Meet-EU 2024

“Meet-EU” is an international team-based course organised by five 4EU+ member universities (Heidelberg, Milan, Paris, Prague, Warsaw) as part of the 4EU+ joint educational offer during the academic year 2024/2025. Meet-EU has been designed to promote interdisciplinary training through research and welcomes students from different disciplines, from computer science, physics and mathematics to chemistry, biology and biotechnologies.

Students are asked to address and solve a specific research problem under the supervision of tutors at each university. Students organize themselves in small groups of 4-5 students each. Each group works independently to propose a solution to the question.

During the kick-off meeting, Sulkowska will present TrmD, Giulio Vistoli will present the computation approaches and Marian Novotny the PLINDER database, which can be used to benchmark methods

At mid-course, a face-to-face meeting will be held, the activities completed by each group will be discussed and the groups will be paired to implement a joint second step of the project. Paired teams should be preferably belong to different countries.

At a final meeting, each group will present its results, which will be evaluated by a panel of experts in the field.

The official language of the course is English.

The course is based on team work and each team will conduct independent research activities delivering a presentation. Essays will be presented at a face-to-face conference in TBD. ECTS recognition is done at the home university and faculty.

This year's projects will be directed toward different proteins from the Methyltransferase family. It will focus on structural bioinformatics and involves docking simulations and virtual screening campaigns on the TrmD methyltransferase. Each group will develop an original computational procedure for the identification of potential TrmD inhibitors. The project will include an analysis of their binding sites, selection of the most suitable pockets, preliminary docking studies by using known inhibitors, developing of predictive models to be applied in the following virtual screening campaigns. Hence, the project will involve computational techniques such as molecular dynamics and docking simulations as well as pocket searching and structural analysis. These techniques could be combined with artificial intelligence algorithms to enhance the predictive power of the performed simulations.

• Hou, Ya-Ming, et al. "TrmD: a methyl transferase for tRNA methylation with m1G37." The Enzymes 41 (2017): 89-115.
• Christian, Thomas, et al. "Distinct origins of tRNA (m1G37) methyltransferase." Journal of molecular biology 339.4 (2004): 707-719.
• Elkins, Patricia A., et al. "Insights into catalysis by a knotted TrmD tRNA methyltransferase." Journal of molecular biology 333.5 (2003): 931-949.
• Hori, Hiroyuki. "Transfer RNA methyltransferases with a SpoU-TrmD (SPOUT) fold and their modified nucleosides in tRNA." Biomolecules 7.1 (2017): 23.
• Takeda, Hiroshi, et al. "The substrate specificity of tRNA (m1G37) methyltransferase (TrmD) from Aquifex aeolicus." Genes to Cells 11.12 (2006): 1353-1365.
• Zhong, Wenhe, et al. "Targeting the bacterial epitranscriptome for antibiotic development: discovery of novel tRNA-(N1G37) methyltransferase (TrmD) inhibitors." ACS infectious diseases 5.3 (2019): 326-335.

• 4YPX – Crystal structure of TrmD, a M1G37 tRNA Methyltransferase with SAM-competitive compounds
• 5ZHI – Apo crystal structure of TrmD from Mycobacterium tuberculosis
• 7NNO – Crystal structure of TrmD from Mycobacterium tuberculosis in complex with active-site inhibitor

• Simple pipeline introduction
• Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
• Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

• ChEMBL
• Lignad Scout
• Schrodinger Maestro
• Schrodinger Glide