main experimental techniques used to solve the structure of biological macromolecules (X-ray diffraction, Nuclear Magnetic Resonance, Electron Microscopy). The student knows the issues related to the relationship between sequence, structure and function in proteins. She/he is able to retrieve and interpret the structural and functional information deposited in the databases (UniProt, PDB) and to visualize, analyse and compare macromolecular structures

Course contents

Prerequisites

Basic knowledge of biochemistry, molecular biology, and structural biology is recommended, as well as familiarity with the fundamental concepts of protein structure and function.

Course Topics

• Three-dimensional structure of biological macromolecules and principles of structural biology.
• Experimental techniques for determining macromolecular structures:
  • X-ray crystallography;
  • Nuclear Magnetic Resonance (NMR) spectroscopy;
  • Electron microscopy/cryo-electron microscopy (cryo-EM).
• Concepts of resolution, data quality, and structural interpretation.
• Protein Data Bank (PDB): database organization, file formats, and retrieval of structural information.
• UniProt: database organization and experimental and computational annotations.
• Bioinformatics tools for:
  • visualization of macromolecular structures;
  • secondary structure analysis;
  • solvent accessibility calculation;
  • molecular interaction analysis;
  • structural comparison of proteins.
• Relationships between protein sequence, structure, and function.
• Fundamentals of sequence alignment and functional inference.
• Functional annotation of proteins and Gene Ontology.

Practical Activities/Laboratory Sessions

Practical sessions involve the use of public databases and structural bioinformatics software to retrieve, visualize, compare, and interpret macromolecular structures through case studies of biological relevance.

Activities may include both individual and group exercises, as well as critical discussion of case studies.

Readings/Bibliography

Recommended Textbooks

• James P. Allen, Biophysical Chemistry, Wiley.
• Jenny Gu and Philip E. Bourne, Structural Bioinformatics, Wiley.

Teaching Materials

Lecture slides, scientific articles, exercises, and additional teaching materials will be made available through the University's Virtuale platform.

Additional readings may be suggested during the course to provide further insight into specific topics.

Teaching methods

The course combines lectures with practical sessions designed to help students achieve the intended learning outcomes.

Practical activities involve the use of public databases (e.g., UniProt and the Protein Data Bank) and software for the visualization and analysis of macromolecular structures.

Guided individual practical exercises are included.

Case studies from the scientific literature may be used to develop students' critical thinking and data interpretation skills.

Assessment methods

Assessment is based on the completion of a project and an oral examination.

The project is designed to evaluate the student's ability to retrieve, analyze, and interpret structural and functional data using biological databases and bioinformatics tools.

The oral examination assesses:

• knowledge of the course topics;
• the ability to relate protein sequence, structure, and function;
• the ability to interpret experimental and structural data;
• the appropriate use of scientific terminology.

The final grade takes into account both the quality of the project and the oral examination.

Assessment Criteria

• 18–21: basic knowledge of the course topics and understanding of the fundamental concepts.
• 22–25: good knowledge of the course contents and ability to apply the acquired concepts.
• 26–29: thorough knowledge of the subject, independent analytical skills, and appropriate use of scientific terminology.
• 30–30 with honours: comprehensive mastery of the course topics, strong critical thinking skills, ability to establish interdisciplinary connections, and excellent scientific communication.

Use of Generative Artificial Intelligence

The project may include a declared use of generative Artificial Intelligence, provided that it is accompanied by a mandatory critical evaluation of the generated results.

Teaching tools

• Teaching materials available through the Virtuale platform.
• Lecture slides.
• Links to public databases (UniProt and the Protein Data Bank).
• Software for molecular structure visualization and analysis.
• Datasets, guided exercises, and selected scientific articles, where appropriate.

The use of generative Artificial Intelligence tools is allowed as support for individual study (e.g., summarization, explanations, self-assessment, or further exploration), in accordance with the guidelines established for course assessment.

Students with Specific Learning Disorders (SLD) or temporary or permanent disabilities are encouraged to contact the University's dedicated office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en). The office will propose any necessary accommodations, which must be submitted to the instructor for approval at least 15 days before the examination. The instructor will evaluate the appropriateness of such accommodations in relation to the intended learning outcomes.

Students who have been granted working student status should consult the dedicated University web page (https://www.unibo.it/it/studiare/guida-alla-scelta-del-corso/conciliare-studio-e-lavoro) to apply for this status and learn about the available support measures.

Office hours

See the website of Emidio Capriotti

See the website of Maria Paola Turina