The course aims to introduce students to the fundamental concepts and computational tools used in the study of the three-dimensional structure of biological macromolecules (proteins, nucleic acids, and their complexes). Through theoretical lessons and practical activities, the course provides an overview of the main computational and experimental techniques for the determination, visualization, analysis, and prediction of molecular structures and their interactions.

By the end of the course, students will be able to:

• Understand the structural organization of biological macromolecules and the relationship between structure and function;

• Use databases and software tools for the analysis, visualization, and modeling of three-dimensional structures;

• Apply methodologies for homology modeling, structure prediction, and molecular docking;

• Evaluate the quality of structural models and critically discuss the results obtained;

• Develop basic computational projects in the field of biotechnology.

1. Fundamentals of Structural Biology

• Biological macromolecules: proteins, DNA, and RNA

• Structural levels: primary, secondary, tertiary, and quaternary

• Relationship between structure and function

• Overview of main experimental techniques: X-ray crystallography, NMR spectroscopy, cryo-electron microscopy

2. Structural Databases

• Protein Data Bank (PDB) and associated portals (RCSB)

• Structural file formats (PDB)

• Navigation, extraction, and interpretation of structural data

• Validation and quality assessment of experimental structures

3. Molecular Visualization

• Software tools (VMD, Maestro)

• Basic operations: loading, selection, representations, coloring, measurements

• Identification of structural elements (helices, sheets, loops, domains)

4. Molecular Interactions

• Hydrogen bonds, hydrophobic interactions, salt bridges, van der Waals forces

• Active sites, functional pockets, and intra-/inter-chain molecular interactions

• Examples of protein–ligand and protein–nucleic acid interactions

5. Homology Modeling

• Sequence alignment and template selection

• Principles of structure-based modeling

• Tools: SWISS-MODEL, MODELLER

• Model quality assessment (e.g., Ramachandran plot)

6. AI-Based Structure Prediction

• Deep learning in structure prediction (AlphaFold)

• Interpretation of results (confidence score, pLDDT, PAE)

• Applications and limitations of predicted models

7. Elements of Statistical Thermodynamics and Molecular Dynamics

• Basic simulation concepts: conformational space, minimization, equilibrium

• Forces and parameters: force fields, explicit vs implicit solvent

• Brief introduction to tools: GROMACS, NAMD (conceptual overview only)

• Qualitative analysis of trajectories: RMSD, local flexibility (RMSF), conserved interactions

None

Knowledge and Understanding. The student will acquire foundational knowledge of the structural principles of biological macromolecules and the computational techniques used to analyze them.
They will understand the functioning of key software tools for the visualization, modeling, and analysis of three-dimensional structures.

Applied Knowledge and Understanding. The student will be able to apply theoretical concepts to solve basic structural problems, such as homology modeling, structure prediction, and molecular dynamics studies.
They will be capable of using open-source computational tools to perform simple practical tasks in the field of biotechnology.

Making Judgements. The student will develop the ability to critically evaluate the quality of structural models and the results obtained from computational simulations or predictions.
They will be able to identify the limitations, uncertainties, and potential of the different techniques employed.

Communication Skills. The student will be able to clearly and accurately describe the concepts and methodologies learned, both in written and oral form.
They will be able to present the results of a computational project using appropriate technical language, supported by molecular visualizations.

Learning Skills. The student will develop the skills necessary to independently explore more advanced bioinformatics tools and resources.
They will acquire a working methodology applicable to future activities in computational structural biology, bioinformatics, or molecular design.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

None

Teaching

Frontal Lessons

Attendance

Attendance is strongly encouraged yet not mandatory

Course books

For the exam:

• Slides and course work provided

Extra/For Consultation:

• Leach, Molecular Modelling: Principles and Applications, Pearson

Assessment

Oral Exam. The exam consists of open-ended questions on topics covered in the course, with particular attention to the student's ability to explain fundamental concepts of structural biology, describe the use of computational tools, and interpret real or simulated structural data.

Grade Interpretation

27/30 – 30/30 cum laude The student demonstrates an in-depth understanding of the relationship between the three-dimensional structure and biological function of macromolecules.
They are able to critically analyze and discuss structural data obtained either experimentally or computationally, using appropriate terminology.
They accurately describe the use of computational tools for visualization, modeling, and quality assessment of structures.
They coherently argue the biological implications of specific structural features (e.g., pockets, domains, interactions).
They show independence and mastery in integrating theoretical and practical knowledge.

24/30 – 26/30 The student shows a good understanding of the fundamental concepts of computational structural biology and the main tools for structural analysis.
They correctly use technical terminology and can interpret structural data, although with some uncertainty regarding more complex details.
They understand and can describe the essential steps of homology modeling and the use of AI-predicted models.
They are able to comment on structural examples covered in class and relate structural features to biological function.

21/30 – 23/30 The student has a basic knowledge of the main concepts covered in the course, though gaps in terminological or methodological accuracy are evident.
They can describe the structural levels of macromolecules and have a general understanding of the role of structure in biological function.
They are able to use or describe basic computational tools, though with limited or partial autonomy.
They can recognize and comment on structures and functions discussed during lessons, but not always clearly or consistently.

18/30 – 20/30 The student demonstrates a partial or superficial understanding of the course topics.
Fundamental concepts regarding macromolecular structure and computational tools are present but fragmented or unclear.
Their ability to perform structural analysis is very limited, and descriptions are generic or inaccurate.
Difficulties emerge in using technical terminology and in relating structure to biological function.
Interpretation of data or molecular visualizations appears mechanical or lacking in awareness.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

No difference for non-attending students.

Attendance

Attendance is strongly encouraged yet not mandatory.

Course books

No difference for non-attending students.

Assessment

No difference for non-attending students.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.