High throughput prediction of inter-protein coevolution

Inter-protein co-evolution analysis can reveal in/direct functional or physical protein interactions. Inter-protein co-evolutionary analysis compares the correlation of evolutionary changes between residues on aligned orthologous sequences. On the other hand, modern methods used in experimental cell biological research to screen for protein-protein interaction, often based on mass spectrometry, often lead to identification of large amount of possible interacting proteins. If automatized, inter-protein co-evolution analysis can serve as a valuable step in refining the results, typically containing hundreds of hits, for further experiments. Manual retrieval of tens of orthologous sequences, alignment and phylogenetic tree preparations of such amounts of data is insufficient. The aim of this thesis is to create an assembly of scripts that automatize high-throughput inter-protein co-evolution analysis. Scripts were written in Python language. Scripts are using API client interface to access online databases with sequences of input protein identifiers. Through matched identifiers, over 85 representative orthologous sequences from vertebrate species are retrieved from OrthoDB orthologues database. Scripts align these sequences with PRANK MSA algorithm and create corresponding phylogenetic tree. All protein pairs are structured for multicore computation with CAPS programme on CSC supercomputer. Multiple CAPS outputs are abstracted into comprehensive form for comparison of relative co-adaptive co-evolution between proposed protein pairs. In this work, I have developed automatization for a protein-interactome screen done by proximity labelling of B cell receptor and plasma membrane associated proteins under activating or non-activating conditions. Applying high-throughput co-evolutionary analysis to this data provides a completely new approach to identify new players in B cell activation, critical for autoimmunity, hypo-immunity or cancer. Results showed unsatisfying performance of CAPS, explanation and alternatives were given

Identification of environmental stress biomarkers in seedlings of European beech (Fagus sylvatica) and Scots pine (Pinus sylvestris)

Climate development models predict alterations that will critically influence plant metabolism in southern and central Europe. Although the molecular players involved in the response to climatic stress factors have been well described in crops, little information is available for forest tree species. Consequently, the identification of molecular biomarkers suitable for evaluating the actual impact of different environmental stress conditions on forest plants would be of great importance for monitoring purposes and forest management. In this study, we evaluated a biochemical methodology for the assessment of temperature stress in European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.) seedlings by analyzing a set of metabolites and enzymes involved in free radical scavenging and cell wall synthesis. The results indicate that the combined analysis of the specific activities and isoform profile of peroxidases, superoxide dismutases, and glutathione peroxidases coupled with the amount variation of phenolic compounds enabled the discrimination between stressed and control seedlings. This approach represents a promising platform for the assessment of temperature stress in forest trees and could also enhance selection and breeding practices, allowing for plants more tolerant and (or) resistant to abiotic stress.This is peer-reviewed version of the following article: Popović, M.; Šuštar, V.; Gričar, J.; Štraus, I.; Torkar, G.; Kraigher, H.; de Marco, A. Identification of Environmental Stress Biomarkers in Seedlings of European Beech (Fagus Sylvatica) and Scots Pine (Pinus Sylvestris). Canadian Journal of Forest Research 2015, 46 (1), 58–66. [https://doi.org/10.1139/cjfr-2015-0274

B cells rapidly target antigen and surface-derived MHCII into peripheral degradative compartments

In order to mount high-affinity antibody responses, B cells internalise specific antigens and process them into peptides loaded onto MHCII for presentation to T helper cells (T H cells). While the biochemical principles of antigen processing and MHCII loading have been well dissected, how the endosomal vesicle system is wired to enable these specific functions remains much less studied. Here, we performed a systematic microscopy-based analysis of antigen trafficking in B cells to reveal its route to the MHCII peptide-loading compartment (MIIC). Surprisingly, we detected fast targeting of internalised antigen into peripheral acidic compartments that possessed the hallmarks of the MIIC and also showed degradative capacity. In these vesicles, intemalised antigen converged rapidly with membrane-derived MHCII and partially overlapped with cathepsin-S and H2-M, both required for peptide loading. These early compartments appeared heterogenous and atypical as they contained a mixture of both early and late endosomal markers, indicating a specialized endosomal route. Together, our data suggest that, in addition to in the previously reported perinuclear late endosomal MIICs, antigen processing and peptide loading could have already started in these specialized early peripheral acidic vesicles (eMlIC) to support fast peptide-MHCII presentation. This article has an associated First Person interview with the first author of the paper.Peer reviewe

3D CN-LSTM for prediction of medical nanoparticle properties

Cancer is an increasing and already one of the most common causes of deathin developed countries. One way to fight cancer tumours is with targeted anti-tumour drug delivering nanoparticles (NPs). NPs can be composed of goldcore covered with a variety of drug and supporting substances (SS) in varyingratios. Since chemical synthesis of all potential NPs is costly, to find the mostoptimal drug and drug-SS ratios out of many potential candidates, NPs aresimulated in silico in molecular dynamics (MD) simulations. To further lowerthe costs and expand coverage of potential optimal NP compositions, compu-tationally demanding MD simulations of NPs could in part be replaced withDeep Learning (DL) neural networks. Here the properties of NPs at laterstages of MD simulation would be predicted with DL from NP properties fromstarting stages of MD simulations. As MD simulations are time series and NPs simulated are 3D objects, onecan join two types of DL: recurrent neural networks (RNN) and convolutionalneural networks (CNN) to create a suitable DL network. The scope of thismaster’s thesis is running MD simulations, finding proper DL architecture forthe model, refining the input and assessing the predictions of the refined model. The architecture giving the best prediction of NP drug exposure is a com-bination of concatenated 3D CNN for NP structure input and dense layers forother types of input fed into Long Short-Term Memory (LSTM) RNN