Guía Docente 2020-21
BIOINFORMÁTICA ESTRUCTURAL

BASIC DETAILS:

Subject: BIOINFORMÁTICA ESTRUCTURAL
Id.: 33372
Programme: DOBLE GRADO EN FARMACIA Y BIOINFORMÁTICA. PLAN 2018
Module: BIOINFORMÁTICA
Subject type: OBLIGATORIA
Year: 3 Teaching period: Primer Cuatrimestre
Credits: 3 Total hours: 75
Classroom activities: 35 Individual study: 40
Main teaching language: Inglés Secondary teaching language: Castellano
Lecturer: ROIG MOLINA, FRANCISCO JOSE (T) Email: fjroig@usj.es

PRESENTATION:

This course covers the algorithmic approaches used to predict the primary, secondary and tertiary structure of proteins. In addition to understanding the theoretical bases, we will study how to apply these methods in practice for structure prediction and expression analysis. Within the course, a general overview of the tools and databases most used in structural Bioinformatics will be given, emphasizing the input formats, parameters and interpretation of results common to many of them.
 
Upon completing the subject, the student will be able to:

PROFESSIONAL COMPETENCES ACQUIRED IN THE SUBJECT:

General programme competences G01 Use learning strategies autonomously for their application in the continuous improvement of professional practice.
G02 Perform the analysis and synthesis of problems of their professional activity and apply them in similar environments.
G03 Cooperate to achieve common results through teamwork in a context of integration, collaboration and empowerment of critical discussion.
G04 Reason critically based on information, data and lines of action and their application on relevant issues of a social, scientific or ethical nature.
G05 Communicate professional topics in Spanish and / or English both orally and in writing.
G06 Solve complex or unforeseen problems that arise during the professional activity within any type of organisation and adapt to the needs and demands of their professional environment.
G07 Choose between different complex models of knowledge to solve problems.
G09 Apply information and communication technologies in the professional field.
G10 Apply creativity, independence of thought, self-criticism and autonomy in the professional practice.
Specific programme competences E02 Develop the use and programming of computers, databases and computer programs and their application in bioinformatics.
E03 Apply the fundamental concepts of mathematics, logic, algorithmics and computational complexity to solve problems specific to bioinformatics.
E04 Program applications in a robust, correct, and efficient way, choosing the paradigm and the most appropriate programming languages, applying knowledge about basic algorithmic procedures and using the most appropriate types and data structures.
E05 Implement well-founded applications, previously designed and analysed, in the characteristics of the databases.
E06 Apply the fundamental principles and basic techniques of intelligent systems and their practical application in the field of bioinformatics.
E07 Apply the principles, methodologies and life cycles of software engineering to the development of a project in the field of bioinformatics.
E12 Apply the principles and techniques of protein computational modelling to predict their biological function, their activity or new therapeutic targets (Structural Bioinformatics, Computational Toxicology).
E13 Apply omics technologies for the extraction of statistically significant information and for the creation of relational databases of biodata that can be updated and publicly accessible to the scientific community.
E14 Use programming languages, most commonly used in the field of Life Sciences, to develop and evaluate techniques and/ or computational tools.
E15 Infer the evolutionary history of genes and proteins through the creation and interpretation of phylogenetic trees.
E16 Plan linkage and association studies for medical and environmental purposes.
E17 Induce complex relationships between samples by applying statistical and classification techniques.
E18 Apply statistical and computational methods to solve problems in the fields of molecular biology, genomics, medical research and population genetics.
E21 Apply computational and data processing techniques for the integration of physical, chemical and biological concepts and data for the description and/ or prediction of the activity of a substance in a given context.

PRE-REQUISITES:

Not prerequisites needed.

SUBJECT PROGRAMME:

Subject contents:

1 - Proteins: Structure and function.
    1.1 - What is a protein?
    1.2 - Protein structure: Structuring levels
    1.3 - Structure-function relationship
2 - Bioinformatics and proteins
    2.1 - From sequence to function
3 - Databases and structural data formats
    3.1 - Protein Data Bank
    3.2 - SCOP and CATH
4 - Prediction of structures
    4.1 - Secondary structure prediction
    4.2 - 3D structure prediction using comparative modeling
    4.3 - Model evaluation
    4.4 - Software for structural analysis
       4.4.1 - Visualization: Pymol and Rasmol
       4.4.2 - Modeling: I-Tasser and Modeller
5 - Molecular coupling
    5.1 - What is molecular coupling?
    5.2 - Software
       5.2.1 - AutoDock Vina and SwissDock
6 - Applications of structural bioinformatics.
    6.1 - Functional identification
    6.2 - Development of new drugs

Subject planning could be modified due unforeseen circumstances (group performance, availability of resources, changes to academic calendar etc.) and should not, therefore, be considered to be definitive.


TEACHING AND LEARNING METHODOLOGIES AND ACTIVITIES:

Teaching and learning methodologies and activities applied:

Master classes:
 
Teacher will explain the theory using TIC and physical resources on presential classes. Material will be available on the PDU in advance for previous reading. Students are highly recommended to perform the reading task.
 
 
 
Theoretical-practical classes:
 
Omics needed from practise. In this type of sessions the students will perform a reproduction of command execution done in theory and the completion of proposed exercises, in order to assure correct comprehension.
 
 
 
Self-learning based on critical-thinking:
 
Sutdents will explore scientific publication and discuss between each other about oral presentation of the rest of students. This way, basic knowledge of the field will be reinforce bias for scientific process.
 
 
 
Learning based on proyects:
 
Two projects will be peformed time course of the subject in order to apply the acquire knowledge in the experimental design area:
a)The students will carry out the critical analysis of a publication in which a structural reconstruction performed using bioinformatics, analyzing the methodology, the type of results. Students are expected to be able not only to carry out a bibliographic analysis, but also to be able to understand the objectives, the methodology and the conclusions at a structural and biological level. This work will be 10% of the final grade. The work will be delivered in electronic format.
b)The students will carry out the reconstruction of the 3D structure of a protein sequence, looking for possible ligands. The work will be presented in the form of a scientific article and the execution of the analyzes will be presented in markdown format, preferably written in English. The starting data for the work may be provided by the teacher or selected by the students with the help of the teacher. This work will represent 35% of the final grade The work will be in electronic format and presented in class virtually
Both works will be presented in class on the date shown in the planning.
 
 
Tutoring sessions
The students will be able to ask the teacher any doubts that arise both from the face-to-face classes and from the autonomous or group work. The tutorials can be both face-to-face and using available technological means
 
 
 
 

Student work load:

Teaching mode Teaching methods Estimated hours
Classroom activities
Master classes 16
Tutorials 4
Individual activities (essays, presentations, oral presentations, concept maps, problems ...) 10
Evaluation tests (questionnaires and other instruments) 5
Individual study
Individual study 7
Individual coursework preparation 10
Project work 14
Recommended reading 4
Video clase/Webinars/videolessons/ podcast 5
Total hours: 75

ASSESSMENT SCHEME:

Calculation of final mark:

Individual coursework: 45 %
Final exam: 55 %
TOTAL 100 %

*Las observaciones específicas sobre el sistema de evaluación serán comunicadas por escrito a los alumnos al inicio de la materia.

BIBLIOGRAPHY AND DOCUMENTATION:

Basic bibliography:

Fiser A. From Protein Structure to Function with Bioinformatics. Ridgen DJ, editor. Springer 2008.

Recommended bibliography:

Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. Molecular biology of the cell. New York: Garland Science.2002.
Bateman A. Protein families: relating protein sequence, structure, and function. New Jersey: John Wiley and Sons.2014
Berg JM, Tymoczko JL, Stryer L. Biochemistry. 5th edition. New York: W H Freeman. 2002.
Fiser A. Template-based protein structure modeling. Methods Mol Biol. 2010;673:73-94.
Persson B. Bioinformatics in protein analysis. EXS. 2000;88:215-231.
Roger Sayle and E. James Milner-White. RasMol: Biomolecular graphics for all. Trends in Biochemical Sciences (TIBS), September 1995, Vol. 20, No. 9, p. 374.
Webb B., Sali A. Comparative Protein Structure Modeling Using Modeller. Current Protocols in Bioinformatics 54, John Wiley and Sons, Inc., 5.6.1-5.6.37, 2016.
Xu Y, Xu D, Liang J. Computational methods for protein structure prediction and modeling volume 1: basic characterization. Berlin: Springer. 2007.
Xu Y, Xu D, Liang J. Computational methods for protein structure prediction and modeling volume 2: Structure Prediction. Berlin: Springer. 2007.
Yang, J. and Zhang, Y.Protein Structure and Function Prediction Using I-TASSER. Current protocols in bioinformatics, 2015:52, 5.8.1–5.8.15.

Recommended websites:

Protein Data Bank https://www.rcsb.org/