Wiktor Jurkowski

Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome.

The Polycomb repressive complexes PRC1 and PRC2 maintain embryonic stem cell (ESC) pluripotency by silencing lineage-specifying developmental regulator genes. Emerging evidence suggests that Polycomb complexes act through controlling spatial genome organization. We show that PRC1 functions as a master regulator of mouse ESC genome architecture by organizing genes in three-dimensional interaction networks. The strongest spatial network is composed of the four Hox gene clusters and early developmental transcription factor genes, the majority of which contact poised enhancers. Removal of Polycomb repression leads to disruption of promoter-promoter contacts in the Hox gene network. In contrast, promoter-enhancer contacts are maintained in the absence of Polycomb repression, with accompanying widespread acquisition of active chromatin signatures at network enhancers and pronounced transcriptional upregulation of network genes. Thus, PRC1 physically constrains developmental transcription factor genes and their enhancers in a silenced but poised spatial network. We propose that the selective release of genes from this spatial network underlies cell fate specification during early embryonic development.

Integrating Pathways of Parkinson's Disease in a Molecular Interaction Map

Parkinsons disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map.

Community-wide assessment of GPCR structure modelling and ligand docking

Recent breakthroughs in the determination of the crystal structures of G protein-coupled receptors (GPCRs) have provided new opportunities for structure-based drug design strategies targeting this protein family. With the aim of evaluating the current status of GPCR structure prediction and ligand docking, a community-wide, blind prediction assessment — GPCR Dock 2008 — was conducted in coordination with the publication of the crystal structure of the human adenosine A2A receptor bound to the ligand ZM241385. Twenty-nine groups submitted 206 structural models before the release of the experimental structure, which were evaluated for the accuracy of the ligand binding mode and the overall receptor model compared with the crystal structure. This analysis highlights important aspects for success and future development, such as accurate modelling of structurally divergent regions and use of additional biochemical insight such as disulphide bridges in the extracellular loops.

Prediction of functional sites based on the fuzzy oil drop model

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins. This paper describes a computational model that can be used to identify potential areas that are able to interact with other molecules (ligands, substrates, inhibitors, etc.). The model for active site recognition is based on the analysis of hydrophobicity distribution in protein molecules. It is shown, based on the analyses of proteins with known biological activity and of proteins of unknown function, that the region of significantly irregular hydrophobicity distribution in proteins appears to be function related.

Analysis of ELP4, SRPX2, and interacting genes in typical and atypical rolandic epilepsy

Rolandic epilepsy (RE) and its atypical variants (atypical rolandic epilepsy, ARE) along the spectrum of epilepsy–aphasia disorders are characterized by a strong but largely unknown genetic basis. Two genes with a putative (ELP4) or a proven (SRPX2) function in neuronal migration were postulated to confer susceptibility to parts of the disease spectrum: the ELP4 gene to centrotemporal spikes and SRPX2 to ARE. To reexamine these findings, we investigated a cohort of 280 patients of European ancestry with RE/ARE for the etiological contribution of these genes and their close interaction partners. We performed next‐generation sequencing and single‐nucleotide polymorphism (SNP)–array based genotyping to screen for sequence and structural variants. In comparison to European controls we could not detect an enrichment of rare deleterious variants of ELP4, SRPX2, or their interaction partners in affected individuals. The previously described functional p.N327S variant in the X chromosomal SRPX2 gene was detected in two affected individuals (0.81%) and also in controls (0.26%), with some preponderance of male patients. We did not detect an association of SNPs in the ELP4 gene with centrotemporal spikes as previously reported. In conclusion our data do not support a major role of ELP4 and SRPX2 in the etiology of RE/ARE.

Conformational subspace in simulation of early‐stage protein folding

A probability calculus was used to simulate the early stages of protein folding in ab initio structure prediction. The probabilities of particular ϕ and ψ angles for each of 20 amino acids as they occur in crystal forms of proteins were used to calculate the amount of information necessary for the occurrence of given ϕ and ψ angles to be predicted. It was found that the amount of information needed to predict ϕ and ψ angles with 5° precision is much higher than the amount of information actually carried by individual amino acids in the polypeptide chain. To handle this problem, a limited conformational space for the preliminary search for optimal polypeptide structure is proposed based on a simplified geometrical model of the polypeptide chain and on the probability calculus. These two models, geometric and probabilistic, based on different sources, yield a common conclusion concerning how a limited conformational space can represent an early stage of polypeptide chain‐folding simulation. The ribonuclease molecule was used to test the limited conformational space as a tool for modeling early‐stage folding. Proteins 2004.

Fuzzy oil drop model to interpret the structure of antifreeze proteins and their mutants

Mutations in proteins introduce structural changes and influence biological activity: the specific effects depend on the location of the mutation. The simple method proposed in the present paper is based on a two-step model of in silico protein folding. The structure of the first intermediate is assumed to be determined solely by backbone conformation. The structure of the second one is assumed to be determined by the presence of a hydrophobic center. The comparable structural analysis of the set of mutants is performed to identify the mutant-induced structural changes. The changes of the hydrophobic core organization measured by the divergence entropy allows quantitative comparison estimating the relative structural changes upon mutation. The set of antifreeze proteins, which appeared to represent the hydrophobic core structure accordant with “fuzzy oil drop” model was selected for analysis.

Early-Stage Folding in Proteins (In Silico) Sequence-to-Structure Relation

A sequence-to-structure library has been created based on the complete PDB database. The tetrapeptide was selected as a unit representing a well-defined structural motif. Seven structural forms were introduced for structure classification. The early-stage folding conformations were used as the objects for structure analysis and classification. The degree of determinability was estimated for the sequence-to-structure and structure-to-sequence relations. Probability calculus and informational entropy were applied for quantitative estimation of the mutual relation between them. The structural motifs representing different forms of loops and bends were found to favor particular sequences in structure-to-sequence analysis.

Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks

BackgroundCellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellular phenotypes, only few key genes, which are coined here as reprogramming determinants, are required to be directly perturbed in order to induce cellular reprogramming. However, identification of reprogramming determinants still remains a combinatorial problem, and the state-of-art methods addressing this issue rests on exhaustive experimentation or prior knowledge to narrow down the list of candidates.ResultsHere we present a computational method, without any preliminary selection of candidate genes, to identify reduced subsets of genes, which when perturbed can induce transitions between cellular phenotypes. The method relies on the expression profiles of two stable cellular phenotypes along with a topological analysis stability elements in the gene regulatory network that are necessary to cause this multi-stability. Since stable cellular phenotypes can be considered as attractors of gene regulatory networks, cell fate and cellular reprogramming involves transition between these attractors, and therefore current method searches for combinations of genes that are able to destabilize a specific initial attractor and stabilize the final one in response to the appropriate perturbations.ConclusionsThe method presented here represents a useful framework to assist researchers in the field of cellular reprogramming to design experimental strategies with potential applications in the regenerative medicine and disease modelling.

Lysozyme folded in silico according to the limited conformational sub-space

Abstract The conformational sub-space oriented on early-stage protein folding is applied to lysozyme folding. The part of the Ramachandran map distinguished on the basis of a geometrical model of the polypeptide chain limited to the mutual orientation of the peptide bond planes is shown to deliver the initial structure of the polypeptide for the energy minimization procedure in the ab initio model of protein folding prediction. Two forms of energy minimization and molecular dynamics simulation procedures were applied to the assumed early-stage protein folding of lysozyme. One of them included the disulphide bond system and the other excluded it. The post-energy-minimization and post-dynamics structures were compared using RMS-D and non-bonding contact maps to estimate the degree of approach to the native, target structure of the protein molecule obtained using the limited conformational sub-space for the early stage of folding.