Department Bionanoscience
Principal investigator Nikolina Sostaric
E-mail address n.sostaric@tudelft.nl
Website https://nikolinasostaric.github.io/
The dark interactome
Supervisor: Stefan Loonen, s.loonen-2@tudelft.nl
Protein electron carriers (PEC) are ubiquitous proteins that mediate the electron transfer between small-molecule electron carrier pools and redox-dependent enzymes. Generally an organism contains few PECs (f.e. ferredoxin or flavodoxin) which serve many different enzymes. However, which enzyme gets served by which PEC is not well characterized. We want to investigate what the potential PEC-enzyme pairs are in E. coli, through the use of various computational techniques. The first step will be identifying potential pairs through structure prediction techniques and protein-protein interaction tools such as Alphafold and Foldseek. Depending on the performance of these methods we can further characterize potential PEC-enzyme pairs through machine learning or molecular dynamics based approaches.
Techniques
- Bioinformatics
- Coding
- Protein structure predictions
- Molecular dynamics simulations
- Using DelftBlue supercomputing cluster
Further reading
Xian and Wang 2024
Simulating properties of elastin-like polypeptides
Supervisor: Stefan Loonen, s.loonen-2@tudelft.nl & Christine Visser, c.m.visser-1@tudelft.nl
Biomaterials are engineered to interact with biological systems, offering significant potential in medicine and nutrition. However, many rely on animal-derived proteins, which limit tunability and raise ethical concerns. This project focuses on sustainably producing natural and engineered proteins like collagen and elastin—key components of the extracellular matrix (ECM)—to develop biomaterials for soft tissue repair and cell-based meat. Collagen provides strength and flexibility, while elastin adds elasticity. To replicate elastin, scientists design elastin-like polypeptides (ELPs), which transition from soluble to insoluble at specific temperatures, crucial for biomedical use. Using molecular dynamics (MD) simulations, we’ll model ELP designs by altering amino acids, linkers, and conditions, identifying promising candidates for further experimental studies.
Techniques
- Molecular dynamics simulations
- Coding
- Usage of DelftBlue supercomputing cluster
Further reading
Barreiro et al. Biomacromolecules 2023, 24, 489−501
Dynamics of photoreactive proteins
Supervisor: Nikolina Sostaric, n.sostaric@tudelft.nl & Beatriz Orozco Monroy, b.e.orozcomonroy@tudelft.nl
DNA segregation is one of the requirements in building a synthetic cell. To experimentally construct a DNA segregation system for synthetic cells, we are utilizing bacterial proteins known for plasmid segregation fused with photoreactive proteins to enable light-controlled segregation processes
In this project, we would like to acquire a better understanding of the dynamics of these photoreactive proteins and their synthetic constructs, as well as their interactions with DNA. More specifically, we would do 3D modelling and perform molecular dynamics simulations of different LOV2 (light, oxygen, or voltage) domain constructs.
Techniques
- Molecular dynamics simulations
- Protein structure prediction
- Coding
- Usage of supercomputing cluster
Further reading
Peter, E., Dick, B. & Baeurle, S. Mechanism of signal transduction of the LOV2-Jα photosensor from Avena sativa. Nat Commun 1, 122 (2010). https://doi.org/10.1038/ncomms1121
Clustering data based on activation profiles
Supervisor: Nikolina Sostaric, n.sostaric@tudelft.nl & Marianne Bauer, m.s.bauer@tudelft.nl
We have previously used the information bottleneck algorithm to cluster stationary data and identify groups of protein inputs that fulfill the same function. The goal here is to develop a clustering algorithm based on the number of RNAs in different conditions; this algorithm would need to cluster RNAs that behave similarly across conditions. We would like to see if one can develop an algorithm based on the conditional entropy in this data. This project will rely heavily on analytics and numerics.
Techniques
- Python coding
- Clustering
- Analytics
- Statistics