Structural Bioinformatics

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
UB
The course offers practical and theoretical knowledge about the structure of proteins and other biomacromolecules as well as the methods used for their characterization and analysis. The course includes:
The structural principles of biopolymers: proteins and DNA
Prediction and analysis of three-dimensional structures of biomolecules and their complexes
Molecular simulations of proteins and DNA.

Teachers

Person in charge

Weekly hours

Theory
2
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
6

Competences

Learning Outcomes

Knowledge

  • K1 - Recognize the basic principles of biology, from cellular to organism scale, and how these are related to current knowledge in the fields of bioinformatics, data analysis, and machine learning; thus achieving an interdisciplinary vision with special emphasis on biomedical applications.
  • K2 - Identify mathematical models and statistical and computational methods that allow for solving problems in the fields of molecular biology, genomics, medical research, and population genetics.
  • K5 - Identify the nature of the biological variables that need to be analyzed, as well as the mathematical models, algorithms, and statistical tests appropriate to develop and evaluate statistical analyses and computational tools.
  • K7 - Analyze the sources of scientific information, valid and reliable, to justify the state of the art of a bioinformatics problem and to be able to address its resolution.

    • Skills

  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S10 - Use acquired knowledge and the skills of bioinformatics problem solving in new or unfamiliar environments within broader (or multidisciplinary) contexts related to bioinformatics and computational biology.

    • Objectives

    1. 1. Recognition of the structural patterns of biomolecules and relationship with their biological function. The student must demonstrate understanding of the physicochemical descriptors of structure: terms of potential energy, solubility, acidity, hydrophobicity
      Related competences: K1, K2, K5,
    2. 2. Correlate three-dimensional structure of biomolecules with their biological function
      Demonstrate understanding of:
      - Relationship between sequence, structure, and function: global and local flexibility and similarity of the sequence, three-dimensional preservation of active centres, conservation of interactions with ligands and other proteins.
      - Bases and applications of the homology concept. Identify the conserved residues in structure and describe its possible structural function.
      Related competences: K1, K5, K7, S7,
    3. 3. Manage the software that allows processing data representing structures and sequences of biomolecules.
      Related competences: K2, S7, S10,

    Contents

    1. Part 0. INTRODUCTION
      Introduction to the course.Aims, position of structural bioinformatics within bioinformatics, main objectives. Application examples
    2. Part 1. STRUCTURE AND MODELLING
      Fundamentals of macromolecular structures. Conformational space. Experimental structure determination. Data sources and formats. Databases and Molecular visualization.
      Structural data quality, common issues and fixes.Structure comparison, Sequence/structural alignment, structural families, the concept of homology. Structure prediction (1D, Threading, Comparative, Ab initio, Alphafold). Complex prediction (Docking)
    3. Part 2. CONFORMATIONAL SPACE AND SIMULATION
      Energy evaluation. Molecular force fields. System setup for simulation. Optimization of the simulation process and HPC. Strategies for improved comformation sampling. Simulation analysis. Quality control. Flexibility analysis. Strategies for entropy and free enegy evaluation. Advanced analysis. nmetwork analysis and AI-based methods
    4. Part 3. STRUCTURES IN SYSTEM BIOLOGY
      Protein domains. Interactions between chains and between domains. Predicting physical interactions based on domains. Transitive and permanent complexes. Other predictions of relationships between genes and proteins. Communication systems and signalling networks (phosphorylation). Study of interaction networks: Interactome. Large macromolecular complexes.

    Activities

    Activity Evaluation act


    Partial Exam

    Mid-term exam (Problem solving)
    Objectives: 1 2 3
    Week: 5
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final Exam

    Final Exam.
    Objectives: 1 2 3
    Week: 1 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Theoretical sessions

    Content presentation sessions. Slide presentations and guided demonstrations.
    • Theory: 1 - INTRODUCTION. Introduction to the course.Aims, position of structural bioinformatics within bioinformatics, main objectives. Examples 2 - STRUCTURE AND MODELLING. Fundamentals of maclomolecular structures. Conformational spaces. Experimental structure determination. Data sources and formats. Databases Molecular visualization. Structural data quality, common issues and fixes.Structure comparison, Sequence/structural alignment, structural families, the concept of homology. Structure prediction (1D, Threading, Comparative, Ab initio). Complex prediction (Docking) 3- CONFORMATIONAL SPACE. Simulations. Molecular force fields. System setup for simulation. Optimization of the simulation process and HPC. Strategis for improved comformation samplint. Simulation analysis. Quality control. Flexible systems and use of molecular dynamics to explore flexibility. Strategies for entropy and free enegy evaluation 4 - STRUCTURES IN SYSTEM BIOLOGY Partition of protein domains. Interactions between chains and between domains. Predicting physical interactions based on domains. Transitive and permanent complexes. Other predictions of relationships between genes and proteins. Communication systems and signalling networks (phosphorylation). Study of interaction networks: Interactome. Large macromolecular complexes.
    Objectives: 1 2
    Contents:
    Theory
    27h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    20h

    Guided problem solving


    Objectives: 1 2 3
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    10h
    Guided learning
    0h
    Autonomous learning
    20h

    Guided structural analysis

    Resolution of practical cases on bioinformatics analysis tools, usually available via the web, or easily installableResolution of practical cases on bioinformatics analysis tools, usually available via the web, or easily installable
    Objectives: 1 2 3
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    14h
    Guided learning
    0h
    Autonomous learning
    30h

    Session on Project presentations

    Short group presentations of the results of the analysis project
    Objectives: 1 2 3
    Week: 14
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Intregrated Analysis project

    Free-subject project that involves the use of structural analysis or prediction tools developed during the course, applied to the understanding of the structure-function relationship of a protein system.

    Theory
    0h
    Problems
    0h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    20h

    Teaching methodology

    - The theoretical classes will be expository with the help of graphic materials (slides, videos, computer demonstrations).

    - The problem-solving session will detail the methodology for solving the selected problems. It will include expository and practical sessions.

    - The guided structural analysis sessions will be held in "Hackathon" style working groups to solve the use of structural bioinformatics tools for the resolution of practical cases.

    Evaluation methodology

    For the evaluation of the subject, the grade of the partial exam (MTE) and final exam (FE) and the grade of the practical sessions and the analysis project (Proj) will be taken into account according to the following formula:

    Grade = MTE * 0.2 + FE * 0.6 + Proj * 0.2

    A grade equal to or greater than 5 is required to pass.
    The Practical Sessions and Project (Proj) qualification is conditional on a minimum in-person attendance of 60% in the practical/problem sessions.
    Students who have failed with a grade equal to or greater than 3 may take the re-evaluation exam (RT). In this case, the grade of the subject will be 0.2 * Proj + RT * 0.8.

    Bibliography

    Basic

    Previous capacities

    Basic knowledge of macromolecule structure (Physical and organic chemistry, Biochemistry, Molecular Biology)
    Knowledge of Thermodynamics and kinetics and evaluation of energies in macromolecules (Physical and organic chemistry, Biophysics)
    Knowledge of molecular visualization tools
    Knowledge of programming (python)