Quantitative chemical structure and activity relationship
A.A. 2021/2022
Obiettivi formativi
The purpose of this course is that participants gain knowledge on and understand:
- the computational strategies for modelling targets, responsible for biological activity and toxicity, simulating both their interaction with xenobiotics or biotechnological products and their molecular recognition mechanisms at an atomistic level;
- methods to predict and validate the mechanism of action (MoA) of xenobiotics and biotechnological products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the principal physicochemical properties of xenobiotics of relevance to health risk assessment;
- the accuracy of in silico approaches used in scientific studies and risk assessment reports.
- the computational strategies for modelling targets, responsible for biological activity and toxicity, simulating both their interaction with xenobiotics or biotechnological products and their molecular recognition mechanisms at an atomistic level;
- methods to predict and validate the mechanism of action (MoA) of xenobiotics and biotechnological products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the principal physicochemical properties of xenobiotics of relevance to health risk assessment;
- the accuracy of in silico approaches used in scientific studies and risk assessment reports.
Risultati apprendimento attesi
At the end of the course, the student is expected to know:
- the application of the computational methods used in toxicological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used in risk assessment reports;
to gain:
- the bases for deeply understanding methods and results of a toxicological paper and/or data reports;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
- the application of the computational methods used in toxicological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used in risk assessment reports;
to gain:
- the bases for deeply understanding methods and results of a toxicological paper and/or data reports;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
Periodo: annuale
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
annuale
More specific information on the delivery modes of training activities for academic year 2021/22 will be provided over the coming months, based on the evolution of the public health situation.
Prerequisiti
The course requires the knowledge of base notions of mathematics, chemistry, biochemistry and molecular biology, which are needed for a proficient comprehension of the lessons.
Modalità di verifica dell’apprendimento e criteri di valutazione
The examination will include a short report based on a relevant paper from the technical literature to discuss how topics covered in class are addressed and applied in the paper for the In silico methods in toxicology module, and of an oral discussion about two different topics of the Structural bioinformatics module.
In Silico Methods in Toxicology
Programma
The module in silico methods in toxicology is aimed at providing students with the fundamental concepts of molecular modelling and a collection of representative in silico approaches playing an increasingly important role in toxicology. Advantages deriving from a complementary use of experimental and in silico techniques will be deeply discussed during the course.
1. Introductions and overview of chemoinformatics and computational toxicology
2. Chemical structure representation (SMILES, MOL, INChI, etc)
3. Structure fingerprints, pairwise similarity
4. Molecular and physicochemical properties
5. Chemical database searching and retrieval; exact, substructure, and similarity searches
6. Humble beginning of QSAR and computational tox endpoints
7. QSAR and Rules for Skin Sensitization and AOP; Skin Permeability
8. QSAR and Rules for Bacterial Reverse Mutagenicity
9. Fundamentals of Read-Across
10. Chemical Safety Assessment - Future perspectives, comp toxin the world of artificial intelligence
11. Discussion and course wrap up
1. Introductions and overview of chemoinformatics and computational toxicology
2. Chemical structure representation (SMILES, MOL, INChI, etc)
3. Structure fingerprints, pairwise similarity
4. Molecular and physicochemical properties
5. Chemical database searching and retrieval; exact, substructure, and similarity searches
6. Humble beginning of QSAR and computational tox endpoints
7. QSAR and Rules for Skin Sensitization and AOP; Skin Permeability
8. QSAR and Rules for Bacterial Reverse Mutagenicity
9. Fundamentals of Read-Across
10. Chemical Safety Assessment - Future perspectives, comp toxin the world of artificial intelligence
11. Discussion and course wrap up
Metodi didattici
Frontal lessons. Class time will also be used for hands-on exercises based on topics and concepts covered during that session.
Materiale di riferimento
No textbook is required for this module. The instructors will, however, provide a list of recommended books, papers, and other materials that students may wish to explore for more in-depth coverage of particular topics.
Structural Bioinformatics
Programma
The module Structural bioinformatics is aimed at depicting a landscape of the most up-to-date bioinformatics techniques for the study of the biopolymers (nucleic acids and proteins) that are the main targets of xenobiotics. Students will be introduced to the theoretical bases of the most used computational approaches (4 CFU). Bioinformatics laboratories, totalling 16 hours (1 CFU), will be provided together with lectures, and will be carried out by the Molecular Operating Environment (MOE, https://www.chemcomp.com/Products.htm) fully integrated drug discovery software package, kindly offered by the Chemical Computing Group (http://www.chemcomp.com/index.htm).
Frontal lessons (4 CFU)
1. Introduction to bioinformatics
2. Genome organization and evolution
3. Databases, archives and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database search and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading and ab initio approaches
7. Case studies: chimeric comparative modelling of a receptor/target; distant homology modelling of an enzyme
8. Xenobiotic and novel drug risk assessment and prioritization
9. Case study: evaluation of endocrine active substances through an efficient in silico pipeline
10. Introduction to systems biology
Bioinformatics laboratories (1 CFU)
1. Building small molecules, biopolymers and introducing their PTMs
2. Protein comparative modelling
3. Molecular docking
4. Quantitative structure-activity relationship in practice
Frontal lessons (4 CFU)
1. Introduction to bioinformatics
2. Genome organization and evolution
3. Databases, archives and information retrieval
4. Substitution matrices, pairwise and multiple alignments, database search and phylogenetic trees
5. Protein structure and architecture
6. Protein structure prediction and validation: comparative modelling, threading and ab initio approaches
7. Case studies: chimeric comparative modelling of a receptor/target; distant homology modelling of an enzyme
8. Xenobiotic and novel drug risk assessment and prioritization
9. Case study: evaluation of endocrine active substances through an efficient in silico pipeline
10. Introduction to systems biology
Bioinformatics laboratories (1 CFU)
1. Building small molecules, biopolymers and introducing their PTMs
2. Protein comparative modelling
3. Molecular docking
4. Quantitative structure-activity relationship in practice
Metodi didattici
Frontal lessons and practical laboratories to be carried out through computational software provided to students by teacher and tutor.
Materiale di riferimento
Arthur M. Lesk, Introduction to Bioinformatics, Fifth Edition. Oxford University Press 2019
Moduli o unità didattiche
In Silico Methods in Toxicology
CHIM/08 - CHIMICA FARMACEUTICA - CFU: 5
Lectures: 40 ore
Docente:
Escher Sylvia Emmi
Structural Bioinformatics
BIO/10 - BIOCHIMICA - CFU: 5
Laboratory individual activity: 16 ore
Lectures: 32 ore
Lectures: 32 ore
Docente:
Eberini Ivano
Docente/i
Ricevimento:
Lunedì, mercoledì e venerdì dalle 9 alle 10 e su richiesta previo messaggio via Microsoft Teams o email
Microsoft Teams