About Ligand-binding site characterization through machine learning, PhD - at Faculty of Science, Charles University

Ligand binding is one of the most common and most important functions proteins have evolved to accomplish. Understanding how proteins bind small ligands is useful in designing better enzymes or developing new drugs. Recent advances in biological data acquisition (sequencing, structure determination methods etc.) have brought millions of sequences that need to be annotated and an identification of potential ligand-binding sites and their descriptions are among the annotations that are often attempted.

Our structural bioinformatics group (CUSBG) has developed a ligand-binding site prediction tool P2Rank (https://prankweb.cz/) that is used as a state-of-the-art tool used worldwide. However, P2Rank so far provides only limited descriptions of predicted binding sites. We would like to use a wealth of existing experimental data to study similarities among predicted binding sites to find out which ligands could bind the predicted binding site. We also aim to combine information from multiple structures of a given protein to improve our descriptions. The project will combine advanced machine learning approaches with careful examining of available biological data and its filtering. An oversight over machine learning part of the project will be provided by David Hoksza from the Faculty of Mathematics and Physics, Charles University.

Five relevant publications of the research group:

Jakubec D, Skoda P, Krivak R, Novotny M, Hoksza D. (2022). PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures. Nucleic Acids Research, 50 W1 W593-W597.

Jendele, L., Krivak, R., Skoda, P., Novotny, M. & Hoksza, D. (2019). PrankWeb: a web server for ligand binding site prediction and visualization. Nucleic Acids Research 47(W1), W345-W349.

Feidakis CP, Krivak R, Hoksza D, Novotny M. (2022). AHoJ: rapid, tailored search and retrieval of apo and holo protein structures for user-defined ligands. Bioinformatics 38(24):5452-5453. doi: 10.1093/bioinformatics/btac701.

Quaglia F, M

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