Structural Bioinformatics and Molecular Modeling
A.Y. 2023/2024
Learning objectives
The purpose of this course is that participants gain knowledge on and understand:
- the computational analysis of the principal physicochemical and structural properties which influence the recognition between pharmacological targets and biotechnological drugs and products;
- the accuracy and the applicability domain 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;
- the methods to predict and validate the mechanism of action of biotechnological drugs and products, with particular attention to rational design of studies on animal models, according to the 3Rs principle.
- the computational analysis of the principal physicochemical and structural properties which influence the recognition between pharmacological targets and biotechnological drugs and products;
- the accuracy and the applicability domain 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;
- the methods to predict and validate the mechanism of action of biotechnological drugs and products, with particular attention to rational design of studies 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;
- the capacity to clearly communicate scientific results from in silico studies
to reach lifelong learning skills such as:
- a multifaceted computational knowledgebase, useful for further student's personal study of this topic.
- the capacity to work in the framework of academic or nonacademic institutions actively participating in multidisciplinary projects
- 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;
- the capacity to clearly communicate scientific results from in silico studies
to reach lifelong learning skills such as:
- a multifaceted computational knowledgebase, useful for further student's personal study of this topic.
- the capacity to work in the framework of academic or nonacademic institutions actively participating in multidisciplinary projects
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:
1. Essential biology
2. Essential informatics
3. Essential statistics
4. Biological evolution
5. Sequence alignments
6. Phylogenetic trees
7. Next-gen sequencing
8. Genome assembly and annotation
9. Bioinformatic approaches for protein sequence analysis
10. Protein strucutres
11. Protein interactions
12. Bioinformatics laboratory (1 ECTS)
The teaching unit of "Molecular modeling: basic methodologies"
1. Introduction to Quantum Mechanics:
a. The Schrödinger equation
b. The wave function
c. The Hamiltonian operator
d. The main approximations in the QM calculation (BO, HF, LCAO)
e. The set of bases
f. The SCF cycle
g. The surface of potential energy and the optimization of geometry
j. QM methods in the calculation of molecular properties (ab-initio, semiempirical and DFT methods)
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. Elements of statistical thermodynamics
b. Simulated annealing
c. Umbrella sampling
d. Replica exchange MD
e. Metadynamics
f. 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)
The teaching unit of "Computational methodologies in biopharmaceutical development"
a) molecular descriptors: methods for their calculation and applications in physicochemical profiling;
(b) ligand-based approaches: QSAR and pharmacophore models;
(c) structure-based approaches: molecular docking, virtual screening and MD-based simulations;
(d) chemometric approaches and big data
1. Essential biology
2. Essential informatics
3. Essential statistics
4. Biological evolution
5. Sequence alignments
6. Phylogenetic trees
7. Next-gen sequencing
8. Genome assembly and annotation
9. Bioinformatic approaches for protein sequence analysis
10. Protein strucutres
11. Protein interactions
12. Bioinformatics laboratory (1 ECTS)
The teaching unit of "Molecular modeling: basic methodologies"
1. Introduction to Quantum Mechanics:
a. The Schrödinger equation
b. The wave function
c. The Hamiltonian operator
d. The main approximations in the QM calculation (BO, HF, LCAO)
e. The set of bases
f. The SCF cycle
g. The surface of potential energy and the optimization of geometry
j. QM methods in the calculation of molecular properties (ab-initio, semiempirical and DFT methods)
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. Elements of statistical thermodynamics
b. Simulated annealing
c. Umbrella sampling
d. Replica exchange MD
e. Metadynamics
f. 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)
The teaching unit of "Computational methodologies in biopharmaceutical development"
a) molecular descriptors: methods for their calculation and applications in physicochemical profiling;
(b) ligand-based approaches: QSAR and pharmacophore models;
(c) structure-based approaches: molecular docking, virtual screening and MD-based simulations;
(d) chemometric approaches and big data
Prerequisites for admission
To conveniently attend the lessons, the student should possess basic knowledge of computer science, mathematics, physics, organic chemistry, biochemistry and molecular biology
Teaching methods
The teaching units of "Molecular modeling: basic methodologies" and "Computational methodologies in biopharmaceutical development" involves 3 CFU (24 h) of frontal lessons in classroom, while the unit of Structural Bioinformatics is subdivided into 3 CFU (24 h) of frontal lessons plus 16 hours of practical training on computers (1 CFU).
Teaching Resources
For all units, the slides will be provided. To delve into the covered topics the following textbooks are recommended:
Manuela Helmer Citterich, Fabrizio Ferrè, Giulio Pavesi, Chiara Romualdi, Graziano Pesole. Fondamenti di bioinformatica. Biologia Zanichelli 2018
A. R. Leach, Molecular Modelling: Principles and Applications. Prentice Hall College Div 2001
K. A. Dill & S. Bromberg, Molecular Driving Forces, Statistical Thermodynamics in Chemistry and Biology. Garland Science, 2002
Manuela Helmer Citterich, Fabrizio Ferrè, Giulio Pavesi, Chiara Romualdi, Graziano Pesole. Fondamenti di bioinformatica. Biologia Zanichelli 2018
A. R. Leach, Molecular Modelling: Principles and Applications. Prentice Hall College Div 2001
K. A. Dill & S. Bromberg, Molecular Driving Forces, Statistical Thermodynamics in Chemistry and Biology. Garland Science, 2002
Assessment methods and Criteria
The exam is subdivided into two parts: the first practical part concerning only the structural bioinformatics unit and if the student reaches a positive mark in this first part, a second oral examination during which the student should demostrates his knowledge and understanding of the topics covered by all three units.
Molecular modeling in drug design and development
BIO/10 - BIOCHEMISTRY
CHIM/06 - ORGANIC CHEMISTRY
CHIM/08 - PHARMACEUTICAL CHEMISTRY
CHIM/06 - ORGANIC CHEMISTRY
CHIM/08 - PHARMACEUTICAL CHEMISTRY
Lessons: 24 hours
Professor:
Mazzolari Angelica
Molecular modeling methods: basic techniques
BIO/10 - BIOCHEMISTRY
CHIM/06 - ORGANIC CHEMISTRY
CHIM/08 - PHARMACEUTICAL CHEMISTRY
CHIM/06 - ORGANIC CHEMISTRY
CHIM/08 - PHARMACEUTICAL CHEMISTRY
Lessons: 24 hours
Professor:
Contini Alessandro
Structural Bioinformatics
BIO/10 - BIOCHEMISTRY
CHIM/06 - ORGANIC CHEMISTRY
CHIM/08 - PHARMACEUTICAL CHEMISTRY
CHIM/06 - ORGANIC CHEMISTRY
CHIM/08 - PHARMACEUTICAL CHEMISTRY
Single bench laboratory practical: 16 hours
Lessons: 24 hours
Lessons: 24 hours
Professor:
Eberini Ivano
Educational website(s)
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