Course Overview First week: Inmunology

This course provides general knowledge about how the human immune system works. It consists of studying innate and acquired immunity, and also the use of elements of this system for medical research with, among other things, monoclonal antibodies and vaccination. Physicochemical methods for the analysis of cells and molecules of the immune system will be presented as well as associated biotechnology techniques.

Learning Outcomes First week: Inmunology

1 - Acquire the basics needed to understand the immune system and the molecules involved in order to understand the human body's defense mechanisms against a pathogen.

2 - Know the analytical methods used in in vitro immunology, with the aim of putting them into practice by following an experimental protocol.

3 - Know the applications of immune molecules, such as monoclonal antibodies and immunotherapy.

4 - Ability to set up an experimental protocol using appropriate analytical tools and methods

Course Content First week: Inmunology

Course 1: Introduction

History of immunity
The immune response
Elements of the immune system

Course 2: The non specific immune response

Generalities

The skin

Non specific Immunity cells

Innate immunity receptors

Humoral factors of innate cells

Inflammation

Course 3: Lymphoid organs

Introduction
Primary lymphoid organs: Thymus and bone marrow
Secondary lymphoid organs: spleen, lymphatic nodes and MALT

Course 4 : The major histocompatibility complex

 Generalities

 Genomic level

 Structure of the MHC

                             

Course 5: Lymphoid cells

Introduction
T and B cells

Antigen processing

Supression oh the immune response

Course 6: Structure et fonctions of immunoglobulins

Introduction
General characteristics
The different classes of Ig
Biological functions of the constant part

Instructional Method First week: Inmunology

The course will be divided into 2 parts:

- Lecture during 15 hours

- Exercices during 6 hours

Required Course Materials First week: Inmunology

A personal computer

Assessment First week: Inmunology

First week: Inmunology

At the end of each course, students will be assessed by a quizz (wooclap or moodle) 50%

At the end of the session, they have to describe an experiment, and resolve the problem given. 50%

Course Overview Second week: Bioinformatics

The bioinformatics part of the course aims to develop skills in analyzing protein structures and molecule-target interactions. This involves handling protein databases and utilizing a range of tools for structural analysis, including molecular modeling tools such as Docking. These skills are highly applicable in various fields such as pharmacy, toxicology, environmental science, and more broadly, in biotechnological industries. This course will explore its application in immunology.

Learning Outcomes Second week: Bioinformatics

By the end of this course, students should be able to:

• Understand Protein Structures: Synthesize information to gain a comprehensive understanding of protein structures, employing methods for analysis, remediation, and minimization of structural issues.
• Apply Molecular Docking and Dynamics Principles: Utilize principles and practical applications of molecular docking techniques, alongside an introduction to molecular dynamics concepts, to predict and systematically study molecular interactions.
• Proficiently Utilize Protein Databases: Demonstrate proficiency in handling protein databases, analyzing PDB structure files, and conducting homology searches using tools such as BLAST and foldseek.
• Develop Modeling and Refinement Skills: Apply knowledge to add missing amino acids, refine protein structures, and minimize energy using force fields, demonstrating a detailed understanding of inter- and intramolecular interactions.
• Utilizing Bioinformatics Tools for protein structure prediction: A concise overview of software applications, such as Alphafold2, employing deep learning methods like D-ITASSER and C-ITASSER, alongside other computational tools used in predicting protein structures.
• Comprehend Virtual Screening Concepts: Evaluate techniques for high-throughput screening, apply ligand-based and structure-based approaches, and filter compound libraries for potential drug candidates in virtual screening concepts for drug discovery.
• Apply Computational Techniques in Drug Development: Apply computational methods to study drug-target interactions, reduce experimental trials, predict stable molecular complexes, and utilize virtual screening in identifying potential drug leads.
• Introduction to Molecular Dynamics Simulation: Gain foundational knowledge and understanding of molecular dynamics simulations, emphasizing theoretical concepts and their application in studying molecular interactions and stability over time.

These outcomes focus on providing students with an introductory understanding of molecular dynamics simulations, aligning with the course structure that includes theoretical learning without hands-on practice in this specific area.

Course Content Second week: Bioinformatics

This course is designed to cover essential topics in computational techniques for protein structure analysis across various scientific domains. The following modules will be addressed:

• Module 1: Fundamentals of Protein Structures

Introduction to protein structures and their significance in biological systems.

• Module 2: Protein Databases and Analysis

Introduction to protein databases, such as the Protein Data Bank (PDB).

Analysis and interpretation of PDB structure files.

Practical applications and exercises using protein databases.

Methods for protein structure analysis, including identification, rectification, and minimization of structural irregularities.

Addressing missing amino acids, refining structures, and energy minimization techniques.

Understanding force fields and inter- as well as intramolecular interactions.

• Module 3: Molecular Docking Techniques

Principles and applications of molecular docking.

Utilizing bioinformatics software for predicting molecular orientations.

Determining the most stable molecular complexes through docking simulations.

• Module 4: Homology Searches and Computational Prediction Tools

Conducting searches for homologous proteins using tools like BLAST, Fasta, and foldseek.

Introduction to computational prediction tools, including alphafold2 and deep learning methodologies.

Predicting protein structures and understanding sequence alignment concepts.

• Module 5: Virtual Screening Concepts in Drug Discovery

Techniques and methodologies for virtual screening in drug discovery.

High-throughput screening methods.

Ligand-based and structure-based approaches for identifying potential drug candidates.

Filtering compound libraries and practical applications in drug development.

• Module 6: Molecular Dynamics Simulation

Brief overview and significance of molecular dynamics simulations in protein analysis.

Theoretical understanding of fundamental concepts behind molecular dynamics.

Explanation of the role of molecular dynamics in confirming and studying molecular interactions.

Theoretical discussions on assessing the stability of molecular interactions over time using simulations.

Please note that this module will not include hands-on practice or in-depth practical sessions in molecular dynamics simulations. Instead, it focuses primarily on providing students with foundational understanding and theoretical knowledge of the principles behind molecular dynamics simulations in the context of protein structure analysis.

Instructional Method Second week: Bioinformatics

The course employs a multifaceted instructional approach comprising theoretical lectures and hands-on practical application. The learning process begins with comprehensive theoretical explanations, followed by immersive practical sessions (travaux pratiques) to reinforce the acquired skills.

The practical exercises encompass various facets:

• Utilization of Protein Data: Interactive exercises demonstrating how to navigate and extract insights from protein data to identify potential active sites within a protein structure.
• Handling Missing Residues: Engaging exercises focused on identifying and rectifying missing residues in protein structures, emphasizing methods for addition and refinement.
• Structure Refinement and Minimization: Practical sessions dedicated to refining and minimizing protein structures, enhancing their accuracy and stability through hands-on implementation.
• Molecular Docking Techniques: Hands-on experience encompassing the execution of molecular docking using an array of tools, both online and offline. This includes guidance on utilizing tools like AutodockVina for docking simulations.
• Interpretation of Docking Results: Exercises aimed at interpreting and analyzing docking simulation outcomes, emphasizing the comprehension of various result parameters and their significance.
• Visualization using PyMOL: Practical demonstrations illustrating the visualization and interpretation of protein structures through PyMO. This aids in gaining insights into structural features for a comprehensive understanding.

Throughout the course, the primary focus remains on practical applications to reinforce theoretical concepts. By integrating theoretical knowledge with hands-on exercises centered around specific protein examples, students will gain practical expertise in utilizing bioinformatics tools and techniques crucial for computational analysis in molecular biology and drug discovery.

Required Course Materials Second week: Bioinformatics

The required course materials for the computational analysis of protein structures and databases typically include:

• Laptop/Computer Access: Access to a computer or laptop with internet connectivity for accessing online resources, bioinformatics tools, and software applications required for practical sessions.
• Bioinformatics Software: Depending on the course, specific bioinformatics software may be needed for molecular modeling, protein structure analysis, molecular docking, and visualization (e.g., PyMOL, ChimeraX, AutodockVina, BLAST, etc.).
• Protein Databases Access: Access to protein databases (e.g., Protein Data Bank - PDB) for retrieving protein structures and related data.

Assessment Second week: Bioinformatics

• Practical Assignments (50%):

Practical exercises based on protein structure analysis, database utilization, refinement, and molecular docking techniques. These assignments will evaluate the application of learned skills to real-world scenarios.

• Theory-based Assessments (50%):

Periodic quizzes or tests assessing theoretical knowledge on protein structures, bioinformatics tools, molecular docking principles, and drug discovery concepts.

• Grading Criteria:

A (90-100%): Outstanding performance demonstrating a profound understanding and application of concepts in both practical and theoretical assessments.

B (80-89%): Good comprehension and application of skills, showcasing proficiency in most aspects of the course.

C (70-79%): Satisfactory performance, meeting the basic requirements and demonstrating an acceptable level of understanding.

D (60-69%): Marginal performance, indicating a partial grasp of concepts but with notable gaps in understanding or application.

F (Below 60%): Insufficient performance, with significant shortcomings in understanding and application.