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Protein Prediction

Module IN2221

This Module is offered by TUM Department of Informatics.

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.

Module version of WS 2011/2

There are historic module descriptions of this module. A module description is valid until replaced by a newer one.

Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.

available module versions
SS 2015WS 2011/2

Basic Information

IN2221 is a semester module in English language at Bachelor’s level and 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 workloadContact hoursCredits (ECTS)
240 h 90 h 8 CP

Content, Learning Outcome and Preconditions


Intro: What is a protein? What is protein structure & function? Prediction of protein structure: overview.
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 into machine learning with particular focus on how to avoid over-estimating performance.

Learning Outcome

Students understand the principle concepts of protein sequence analysis, in protein structure, and protein structure prediction and are able to evaluate these. They can apply the state-of-the-art methods toward these objectives in computational biology.
Students can develop their own prediction methods (in groups guided by tutors) by combining existing methods, or algorithms, and/or create new methods.
The participants are able to analyse and to evaluate published methods (as readers of the publication, as peer-reviewers, and as competitors). Based on the outcome of these evaluations they are able to create a tool that is readily usable by experimental and computational biologists. This means, they can convert an abstract idea of a solution under consideration of technical aspects into pseudo-code and optionally further into executable programs during the exercises.


Background in Computational Biology as acquired in semester 1-4 in the Bachelor program.

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

VU 6 Protein Prediction I Advanced (IN2221) Richter, L. Rost, B. Tue, 12:30–14:00, MI 01.09.034
Thu, 12:30–14:00, MI 01.09.034
Thu, 14:00–15:30, MI 01.09.034

Learning and Teaching Methods

Lectures, Seminars, Exercises, Problems for individual study: The students apply the theory presented in the lecture by writing a protein structure prediction method in the exercise starting from data in varying form (depending on the problem at hand). In some cases, they will get the complete input from the tutors, in others, they will have to write database parsers and generate the input/output data they will need during the labwork.
Each team will thoroughly estimate the performance of the tool they created and the team will present their results to their peers and to the tutors.


Lectures presented in form of interactive seminars using projector and white board; some lectures will be given on the white board, only. All lectures will be video taped and both the slides and the recordings will be made available shortly after the lecture.


Will be announced in the lecture.

Module Exam

Description of exams and course work

The module is graded by a written exam. The exam will take 120 minutes.

In the exam the participants demonstrate their ability to devise and discuss an appropriate computational approach for a solution for 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 they can choose the appropriate data abstraction level (1D, 2D, 3D) depending to 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 pro's and con's 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.

Details are announced at the beginning of the module.
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