Prediction of Binding Residues in Disordered Regions Based on Protein Embeddings; TUM Master Praktikum Bioinformatics 2022 (Project #3) and Master's Thesis
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Updated
May 7, 2024 - Python
Prediction of Binding Residues in Disordered Regions Based on Protein Embeddings; TUM Master Praktikum Bioinformatics 2022 (Project #3) and Master's Thesis
Web server for prediction of cryptic binding sites
miRNA target identification in non-model species
Library for prediction of cryptic binding sites
Ligand-binding site classification with deep graph neural networks.
Discover transcription factor (TF) binding specificities/sites (TFBS) using binding site motif sequence and structural information.
A python toolkit for analysing membrane protein-lipid interactions.
Toolkit for integrative analysis RNA's functional sites
Prediction of binding residues for metal ions, nucleic acids, and small molecules.
Pytorch implementation of BionoiNet, which is a deep learning-based software to classify ligand-binding sites.
Code for paper titled, "BSite-pro: A Novel Approach for Binding Site Prediction in Protein Sequences".
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