Pro-/Seminar: Computational prediction of protein-RNA and protein-peptide interactions

MAXIMUM NUMBER OF PARTICIPANTS
16

Mandatory

• Registration by e-mail to Kerstin Gronow-Pudelek by October 31, 2024
• Presence at the preliminary meeting on November 12, 2024
• Registration in the LSF by December 3, 2024, is mandatory; later registration is not possible.

If there are more than 16 registrations, the lottery will decide.

Requirements

Knowledge corresponding to semester 4 (proseminar) or knowledge corresponding to BSc degree (seminar)

Conditions for Certification

• Successful presentation
• Regular participation (at least 75% of talks)
• Submission of a two-page report by e-mail to the tutor by 15.12.2024

Preliminary Meeting

• Purpose: placement of the topics and organization
• Time and Place: Tuesday, November 12, 2024, 12:30, E2 1, room 007

Schedule

Additional Information

Please regard the important hints (link) for your seminar presentation!

Topics

1. PrismNet: predicting protein-RNA interaction using in vivo RNA structural information (Proseminar or Seminar)
2. PrismNet: predicting protein-RNA interaction using in vivo RNA structural information (Proseminar or Seminar)
3. DeepCLIP: predicting the effect of mutations on protein–RNA binding with deep learning (Seminar)
4. Widespread autogenous mRNA–protein interactions detected by CLIP-seq (Proseminar or Seminar)
5. Differential exon usage of developmental genes is associated with deregulated epigenetic marks (Proseminar or Seminar)
6. Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD (Seminar)
7. acorde unravels functionally interpretable networks of isoform co-usage from single cell data (Seminar)
8. MARVEL: an integrated alternative splicing analysis platform for single-cell RNA sequencing data (Proseminar or Seminar)
9. Insplico: effective computational tool for studying splicing order of adjacent introns genome-wide with short and long RNA-seq reads (Proseminar)
10. Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins (Proseminar or Seminar)
11. Leveraging protein dynamics to identify functional phosphorylation sites using deep learning models (Seminar)
12. Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins (Proseminar or Seminar)
13. Machine learning integrating protein structure, sequence, and dynamics to predict the enzyme activity of bovine enterokinase variants (Seminar)
14. Biophysical prediction of protein-peptide interactions and signaling networks using machine learning (Proseminar)
15. PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features (Seminar)
16. Protein structure prediction using a maximum likelihood formulation of a recurrent geometric network (Seminar)
17. Protein complex prediction with AlphaFold-Multimer (Proseminar)