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COMPUTATIONAL DRUG DISCOVERY
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COMPUTATIONAL DRUG DISCOVERY
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Academic year 2024/2025
- Course ID
- BIO0220 Pds GEN
- Teachers
- Giulia Caron
Giuseppe Ermondi - Degree course
- [0101M22] Molecular Biotechnology
- Year
- 2nd year
- Teaching period
- Second semester
- Type
- Elective
- Credits/Recognition
- 4
- Course disciplinary sector (SSD)
- CHIM/08 - pharmaceutical chemistry
- Delivery
- Formal authority
- Language
- English
- Attendance
- Obligatory
- Type of examination
- Oral
- Type of learning unit
- corso
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Sommario del corso
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Course objectives
The course will contribute to the educational aims of the Molecular Biotechnology course. It will provide students with the skills to use a selection of in silico tools to address important issues in drug discovery programmes.
The course is organised in two sections both of which are essential in Drug Discovery programs:
- the first illustrates the use of web-based and stand-alone tools to manipulate the 3D structure of proteins;
- the second focuses on in silico methods to model molecular permeability across cell membranes.
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Results of learning outcomes
KNOWLEDGE AND UNDERSTANDING
Acquisition of theoretical skills concerning the use of advanced in-silico techniques used in the drug discovery process
APPLYING KNOWLEDGE AND UNDERSTANDING
Development of the ability to incorporate common in-silico methods and information provided by various internet sources into daily research work
MAKING JUDGEMENTS
Critical evaluation of the information obtained from the use of in-silico techniques
COMMUNICATION SKILLS
Acquisition of oral and written communication of results as well as the ability to use graphical language
LEARNING SKILLS
Acquisition of autonomous learning capacity and self-assessment of its preparation
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Program
- 3D protein structures
- Visualization of 3D structures
- Retrieving 3D structure from PDB and AlphaFold
- Evaluation of the reliability of the structures
- Dynamics of proteins studied using available web tools
- Building of the 3D structure of mutated proteins and prediction of their effects
- Permeability-related computational tools
- Permeability basic concepts
- 2D molecular descriptors to predict permeability
- PerMM to madel translocation pathways
- The impact of conformational variability on permeability
- 3D molecular descriptors to predict permeability
- Application to cyclic peptides
- 3D protein structures
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Course delivery
The lessons will be given in form of frontal and practical lessons in which students will be asked to test software and webservers discussed during the frontal lessons
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Learning assessment methods
Students are encouraged to engage actively, ask questions, and maintain direct communication with the professor during lessons. This is especially important because each session involves practical exercises that require the use of various free software on their laptops. The data generated from these exercises must be presented in either infographics or report format, which will be evaluated. The final grade is the average of the grades of the individual infographics/reports. For those who wish, the grade can be improved with an oral test.
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Support activities
Classroom practice
Suggested readings and bibliography
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Notes
Readings and bibliography will be suggested during the lessons
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