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Computational Biochemistry

Code: BINF026     Sigla: BC

Áreas Científicas
Classificação Área Científica
OFICIAL Biotecnologia

Ocorrência: 2019/2020 - 1S

Ativa? Yes
Unidade Responsável: Biotecnologia
Curso/CE Responsável: Undergraduate in Bioinformatics

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

Docente Responsabilidade
Marta Sofia Guedes de Campos Justino

Docência - Horas

Theorethical: 1,50
Practical and Laboratory: 2,00
Type Docente Turmas Horas
Theorethical Totais 1 1,50
José Gonçalo Deira Duarte de Campos Justino 1,00
Marta Sofia Guedes de Campos Justino 0,50
Practical and Laboratory Totais 1 2,00
José Gonçalo Deira Duarte de Campos Justino 2,00

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.
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