Academic Year:
2021/22
32533 - COMPUTATIONAL BIOCHEMISTRY
This is a non-sworn machine translation intended to provide students with general information about the course. As the translation from Spanish to English has not been post-edited, it may be inaccurate and potentially contain errors. We do not accept any liability for errors of this kind.
The course guides for the subjects taught in English have been translated by their teaching teams
Teaching Plan Information
Code - Course title:
32533 - COMPUTATIONAL BIOCHEMISTRY
Degree:
616 - Máster en Química Teórica y Modelización Computacional (2013)
651 - Máster Erasmus Mundus en Química Teórica y Modelización Computacional
748 -
751 - Máster en Química Teórica y Modelización Computacional Europeo
762 -
Faculty:
104 - Facultad de Ciencias
Academic year:
2021/22
1.1. Content area
Computational Biochemistry
1.2. Course nature
Optional
1.3. Course level
Máster (EQF/MECU 7)
1.6. ECTS Credit allotment
5.0
1.7. Language of instruction
English
1.8. Prerequisites
There no prerequisites.
1.9. Recommendations
There are no recommendations.
1.10. Minimum attendance requirement
Attendance is mandatory.
1.11. Subject coordinator
1.12. Competences and learning outcomes
1.12.1. Competences
These learning objectives contribute to provide the following skills for the students:
BASIC AND GENERAL SKILLS
CB6 – Students possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
CB7 - Students know how to apply the acquired knowledge and their problem solving capacity in new or little known environments within broader (or multidisciplinary) contexts related to their area of study.
CB8 - Students are able to integrate knowledge and face the complexity of making judgments from information that, incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments.
CB9 - Students know how to communicate their conclusions and the knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way.
CB10 - Students possess the learning skills that allow them to continue studying in a way that will be self-directed or autonomous.
CG01 - Students are able to foster, in academic and professional contexts, technological and scientific progress within a society based on knowledge and respect for: a) fundamental rights and equal opportunities between men and women, b) The principles of equal opportunities and universal accessibility for persons with disabilities, and c) the values of a culture of peace and democratic values.
CG02 - Students are able to solve problems and make decisions of any kind under the commitment to the defense and practice of equality policies.
CROSS-COMPREHENSIVE SKILLS
CT01 - Students are able to adapt their selves to different cultural environments by demonstrating that they are able to respond to change with flexibility.
CT02 - Students are organized at work demonstrating that they know how to manage their time and resources.
CT03 - Students have the ability of analyze and synthesize in such a way that they can understand, interpret and evaluate the relevant information by assuming with responsibility their own learning or, in the future, the identification of professional exits and employment fields.
SPECIFIC SKILLS
CE01- Students demonstrate their knowledge and understanding of the facts applying concepts, principles and theories related to the Theoretical Chemistry and Computational Modeling.
CE03 – Students acquire an overview of the different applications of the Theoretical Chemistry and modeling in the fields of Chemistry, Biochemistry, Materials Sciences, Astrophysics and Catalysis.
CE04 - Students understand the theoretical and practical bases of computational techniques with which they can analyze the electronic, morphological and structural structure of a compound and interpret the results adequately.
CE05 – Students have the ability to handle the main sources of scientific information related to Theoretical Chemistry and Computational Modeling. They are able to search for relevant information in web pages of structural data, physical chemical experimental data, databases of molecular calculations, databases of scientific bibliography and scientific works.
CE25 - Students acquire the practical knowledge necessary to carry out studies in biochemical systems using computer simulations.
1.12.2. Learning outcomes
To know the main structural features of biological molecules and the interactions that are at their origin. To understand the theoretical basis of the most used techniques for the simulation of biomolecules. To be able to apply these techniques to simple problems. To recognize the limitations of the studied techniques and to choose among them the most suitable for a given problem.
1.12.3. Course objectives
-
1.13. Course contents
1. Introduction. Biomolecules and their properties. Structural databases of biomolecules. Structure-energy relationship: Biomolecules modeling.
2. Potential energy surfaces in biomolecules. Molecular mechanics force fields. Conformational exploration. Minimization: Reaction coordinate. Molecular Dynamics and Monte Carlo methods. Structure prediction methods. .
3. Advanced MD methods. Ab initio Born-Oppenheimer MD, hybrid QM/MM MD simulations. Enhanced sampling techniques, free energy simulations and Metadynamics.
4. Mixed QM/MM models. Electrostatic and polarizable embedding. Continuum solvation models. Extension to excited states .
5. Structure-activity relationships. Molecular descriptors. Quantitative structure-activity relationships (QSAR).
6. Protein-ligand interaction. Docking techniques.
1.14. Course bibliography
-Molecular Modeling and Simulation: An Interdisciplinary Guide.
Tamar Schlick, Springer.
-Understanding Molecular Simulation, Second Edition: From Algorithms to Applications
Daan Frenkel, Academic Press.
-Essentials of Computational Chemistry: Theories and Models.
Chris Cramer Wiley.
2. Teaching-and-learning methodologies and student workload
2.1. Contact hours
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#horas
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Contact hours (minimum 33%)
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50
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Independent study time
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75
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2.2. List of training activities
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Activity
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# hours
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Lectures
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20
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Seminars
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Practical sessions
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20
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Clinical sessions
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Computer lab
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Laboratory
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Work placement
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Supervised study
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Tutorials
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10
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Assessment activities
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Other
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Lecture: The Professor will deliver lectures about the theoretical contents of the course during two-hour sessions. The presentations will be based on the different materials available at the Moodle platform.
Network teaching: All the tools available at the Moodle website (https://posgrado.uam.es) will be used (uploading of teaching materials, utilization of work team strategies, wiki, blogs, e-mail, etc.).
Teaching in computer room: Teaching will be conducted in a computer room. The classes, in sessions of three hours, will include a brief theoretical introduction, in which the teacher will present the basic concepts, followed by practical applications, in which the student will learn through the resolution of practical examples.
Tutoring sessions: The professor can organize either individual or group tutoring sessions about particular topics and questions raised by students.
Written reports: Orientation and supervision in the preparation of written reports.
3. Evaluation procedures and weight of components in the final grade
3.1. Regular assessment
The knowledge acquired by the student will be evaluated along the course. The educational model to follow will emphasize a continuous effort and advance in training and learning.
The final student mark will be based on exercises that must be done during the course. The next criteria will be followed for assessment of student exercises:
- 10% attendance and participation in class,
- 90% practical case study. Part of this percentage may be applied to the performance of tests.
3.1.1. List of evaluation activities
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Evaluatory activity
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%
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Final exam
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Continuous assessment
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3.2. Resit
The student will have to face a final exam, including both theory and practical exercises. The student mark will be obtained from:
- 60% theoretical exam,
- 40% practical exam.
3.2.1. List of evaluation activities
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Evaluatory activity
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%
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|
Final exam
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|
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Continuous assessment
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4. Proposed workplan
Please check the official schedule published on the Master's website.