B5847 - MOLECULAR MODELING AND SIMULATION

Learning outcomes

The successful student is provided with theoretical fundaments and computational tools suitable to understand biological processes at the molecular level. At the end of the hands-on lab students are able to solve simple molecular modeling problems and to perform and analyze basic molecular dynamics simulations of biologically relevant systems.

Course contents

General concepts:

• Introduction to computational modeling and molecular simulation methods

• Review of statistical mechanics: microstates and macrostates, statistical entropy, the Boltzmann distribution, thermal energy

• Applications of statistical mechanics to biomolecules: conformational transitions, simplified models of protein folding, folding pathways

• Theoretical foundations of biomolecular modeling and simulation

Biomolecular modeling and simulation: theoretical aspects

• Representation of molecular structure and geometry: coordinate systems, potential energy surfaces, and molecular surfaces

• Force fields for molecular mechanics: bonded and non-bonded terms, parameterization procedures, general and specific force fields for biomolecules

• Molecular energy minimization methods

• Molecular dynamics simulations: integration of the equations of motion, construction of model systems, trajectory analysis through the calculation of simple energetic and structural properties

• Applications of machine learning techniques to force field development, trajectory analysis, and conformational sampling

Biomolecular modeling and simulation: hands-on lab

• Introduction to the use of the molecular visualization software VMD

• Introduction to the use of the molecular dynamics software package GROMACS

• Guided molecular dynamics exercises on small peptides and nucleic acids

• Analysis and visualization of simulation results using tools developed in the Jupyter Notebook environment

Readings/Bibliography

Ken Dill, Robert L. Jernigan, and Ivet Bahar. Protein Actions - Principles and Modeling. 1st edition, 2017, Garland Science, Taylor & Francis Group.

Teaching methods

Frontal lectures and training exercises on the computer.

In consideration of the type of activity and the teaching methods adopted, the attendance of this training activity requires the prior attendance of all students to the training Modules 1 and 2 on safety in the study places, in e-learning mode.

Assessment methods

Students will be evaluated based on:

1. a practical examination aimed at assessing the operational skills acquired during the course;
2. a written examination consisting of multiple-choice questions covering the topics included in the course syllabus.

• Students who are not satisfied with the grade obtained in the written examination may request to take an additional oral examination covering the entire course syllabus.

• Regarding assessment, the use of artificial intelligence (AI) is prohibited. Any use of AI constitutes a violation of academic integrity.

• Students with specific learning disorders (SLD) or temporary or permanent disabilities: students are encouraged to contact the relevant University office well in advance. The office will propose any necessary accommodations for the students concerned. Such accommodations must be submitted to the course instructor for approval at least 15 days in advance. The instructor will evaluate their appropriateness in relation to the learning objectives of the course.

Teaching tools

Electronic slides, scientific papers, and other teaching materials available through the platform Virtual Learning Environment.

Office hours

See the website of Matteo Masetti