Computational Biochemistry
Áreas Científicas |
Classificação |
Área Científica |
OFICIAL |
Biotecnologia |
Ocorrência: 2019/2020 - 1S
Ciclos de Estudo/Cursos
Sigla |
Nº de Estudantes |
Plano de Estudos |
Anos Curriculares |
Créditos UCN |
Créditos ECTS |
Horas de Contacto |
Horas Totais |
BINF |
18 |
Study Plan |
3 |
- |
5,5 |
60 |
148,5 |
Docência - Responsabilidades
Língua de trabalho
Portuguese
Objetivos
The UC aims to develop chemical and biochemical structure-based computational skills, focusing on structure modelling, protein modelling and protein interactions. Students will learn the validity and applicability of semi-empirical, density functional theory and ab initio methods, as well as the basics of molecular mechanics and dynamics and of protein-ligand and protein-protein docking.
Resultados de aprendizagem e competências
.
Modo de trabalho
Presencial
Programa
1. Introduction – atomic and molecular structure; bonds and interactions; Schrödinger’s equation and its application on computational chemistry and biochemistry. Software for drawing and visualization; software packages. 2. Computational chemistry – semi-empirical methods, density functional theory and ab initio methods; characteristics, applications and limitations 3. Computational chemistry – implicit and explicit models for condensed phases 4. Protein modeling – homology and ab initio approaches to protein structure 5. Protein modeling – molecular mechanics and dynamics approaches to biological questions; force fields, their applicability and limitations 6. Lipids and membranes - computational approaches to modelling 7. Protein-ligand interactions – docking techniques, applications and limitations; post-docking analysis – quantum chemistry and molecular mechanics/dynamics based refinements 8. Protein-protein interactions – the interactome; tools for protein-protein docking.
Bibliografia Obrigatória
Cramer, C.J; Essentials of Computational Chemistry: Theories and Models.. ISBN: 978-0-470-09182-1
Tsai, C.S; An Introduction to Computational Biochemistry. , John Wiley & Sons, 2002. ISBN: 978-0-471-40120-9
Kukol, A.; Molecular Modeling of Proteins., Springer Nature, 2008. ISBN: 978-1-58829-864-5
Métodos de ensino e atividades de aprendizagem
Teaching 1. 1,5 h weekly lectures aimed at exposition of concepts and techniques. 2. 1,5 h weekly exercise class focused on solving exercises and learning the basics about manipulating the adequate software packages, planning the work that needs to be done for each task and understanding the information obtained by the various techniques. Evaluation 1. Continuous: a number of assignments will be distributed throughout the semester; all assignments will be graded and the average of their grade will account for 50% of the UC final note. A final test (minimum grade: 9.5 in 20) will account for 50% of the UC final note. 2. By exam: final exam (minimum grade: 9.5 in 20). The UC note will be the exam note.
Software
GROMACS
qtgrace
VMD
Tipo de avaliação
Distributed evaluation with final exam
Componentes de Avaliação
Designation |
Peso (%) |
Teste |
40,00 |
Trabalho laboratorial |
60,00 |
Total: |
100,00 |
Componentes de Ocupação
Designation |
Tempo (Horas) |
Frequência das aulas |
52,50 |
Estudo autónomo |
66,00 |
Trabalho laboratorial |
30,00 |
Total: |
148,50 |
Obtenção de frequência
NA
Fórmula de cálculo da classificação final
Final grade (CA) = 0.40 * average test grade + 0.60 * average work grade
Minimum grade of 8 in tests and 9.5 in the average of works
Final grade (Exam) = 100% exame grade
Provas e trabalhos especiais
The classification of the work may be different for different members of each group, depending on the personal performance during the presentation and discussion of the work.
Students with a final grade in the UC higher than 16 (sixteen) will be required to take an Oral Test; the final classification in the UC will be the score of the Oral Test. In case of non-attendance to the Oral Test, the final classification in the UC will be 16 values.