Bioinformatics and Molecular Modeling
A.Y. 2024/2025
Learning objectives
The purpose of this course is that participants gain knowledge on and understand:
- the prediction of the principal physicochemical and structural properties of pharmacological targets and of biotechnological drugs and products;
- the accuracy of in silico approaches used in the development of biotechnological drugs and products;
- the computational strategies for modelling targets, responsible for biological activity, simulating their interaction with biotechnological drugs and their molecular recognition mechanisms at an atomistic level;
methods to predict and validate the mechanism of action of biotechnological drugs and products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
- the prediction of the principal physicochemical and structural properties of pharmacological targets and of biotechnological drugs and products;
- the accuracy of in silico approaches used in the development of biotechnological drugs and products;
- the computational strategies for modelling targets, responsible for biological activity, simulating their interaction with biotechnological drugs and their molecular recognition mechanisms at an atomistic level;
methods to predict and validate the mechanism of action of biotechnological drugs and products, with particular attention to a better rational design of experiments on animal models, according to the 3Rs principle.
Expected learning outcomes
At the end of the course, the student is expected to know:
- the application of the computational methods used in biotechnological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used for developing biotechnological drugs and products;
to gain:
- the bases for deeply understanding computational methods and results described in scientific literature;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
- the application of the computational methods used in biotechnological research;
to critically evaluate:
- the pros and cons of in silico prediction approaches used for developing biotechnological drugs and products;
to gain:
- the bases for deeply understanding computational methods and results described in scientific literature;
to obtain:
a multifaceted bioinformatics knowledgebase, useful for further student's personal study of this topic.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The teaching unit of Structural Bioinformatics will provide an overview of bioinformatics techniques useful for dealing with the analysis of proteins, the main pharmacological targets and constituents of biotechnological drugs, introducing students to the logic and theoretical bases on which the most used computational approaches are based (3 CFU).
The teaching units of "Molecular modeling: basic methodologies" (2 CFU) and "Computational methodologies in biopharmaceutical development" (3 CFU) aim to provide the student with the knowledge necessary to understand the theoretical foundations and the fields in which computational chemistry applied to drug design can find valid applications both in the academic and industrial fields, so as to learn to critically evaluate the quality of the results and the limits of these methodologies.
Structural bioinformatics
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. Introduction to systems biology
Molecular modeling: basic methodologies
Molecular mechanics and dynamics
1. Elements of statistical thermodynamics
2. Introduction to Molecular Mechanics
a. The force fields
b. Solvent models and periodic conditions
c. Geometry optimization
3. Conformational search (CS)
a. Systematic CS methods
b. Stochastic CS methods
4. Molecular Dynamics (MD)
a. The equations of motion and the calculation of a trajectory
b. The microcanonical (NVE), canonical (NVT) and isothermal-isobaric (NPT) ensembles
c. trajectory analysis (energy profiles, RMSD, RMSF, geometrical parameters, hydrogen-bond, cluster analysis, principal component analysis)
d. Applications and limits of MD
5. Enhanced sampling techniques in MD simulations
a. Simulated annealing
b. Umbrella sampling
c. Replica exchange MD
d. Metadynamics
e. Accelerated MD
6. Calculation of free energy in complex systems
a. The potential of mean force (PMF)
b. Alchemical perturbations (Free Energy Perturbation and Thermodynamic Integration)
c. End-point methods (LIE and MM-PBSA)
Computational methodologies in biopharmaceutical development
1. Introduction to Chemoinformatics
a. Computer-Aided Drug Design (CADD)
b. Ligand-based Vs structure-based approaches
c. Scientific models
d. Computers: typology, hardware, Software
2. Molecule representation on computers
a. Molecular graph
b. Connection tables
c. Structure-based line notations
3. Molecular descriptors
a. Definition and classification
b. Fingerprints
c. Physicochemical, Geometrical and electronic descriptors
4. Quantitative Structure-Activity Relationship (QSAR)
a. History and development
b. Traditional regression QSAR models
c. Elements of Machine Learning and Random Forest Algorithm
d. Classification QSAR models
5. Molecular docking simulations
a. Search algorithms
b. Scoring functions
6. Virtual Screening
The teaching units of "Molecular modeling: basic methodologies" (2 CFU) and "Computational methodologies in biopharmaceutical development" (3 CFU) aim to provide the student with the knowledge necessary to understand the theoretical foundations and the fields in which computational chemistry applied to drug design can find valid applications both in the academic and industrial fields, so as to learn to critically evaluate the quality of the results and the limits of these methodologies.
Structural bioinformatics
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. Introduction to systems biology
Molecular modeling: basic methodologies
Molecular mechanics and dynamics
1. Elements of statistical thermodynamics
2. Introduction to Molecular Mechanics
a. The force fields
b. Solvent models and periodic conditions
c. Geometry optimization
3. Conformational search (CS)
a. Systematic CS methods
b. Stochastic CS methods
4. Molecular Dynamics (MD)
a. The equations of motion and the calculation of a trajectory
b. The microcanonical (NVE), canonical (NVT) and isothermal-isobaric (NPT) ensembles
c. trajectory analysis (energy profiles, RMSD, RMSF, geometrical parameters, hydrogen-bond, cluster analysis, principal component analysis)
d. Applications and limits of MD
5. Enhanced sampling techniques in MD simulations
a. Simulated annealing
b. Umbrella sampling
c. Replica exchange MD
d. Metadynamics
e. Accelerated MD
6. Calculation of free energy in complex systems
a. The potential of mean force (PMF)
b. Alchemical perturbations (Free Energy Perturbation and Thermodynamic Integration)
c. End-point methods (LIE and MM-PBSA)
Computational methodologies in biopharmaceutical development
1. Introduction to Chemoinformatics
a. Computer-Aided Drug Design (CADD)
b. Ligand-based Vs structure-based approaches
c. Scientific models
d. Computers: typology, hardware, Software
2. Molecule representation on computers
a. Molecular graph
b. Connection tables
c. Structure-based line notations
3. Molecular descriptors
a. Definition and classification
b. Fingerprints
c. Physicochemical, Geometrical and electronic descriptors
4. Quantitative Structure-Activity Relationship (QSAR)
a. History and development
b. Traditional regression QSAR models
c. Elements of Machine Learning and Random Forest Algorithm
d. Classification QSAR models
5. Molecular docking simulations
a. Search algorithms
b. Scoring functions
6. Virtual Screening
Prerequisites for admission
It is a prerequisite to have gained at least:
3 ECTS of informatics
3 ECTS of mathematics and physics
4 ECTS of organic chemistry
6 ECTS in biochemistry, molecular biology or clinical biochemistry
3 ECTS of informatics
3 ECTS of mathematics and physics
4 ECTS of organic chemistry
6 ECTS in biochemistry, molecular biology or clinical biochemistry
Teaching methods
Frontal lessons.
Teaching Resources
Arthur M. Lesk, Introduction to Bioinformatics, Fifth Edition. Oxford University Press 2019.
Slides will be provided by the teacher after each lesson, and published on ARIEL web site.
Slides will be provided by the teacher after each lesson, and published on ARIEL web site.
Assessment methods and Criteria
The exam will be divided into two parts: an initial practical test and, if the test has a positive result, an oral test based on the verification of the comprehension and elaboration of the program carried out in class.
BIO/10 - BIOCHEMISTRY - University credits: 3
CHIM/06 - ORGANIC CHEMISTRY - University credits: 2
CHIM/08 - PHARMACEUTICAL CHEMISTRY - University credits: 3
CHIM/06 - ORGANIC CHEMISTRY - University credits: 2
CHIM/08 - PHARMACEUTICAL CHEMISTRY - University credits: 3
Single bench laboratory practical: 16 hours
Lessons: 56 hours
Lessons: 56 hours
Professor(s)
Reception:
On Mondays, Wednesdays and Fridays from 9 to 10 am and on appointment previously taken via Microsoft Teams or email
Microsoft Teams