Computational Biochemistry

Code:

Q4034

Acronym:

Q4034

Keywords
Classification	Keyword
OFICIAL	Chemistry

Instance: 2022/2023 - 1S

Active?	Yes
Responsible unit:	Department of Chemistry and Biochemistry
Course/CS Responsible:	Master in Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	1	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:BBC	18	The study plan since 2018	1	-	6	42	162

Teaching language

Portuguese

Objectives

The aim of this course is to give to the student a broad view of the Computational Biochemistry field. The course will focus on molecular dynamics of biological macromolecules. The students will learn to prepare, execute and analyse molecular dynamics simulations of biomolecules

Learning outcomes and competences

Capacity to perform and analyse molecular dynamics simulations . Capacity to predict the properties of biological systemas that do not involve electronic rearrangements.

Working method

Presencial

Program

1. Introduction 1.1. Introduction to Computational Biochemistry 1.2. Most common methods used in the study of biochemical systems. 1.3. Relation between the system size and the method of simulation. 2. Molecular Mechanics 2. 1. Introduction 2. 2. Force Fields 2.3. Bond Enegy 2.4. Angle Energy 2.5. Torsion Energy 2.6. Van der Waals Energy 2.7. Coulombic Energy 2.8. Crossed terms 2.9. Parameterization 2.10. Most used force fields 3. Molecular Dynamics 3.1. Introduction 3.2. Integration of the Equations of Motion 3.3. Periodic Boundary Conditions 3.4. Long Range Interactions 3.4.1 Smoothing functions 3.4.2 Particle Mesh-Ewald 3.5. Examples of Properties Calculated from Molecular Dynamics Trajectories 3.5.1 Density 3.5.2 Diffusion Coefficients 3.5.3 Orientational Dynamics 3.5.4 Root Mean Square Deviations in atomic positions 3.5.5 Root Mean Square Fluctuations in atomic positions 3.5.6 Radial Distribution Functions 3.5.7 Molecular Orientation. 

Mandatory literature

Nuno Cerqueira, Sérgio Sousa, Pedro Fernandes, Maria João Ramos; Bioquímica Computacional, Faculdade de Ciências do Porto, 2007
Nuno Cerqueira, Sérgio Sousa, Pedro Fernandes, Maria João Ramos; Faculdade de Ciências do Porto, 2007; Estudo da enzima protease do virus HIV-1

Teaching methods and learning activities

Problem-Based Learning

Software

Programa de dinãmica molecular AMBER
Programa de visualização VMD

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	50,00
Participação presencial	25,00
Trabalho prático ou de projeto	25,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de relatório/dissertação/tese	24,00
Estudo autónomo	70,00
Frequência das aulas	56,00
Total:	150,00

Eligibility for exams

The students must attend at least 75% of the practical classes

Calculation formula of final grade

The final classification will be the weighted average of the theory classification (50%) and the practical classification (50%).
The theory classification is essentially the result of the theoretical exam, with a small influence from the continuous evaluation made by the teacher during the classes. It can not be less than 7/20 for the student to obtain approval at the course.
The practical classification will be averaged out with the continuous evaluation during the practical classes (25%) and the classification of a report concerning the work developed in classroom during the semester (25%).

Classification improvement

The student can repeat the theoretical examination within the calendar and rules defined by the Conselho Pedagógico. The practical classification cannot be improved as it reflects the progress of the student along the year.