Universitat Internacional de Catalunya
Structural Bioinformatics
Other languages of instruction: Catalan, Spanish
Teaching staff
Questions will be answered before or after class. Questions that are not answered in person will be answered via videoconference.
Introduction
This course provides the fundamental concepts for the use of structural information in biomedical/clinical problems. It addresses the fundamental relationship between structure and function, analyzing the three-dimensional structures of proteins, DNA, and RNA. Students will learn how to process macromolecular structures both graphically and computationally. Different methodologies will be introduced for generating structural information, including cutting-edge approaches such as Artificial Intelligence algorithms. Finally, we will explore how structural bioinformatics can be applied to biomedical problems, particularly those related to the molecular understanding of hereditary diseases.
The course contributes to several United Nations Agenda 2030 Sustainable Development Goals (SDGs), such as SDG 3, 4, 9, and 17, by fostering competences in biomedicine such as health promotion, up-to-date bioinformatics tools, biomedical innovation, and collaborative scientific resources for knowledge exchange.
Pre-course requirements
It is recommended to have completed and passed:
- Introduction to bioinformatics
- Biomolecular interactions
Objectives
- Explain how the relationship between structure and function is the basis of applying structural bioinformatics methods in biomedicine.
- Provide the necessary tools to generate structural information: difference between extraction and prediction.
- Provide structural information to analyze genetic data and solve biomedical/clinical problems.
Competences/Learning outcomes of the degree programme
- CN14 - Identify the principles of biomedical sciences related to health, as well as the basic concepts and tools that have an impact on Biomedical Sciences and allow them to work in any of its fields (biomedical companies, bioinformatics labs, research laboratories, clinical analysis companies, etc.).
- CP05 - Apply biological foundations in the search for practical solutions to health problems, following ethical standards and scientific rigour and respecting fundamental equal rights between men and women, and the promotion of human rights and the values inherent in a peaceful society of democratic values that includes inclusive, non-discriminatory language without stereotypes.
Learning outcomes of the subject
Upon completing the course, students should be able to:
- Identify biomedical problems and aspects that require the use of structural information.
- Analyze how the structure of macromolecules, such as proteins, DNA, and RNA, influences their biological function, from stability to interactions in the context of genetic diseases.
- Apply techniques from the field of structural bioinformatics, such as structural information extraction, molecular modeling/prediction, database management/big data generation, etc.
- Analyze structural information of macromolecules, considering both experimental data and computational predictions to understand the molecular basis of hereditary diseases and predict the pathogenicity of genetic variants.
- Use a range of techniques to understand how sequence determines three-dimensional structure and how this structure relates to biological function.
- Develop integrated approaches to interpret genetic variants and predict their functional impact in clinical contexts, such as the diagnosis and treatment of genetic diseases.
Syllabus
This course introduces students to structural bioinformatics as a tool for understanding biomedical problems at the molecular level. Through theoretical classes and case-based practical sessions, students explore how protein structure relates to function and disease, particularly genetic diseases. Structural bioinformatics enables the analysis of experimental and predicted macromolecular structures to interpret genetic variants and their pathogenic potential. The course emphasizes the connection between sequence, structure, and function, combining molecular modelling, structure visualization, and computational analysis to develop practical skills for variant interpretation, protein analysis, and disease mechanism exploration.
Relevant questions: (i) how to determine which information level we want; (ii) how do we extract 3D info; (iii) how to conclude from what we have? (seeking consistency between different sources of evidence), particularly in the context of biomedical applications.
1.Introduction to Structural Bioinformatics
- What is structural bioinformatics? Why protein structure matters?
- Sequence-structure-function relationship
- Role of structural bioinformatics in biomedicine: examples from disease mechanism elucidation, target discovery and drug design, interpretation of genomic variants
2.Function at the sequence level: UniProt
- Overview of the four levels of protein structure: primary, secondary, tertiary, quaternary
- Exploring UniProt: the most important resource for general information on proteins.
- Tools & Databases: UniProt
3.Function at the sequence level: Domain databases
- Definitions and concepts: domains, motifs, folds, disordered and repetitive regions, functional significance of structural features
- Introducing domain databases
- Tools & Databases: Pfam, InterPro, Smart, PyMol
4.Protein structure databases: experimental methods
- The experimental understanding of structure
- Experimental methods and evaluation: Differences in resolution and reliability
- Tools & Databases: RCSB, PyMol
5.Visualization of Protein Structure
- Extracting information from protein structure, visual exploration and computational analyses
- PyMol basics, hands-on experience with disease-related proteins, different representations to understand the problem of interest
- Tools: PyMol
6.Structure to Function
- Residues and interactions: hydrogen bonds, hydrophobic interactions, active sites, binding pockets, interface residues in complexes
- How structural changes affect function and lead to disease
7.Sequence to function: pairwise sequence alignment
- Pairwise sequence alignment: similarity, identity, scoring matrices
- Tools & Databases: NCBI BLAST, Jalview
8.Sequence to function: multiple sequence alignment
- Multiple sequence alignment: why MSA is important in structure-function analysis
- Tools& Databases: Jalview, EBI, NCBI, PyMol
9.Protein structure prediction: Homology Modelling
- Homology/comparative modelling pipeline, quality evaluation
10.Structure comparison
- Comparing 3D structures: metrics and definitions
- Tools & Databases: DALI, CATH, SCOP, AlphaFold, PyMol
11.Protein Structure prediction: AlphaFold
- How does AlphaFold work?
- How to interpret structure prediction models
- Tools & Databases: PyMol, AlphaFold
12.Disordered Proteins
- What are intrinsically disordered proteins? Biological functions, challenges for structure identification and prediction
- Tools: PyMol
13.Mild introduction to artificial intelligence and machine learning
- What is machine learning? Introducing Supervised and unsupervised learning concepts
- How ML models use structural information to predict pathogenicity
- 4 main steps of building a supervised learning model: dataset collection, defining features, training the model, evaluation
14.Building your own ML model for variant impact prediction
- Applying 4 steps to build your own ML model and predicting its performance
- Tools & Databases: Python, Jupyter Notebook
15.Presentations
- Short project for students to present in the class: Summary of a relevant publication (introducing the problem, protein structure, structural bioinformatics studies, discussion points)
Teaching and learning activities
In person
Face-to-face classroom mode.
The contents will be taught using three methodologies or different learning activities:
2. Theory Lectures (CM) – 6 hours: the lecturer delivers knowledge to the whole group of students in a classroom.
2. Case Methods (MC) – 24 hours: students, in groups, solve clinical cases provided that day by the lecturer. In the classroom, students present their conclusions with active participation of the lecturer, who may introduce new concepts when necessary.
Evaluation systems and criteria
In person
First call:
• Case method resolution: 55%
• Final exam: 35%
• Presentation of a scientific article: 10%
Second or subsequent calls: same criteria as the first call. Continuous assessment and participation grades will be kept.
General rules to consider:
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To be eligible for averaging, a minimum grade of 5 is required in the final exam. In order to pass the course, students must obtain an overall minimum mark of 5.
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Attendance to case methods is mandatory. The continuous nature of this assessment means that it is not possible to pass the subject if at least 75% of the hours have not been attended.
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Class participation means contributing interesting ideas or raising relevant questions that help improve the quality of the session, whether in lectures or case methods.
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Attendance to lectures is not mandatory, but attendees must follow the rules indicated by the lecturers. If arriving late, enter quietly without disturbing the class. If attendance is below 65%, class participation will be graded very low.
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For the final exam (multiple choice):
a. −0.33 for each incorrect answer when there are 4 options (1 point for a correct one).
b. −0.25 for each incorrect answer when there are 5 options (1 point for a correct one).
Bibliography and resources
- Kessel, A., & Ben-Tal, N. (2018). Introduction to proteins: structure, function, and motion. Chapman and Hall/CRC.
- Xiong, J. (2006). Essential bioinformatics. Cambridge University Press
- Creighton, Thomas E. The biophysical chemistry of nucleic acids & proteins. Helvetian Press, 2010
- Bourne, P. E., & Weissig, H. (Eds.). (2010). Structural Bioinformatics (2nd ed.). Wiley-Liss.
Evaluation period
- E1 14/01/2026 A04 18:00h
- E2 17/06/2026 A10 18:00h