Modern tools, such as CRISPR, have revolutionized genome editing. However, when it comes to their application in living organisms, their molecular size and editing activity can be limiting. The goal of this project is to engineer novel genome editors to overcome these limitations.
The candidate will use molecular biology and biochemistry to design and evaluate an array of new genome editing tools. Primarily, new hypercompact CRISPR systems will be used as a basis to engineer fusions with enzymatic domains that facilitate next generation applications, such as prime editing. Additionally, novel enzymes with nucleic acid modifying activities will be characterized and utilized for genome editing. To understand molecular mechanisms of novel enzymes, the PhD candidate will employ methods of structural biology (cryoEM), biochemistry and molecular biology.

The proposed PhD thesis addresses the challenges arising from the explosive growth of sequence and structure databases resulting from recent breakthroughs in next-generation sequencing and structural bioinformatics. The vast amount of biological data presents opportunities for transformative genomics and proteomics analyses, but the bottleneck lies in the efficient handling of these massive datasets within practical time frames. This research aims to develop cutting-edge algorithms and software leveraging the computational power of modern processors and graphics accelerators. The doctoral candidate will focus on creating high-performance solutions for encoding protein and nucleic acid sequences, conducting similarity searches in sequence and structure spaces, performing structure docking, and addressing other large-scale downstream tasks. The outcomes of this study are anticipated to contribute to advancements in several research domains, including structure-based drug discovery, offering novel computational tools for the broader biological research community.

Crystal structures are the richest source of information about materials that allows us to analyse material properties and structure-property relations. Understanding regularities of crystal structures would give us a tool for rational material design, opening new possibilities to invent new drugs, functional materials and materials with required properties. Of especial interest are materials with knotted or interpenetrating covalent bond nets, since the properties of such materials are defined not only by the covalent bond network itself but also the topological nature of the knots that they form. Such materials could yield new molecular machines, new types of biologically active substances, new materials for electronics and computer industry.
Unfortunately, at the moment there are no methods that would enable detection of such materials in crystal structures. One of the main difficulties is the absence of reliable knot invariants, i.e. the mathematical expressions that would guarantee that the knots, links or nets with the same invariant are topologically equivalent. As a consequence there are no reliable algorithms to detect such structures in crystallographic databases.
We thus propose to investigate a possibility to apply the known knot invariants to crystallographic net analysis, taking into account the specifics of the crystal structures (e.g. the finite size of the atoms and the low number of crossings of knots that the molecules form). The main goal would be to create an algorithm for discovery of knots, links and interpenetrating nets in crystal structures and to build a database of such structures.

Deep learning-based methods are nowadays widely applied for different tasks. However, application of these methods in modeling and prediction of the properties of chemical molecules has been less successful compared to other fields. This can be attributed both to the lack of data on chemical molecules, that could be used for model training, and to the imperfection of the employed molecular representations and deep learning methods. Aiming to tackle these problems, in the PhD project we propose to develop novel deep learning methods for the analysis of chemical molecules and prediction of their properties.
Applicants are expected to have either good knowledge and experience in chemistry/biochemistry, or skills in machine learning and neural networks.

The visualization of single proteins through electron microscopy and using metal nanoparticles is mostly acchieved through immuno-gold nanoparticle precipitation, however there are technological limitation with this technology.
In this project, you will explore genetically encoded reporters for the visualization of single proteins in a cell-wide manner using electron microscopy and programmable nucleases. The project aims to create stable lines coding the genetically encoded reporters onto proteins of therapeutic interest and within human cell systems, including human iPS cells, and immune effector cells.
In our project, you will learn how to optimize differentiation protocols aided by genome engineering. You will create cellular master stocks of engineered cells, that could be used for mechanistic dissecting through EM visualization in close collaboration with partners. You will be hosted at the EMBL-VU Institute for Genome Editing Technologies. You will be supervised by Dr. Jonathan Arias (Group Leader), expert in genome engineering and iPS cell platforms. He will guide you to create scientific articles and patents with commercial potential. Learn more about us at https://www.gmc.vu.lt/en/group-of-cell-therapeutics.

Animal sensory systems exhibit high degree of variation that is subtly tuned to the perceptual challenges posed by the conduct of specific tasks, especially foraging. Birds of prey have always fascinated mankind and have been under interest of researchers for their spectacular visual performance and some of the largest eyes on land. The fact that invasive investigations on large, charismatic and often protected raptors have always been uncommon, and are even less usual today, is one of the reasons for the scarce knowledge on the visual system of birds of prey.
However, a comparison between diurnal and nocturnal raptors has revealed some of the adaptations allowing for the extraordinary performance of visually guided behaviours and has illustrated evolutionary functional divergence of morphologically similar large eyes of these birds - high visual acuity in diurnal and high absolute sensitivity in nocturnal species. Still, studies on raptor vision are rare and there are large knowledge gaps remaining, some of which could be filled by performing non-invasive behavioural experiments.
The proposed PhD project will investigate parameters of spatial and temporal vision of diurnal and nocturnal raptors using psychophysical methods. The project is planned in collaboration with the Lithuanian Association of Birds of Prey.