Protein Prediction I for Computer Scientists
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2322 is a semester module in English language at Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|240 h||90 h||8 CP|
Content, Learning Outcome and Preconditions
Methods: Sequence comparisons (sequence-sequence, sequence-profile, profile-profile, HMM); prediction of protein structure in 1D (secondary structure, solvent accessibility, membrane helices), 2D (contact prediction methods, contemporary methods using correlated mutations), 3D (comparative modeling, MD); prediction of disorder in proteins.
The lectures include an introduction to machine learning with particular focus on how to avoid over-estimating performance.
Students have acquired the theoretical background consisting of the presented knowledge to develop and implement simple independent solutions towards the presented problems.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VU||6||Protein Prediction I Beginners (IN2322)||Richter, L. Rost, B.||
Tue, 13:00–15:00, MW 2250
Thu, 12:30–14:00, MI HS3
and singular or moved dates
Learning and Teaching Methods
Lectures (include Q&A): Theoretical background for all topics will be presented in traditional lecture style with slides, as well as, interactively through white board presentations and Q&A sessions.
Exercises (include Q&A): Practical programming exercises deepening and applying the material presented in the lectures; occasionally, presentation of additional material needed for better understanding; exercises also include interactive Q&A sessions, and presentations from the students.
Description of exams and course work
Weekly programming exercises and questions are graded and contribute to 50% of the final grade.
In the exam the participants demonstrate their ability to devise and discuss an appropriate computational approach for a solution to a biological problem in the area of structure prediction. For example, they choose the appropriate methods depending on the type of data they have (1D, 2D, 3D) as well as the appropriate data abstraction level (1D, 2D, 3D) depending on the respective biological question. They demonstrate their understanding of the concepts in the choice of appropriate solution approaches to the given tasks and they can evaluate these in terms of a discussion of the various pros and cons of alternative approaches in biological as well as in technical aspects. They can demonstrate their ability to create a usable tool implementing a solution approach down to the level of pseudo-code. More details are announced at the lecture beginning.
The exam may be repeated at the end of the semester.