Quantitative Chemical Structure and activity relationship

A.Y. 2019/2020
Overall hours
BIO/10 CHIM/08
Learning objectives
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.
Expected learning outcomes
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.
Course syllabus and organization

Unique edition

Lesson period
First semester
Prerequisites for admission
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.
Assessement methods and criteria
The examination consists of a written test including questions related to all the topics developed during the In silico methods in toxicology unit, and of an oral discussion about two different topics of the Structural bioinformatics unit.
In Silico Methods in Toxicology
Course syllabus
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. Complementarity between in vitro, in vivo and in silico approaches in toxicology
2. Potential energy
3. Proteins as complex systems
4. Competitive and non-competitive inhibition
5. Binding affinity and equilibrium dissociation constant (Kd)
6. Introduction to molecular modelling
7. Hints of computational quantum mechanics
8. Molecular mechanics
9. Molecular docking
10. Pharmacodynamics of xenobiotics and evaluation of their toxic effects
11. The target (and off-target) problem
12. Methods to predict target proteins for toxic agents on a proteome-wide scale (Virtual screening of large protein databases)
13. Structure-based versus ligand-based toxicity prediction approaches
14. Species cross-transferability analysis; reduction and optimization of animal testing
Teaching methods
Frontal lessons.
References and all the needed materials will be provided to students by ARIEL.
Structural bioinformatics
Course syllabus
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, http://www.chemcomp.com/MOE-Molecular_Operating_Environment.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
Teaching methods
Frontal lessons and practical laboratories.
Arthur M. Lesk, Introduction to Bioinformatics, Fifth Edition. Oxford University Press 2019
In Silico Methods in Toxicology
CHIM/08 - PHARMACEUTICAL CHEMISTRY - University credits: 5
Lectures: 40 hours
Structural bioinformatics
BIO/10 - BIOCHEMISTRY - University credits: 5
Individual laboratory activities: 16 hours
Lectures: 32 hours
Professor: Eberini Ivano