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COMPUTATIONAL DRUG DISCOVERY
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COMPUTATIONAL DRUG DISCOVERY
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Anno accademico 2023/2024
- Codice dell'attività didattica
- BIO0220
- Docenti
- Giuseppe Ermondi (Titolare del corso)
Giulia Caron (Titolare del corso) - Corso di studi
- Laurea Triennale in Biotecnologie
- Anno
- 3° anno
- Periodo didattico
- Secondo semestre
- Tipologia
- A scelta dello studente
- Crediti/Valenza
- 4
- SSD dell'attività didattica
- CHIM/08 - chimica farmaceutica
- Modalità di erogazione
- Tradizionale
- Lingua di insegnamento
- Inglese
- Modalità di frequenza
- Obbligatoria
- Tipologia d'esame
- Orale
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Sommario insegnamento
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Obiettivi formativi
Providing students with the basic concepts and tools to manage the 3D structure of proteins in the perspective of drug design using Web-based and stand-alone tools
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Risultati dell'apprendimento attesi
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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Modalità di insegnamento
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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Modalità di verifica dell'apprendimento
Students are encouraged to participate, ask questions and maintain a direct relationship with the professor during the lesson.
In each lesson, they are involved in practical exercitation based on online or free tools that they can perform on their notebooks.
Finally, for each topic, students must submit a report that will be evaluated
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Attività di supporto
Classroom practice
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Programma
- 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
- Membranes in drug discovery
- 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
Testi consigliati e bibliografia
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Note
Readings and bibliography will be suggested during the lessons
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