POST-DOC in Bioinformatics Laboratory
City: Warsaw
Deadline for applications: November 21th, 2026 r.
Expected date of competition results announcement: December 2026 r.
Expected starting date: January 2027
Procedure ID: PBI.111.1.2026
This position is related to the National Science Center (NCN) research project of identification of proteolytic cleavage sites in peptide substrates using coarse-grained docking and machine learning methods
Project description:
Proteolytic enzymes (proteases) play a key role in numerous biological processes, such as protein activation and degradation, digestion, and apoptosis. Dysregulation of their activity is associated with numerous diseases, making proteases important therapeutic targets. Accurate identification of substrate cleavage sites is essential for understanding enzyme specificity and designing novel inhibitors. Currently available methods for predicting cleavage sites rely primarily on the analysis of substrate amino acid sequences, largely neglecting the structural and dynamic aspects of enzyme–substrate interactions.
The aim of this project is to develop a novel method for identifying cleavage sites in peptide substrates by integrating structural modeling of enzyme–substrate complexes with machine learning algorithms.
During the project, large datasets of enzyme–peptide complex models will be generated for both peptides that are cleaved by a given enzyme and those that are not. These models, capturing subtle differences in the binding modes of cleaved and non-cleaved substrates, will subsequently be used to train a scoring function using machine learning methods. Cleavage sites in a given peptide will be identified through structural analysis of the generated enzyme–substrate complex model using an appropriately parameterized scoring function. Preliminary studies conducted for pepsin, renin, and HIV-1 protease have confirmed the effectiveness of this approach in predicting cleavage sites and revealed differences in the binding modes of cleaved and non-cleaved substrates.
The developed method will provide a better understanding of the mechanisms of substrate recognition and protease specificity, as well as help identify structural factors influencing enzymatic activity.
Responsibilities will include:
- conducting an extensive literature review and collecting data on enzyme-substrate systems to support the creation of a benchmark dataset for enzymatic cleavage site prediction;
- assessing the performance of machine-learning-based tools for protein structure prediction;
- implementing a scoring method trained on models of enzyme-substrate complexes;
- developing a computational pipeline for cleavage site prediction in peptide substrate;
- contributing to the preparation of scientific publications;
- designing and developing Dock-Cleave, a web service dedicated to the prediction of enzymatic cleavage sites in peptide substrates;
- presenting research findings at scientific conferences.
Required qualifications:
- PhD in Chemistry, Biology, Computational Sciences, or a related field;
- strong expertise in molecular modeling methods, including comparative modeling, molecular dynamics simulations, ligand docking, and protein structure prediction;
- documented experience with the CABS model and machine learning approaches for protein structure prediction (e.g., AlphaFold-Multimer, AlphaFold3);
- proficiency in one or more molecular visualization tools (e.g., VMD, PyMOL, Chimera, YASARA);
- background in protein modeling, enzymology and proteolysis, sequence alignment, and ligand docking;
- excellent command of spoken and written English;
- good programming skills in at least one of the following languages: Python or C++;
- documented scientific achievements, including peer-reviewed publications (JCR-listed) and conference abstracts.
Preferred qualifications:
- experience using AMBER for molecular dynamics simulations;
- scientific publications in the field of molecular modeling;
- experience with molecular modeling tools such as Modeller, ClustalW, MOE, CABS-dock, AutoDock, or GOLD.
We offer:
- employment at one of the leading scientific institutions in Poland;
- employment contract (full-time) for fixed term (ca 2 years);
- salary in line with with National Science Center (NCN) rules;
- access to specialist equipment in the Institute;
- additional support from the social fund (i.e. support in difficult life situations, childcare subsidies and low interest loans);
- possibility to join Employee Capital Plans (PPK);
- additional benefits such as opportunity to join a group life insurance program, medical service package, and a Multisport card on preferential terms.
Application:
Applicants should send the following documents:
- CV, including a list of scientific publications and research achievements;
- motivation letter;
- copy of Ph.D. diploma;
- contact details of at least one academic referee.
Apply via email to This email address is being protected from spambots. You need JavaScript enabled to view it. Please use the subject line: Procedure ID: PBI.111.1.2026
Application deadline: November, 21th 2026
We reserve the right to invite only selected candidates for interviews. Applications received after the deadline will not be considered. Recruitment interviews will be conducted online.
For further information, please contact dr hab. Michał Koliński (This email address is being protected from spambots. You need JavaScript enabled to view it.)
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