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Integrative Structural Biology
Provider: Department of Biology

Activity no.: 5148-23-01-31There are 1 available seats 
Enrollment deadline: 07/02/2023
Date and time07.02.2023, at: 09:00 - 15.06.2023, at: 16:00
Regular seats20
ECTS credits10.00
Contact personKresten Lindorff-Larsen    E-mail address: lindorff@bio.ku.dk
Enrolment Handling/Course OrganiserLucy Holt    E-mail address: lucy.holt@bio.ku.dk
Written languageEnglish
Teaching languageEnglish
Exam detailsStudents must participate actively during the course (minimum 80% participation in practical exercises). The student must produce a poster and present it during the poster session. If the student is unable to attend the poster session, the student must present the poster and discuss it during an oral examination.
Course workload
Course workload categoryHours
Practical exercises23.00
Lectures43.00
Excursions15.00
Preparation of presentation40.00
Preparation126.00
Presentations3.00

Sum250.00


Content
The course will introduce students to the many techniques that researchers use to capture biological structure and it will teach students to integrate data obtained from these different techniques using state-of-the-art computational methods. The goal of the course is to enable students to solve biological problems by applying and integrating state-of-the-art methods in structural biology, and to read and understand the evermore complex structural biology literature. The course involves three key components:

Component 1: Experimental Methods
The first part of the course introduces students to the many advanced techniques researchers use to capture biological structure. This will involve hands on experimental exercises during which students learn how to use these methods in order to derive the three-dimensional structures of proteins.
Nine experimental methods modules are offered to students including: Small-angle X-ray scattering, Nuclear magnetic resonance spectroscopy, Hydrogen deuterium exchange mass spectrometry, X-ray crystallography, Fluorescence methods including single molecule approaches, Optical Tweezers, Cryo-electron microscopy of membrane proteins, and Neutron scattering and diffraction. Each module includes a mixture of lectures, practical exercises and, where relevant, visits to large-scale facilities such as ESS, MAX-IV, the Core Facility for Integrated Microscopy, the cOpenNMR facility for nuclear magnetic resonance spectroscopy and the facility for biological small-angle X-ray scattering, CPHSAXS.
PhD students are expected to enrol in 3–4 of the 9 experimental methods modules. Placement in the modules will be allocated dependent on student preferences, interests and laboratory/facility capacities.

Component 2: Computational Integration
The second part of the course will provide an overview of how computational methods in structural biology, e.g. molecular dynamics simulations and machine learning methods, can be used to study the structure and dynamics of proteins and how computational methods can be used for protein design. The course will also teach students to integrate data obtained from various structural biology techniques using state-of-the-art computational modelling and machine learning methods. This will involve hands-on experience in computational experiments. This part of the course will include lectures, journal clubs, practical exercises, group discussion and hands-on-introductions to e.g. software used in protein structure determination and molecular simulations.

Component 3: Poster Session
The student must produce a poster based on the course activities. Posters must be presented during a poster session at the conclusion of the course.

Learning outcome

 Knowledge:

  • Knowledge on advanced structural biology techniques and instrumentation
  • Understand how protein structures are determined via advanced structural biology techniques Understand how different methods may be integrated to study complex and dynamic macromolecules and their assemblies.
  • Understand the time and length scales that different methods can access.
  • Understand the basic principles of computer simulation techniques and how they can be used together with experimental methods.

Skills:

  • Ability to read and critically evaluate publications containing structural biology data
  • Be able to, at a basic level, design strategies for structural studies of proteins.
  • Can differentiate between methods according to resolution in both time and length scales.
  • To compare the strengths, limitations and complementary potential of structural data obtained using different techniques.
  • Be able to use simple methods for protein structure determination.
  • Be able to perform and visualize results from structure prediction methods and molecular simulations.
  • Can read and extract key scientific findings from primary literature on integrative structural biology.
  • Can perform experiments within structural biology and use computational tools to evaluate structural biology data.

Competences:

  • Critically evaluate data obtained in structural biology research.
  • Combine a broad range of biophysical methods, including those based on computational methods to envisage how these methods can be integrated in structural studies of proteins.
  • Ability to combine methods to study the structure, dynamics and functions of proteins for use in both pharmaceutical sciences (for example as drug targets or protein pharmaceuticals) or biotechnology (for example enzymes)

Target group
The course is an advanced interdisciplinary course and students interested in biological structure are encouraged to apply from a wide variety of disciplines, including chemistry, biochemistry, biology, physics, computer science, human biology, neuroscience, and the pharmaceutical sciences. It will be beneficial to have some basic knowledge of protein science and students who do not should expect to supplement their knowledge on this topic during the course. To make it possible for students from a variety of disciplines to participate, students will receive a brief six-hour introduction crash course to either protein science or computer science, depending on their backgrounds.

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