Semestral projects

Introduction to semestral projects

Semestral projects are projects that are shorter than projects of bachelor's or master's theses. They typically span a single semester. Each project is supervised by a supervisor, which also serves as the primary contact for students interested in a project. 

Students are rewarded for working on a semestral project with three credits via courses C5003 Project from chemoinformatics and bioinformatics (autumn semester) and C5002 Projekt z chemoinformatiky a bioinformatiky (spring semester). Enrollment in these courses is possible upon explicit agreement with the project's supervisor. If you are enrolled in one of these courses, you will be required to write a 3-page summary about the project (and its results) upon its completion. You will also be required to present your project and its results at a Structural bioinformatics research group meeting.

Skilled students can also be offered a part-time job. This possibility also applies to students who are not studying at Masaryk University (e.g. they are still attending a high school or studying at a different university).

Currently offered semestral projects are listed below. If you are interested in a project, don't hesitate to get in touch with the supervisor of the project you are interested in. 

Currently offered semestral projects

Comparison of the conformational dependence of the partial atomic charges of solvated proteins

Supervisor: RNDr. Ondřej Schindler (e-mail, MUNI)

Synopsis: Partial atomic charges describe the distribution of electron density within a molecule. Many methods exist for calculating partial atomic charges. The conformational dependence of partial atomic charges tells how much the partial atomic charge changes when the conformation of the structure changes.

Goals: This project aims to determine which type of atomic charge is the most conformationally dependent. To do this, we need to create a set of small proteins from the AlphaFold DB, calculate selected types of partial atomic charges for this set, and compare their conformational dependence.

Prerequisites: Knowledge of Python programming and experience with the Linux operating system. Experience using MetaCentrum computing resources is an advantage.

Optimisation of a template generation tool for conformational analysis of selected cycles

Supervisor: Mgr. Bc. Gabriela Bučeková (e-mail, MUNI)

Consultant: Mgr. Viktoriia Doshchenko (e-mail)

Synopsis: One of the most widely used resources that contain 3D structural data of macromolecules and their corresponding small molecules (i.e. ligands) is the Protein Data Bank (PDB). The PDB includes structure validation reports that contain information not only about the quality of the structures. Despite validation, some structures may have minor or major errors, so it is crucial to verify their quality. The ConfAnalyser tool generates templates for analysing the conformations of cycles in ligands. This tool works with the coordinates in the PDB file. Based on the coordinates of the individual cycles, these files from the dataset are divided into subsets that correspond to the particular conformations of the selected cycles.

Goals: The goal of this work is to optimise the ConfAnalyser tool.

Prerequisites: Python programming fundamentals.

The impact of new structural methods on the evolution of the quality of structures published in academic journals

Supervisor: Mgr. Bc. Jana Porubská (e-mail, MUNI)

Synopsis: Reliable scientific data is a prerequisite for further research and applications. Improving data quality ensures that results are accurate and correctly interpreted. New methods that enable the acquisition of protein structure information have the potential to influence the trend in the quality of structures in a given academic journal.

Goals: In this project, the student aims to investigate new methods for obtaining protein structures and their potential impact on trends in their quality in relevant academic journals. The student will select a relevant set of publications focusing on structure discovery methods, including experimental techniques, bioinformatics approaches, or a combination of both. The student then selects the methods with the largest number of published structures and analyses the quality trends for the journal in which the method was published (using structure quality metrics).

Prerequisites: Ability to work with databases and process many articles, basic knowledge of statistics and statistical calculations.