CLP-based protein fragment assembly

The paper investigates a novel approach, based on Constraint Logic Programming (CLP), to predict the 3D conformation of a protein via fragments assembly. The fragments are extracted by a preprocessor-also developed for this work- from a database of known protein structures that clusters and classifies the fragments according to similarity and frequency. The problem of assembling fragments into a complete conformation is mapped to a constraint solving problem and solved using CLP. The constraint-based model uses a medium discretization degree Ca-side chain centroid protein model that offers efficiency and a good approximation for space filling. The approach adapts existing energy models to the protein representation used and applies a large neighboring search strategy. The results shows the feasibility and efficiency of the method. The declarative nature of the solution allows to include future extensions, e.g., different size fragments for better accuracy.Comment: special issue dedicated to ICLP 201

A hybrid approach to protein folding problem integrating constraint programming with local search

<p>Abstract</p> <p>Background</p> <p>The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.</p> <p>Results</p> <p>Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.</p> <p>Conclusion</p> <p>Constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.</p

Characterization of Proteostasis Mechanisms in Chlamydia trachomatis

Chlamydia trachomatis is a gram negative, obligate intracellular pathogen with a highly reduced genome of ~1 Mbp. It is the leading cause of the reportable sexually transmitted infection known as chlamydia in the United States and is the leading cause of preventable blindness (trachoma) worldwide. While treatment of infections is possible, weaknesses of current approaches include treatment failure, antibiotic-induced dysbiosis, and resistance development of bystander bacteria during chlamydial treatment. These weaknesses support the need for improved therapeutic approaches. C. trachomatis undergoes a biphasic developmental cycle with two forms, the infectious elementary body (EB) and replicative reticulate body (RB), that have unique protein profiles. Due to the differing proteomes of each developmental form, we hypothesized that mechanisms that facilitate protein turnover will be essential for progression of C. trachomatis through the developmental cycle making them ideal drug targets. This study focused on characterization of two caseinolytic protease (Clp) systems: the ClpX/P2/P1 system and the ClpC/P1/P2 / McsAB system. We predicted that ClpP1 and ClpP2 come together to form the proteolytic component, that ClpX and ClpC are unfoldases that unfold and linearize large substrates in an ATP dependent manner for ClpP-dependent proteolysis, and that McsAB are adaptor proteins with McsA activating the kinase McsB to tag proteins for degradation by the ClpC/P1/P2 complex. The Clp system has been the focus of numerous studies as a target for novel antimicrobials and we hypothesized that the chlamydial Clp system would also be a druggable target. To assess the functionality of the Clp system, we successfully purified all components except McsB for use in vitro assays. Using oligomerization, peptide and protein degradation assays, and ATP hydrolysis assays, we characterized the activity of the ClpP1, ClpP2, and ClpX components individually and in complexes. We also measured the activity of a collection of ClpX mutants. In addition, we assessed the activity of ClpP-targeted activating compounds that were potent in vivo inhibitors of C. trachomatis. We demonstrated that ClpP2/P1 can form hetero-oligomers and degrade peptides and that ClpX has ATPase activity, can oligomerize, and can degrade an SsrA-tagged GFP when complexed with ClpP2/P1. While the activator studies did not support interactions with ClpP2/P1 under the conditions tested, assays were developed for further analysis of Clp-targeted compounds. Our in vitro results support that C. trachomatis possesses a functional Clp system. In addition, in vivo expression of ClpX mutants confirmed to lack activity in our in vitro assays led to reduced chlamydial fitness and alterations in development supporting our hypothesis that the Clp system is required for chlamydial development. Collectively, our results indicate that the Clp system is critical to C. trachomatis survival in cells and suggests that drugs altering Clp-function could be a novel approach for anti-chlamydial therapeutics

A Constraint Solver for Flexible Protein Models

This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geometric properties. The emphasis of this investigation is the use of multi-body systems to model native conformations of protein structures---where each body represents an entity of the protein (e.g., an amino acid, a small peptide) and the geometric constraints are related to the spatial properties of the composing atoms. The paper explores the use of the proposed class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction. The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems

Proteome and Interactome Linked to Metabolism, Genetic Information Processing, and Abiotic Stress in Gametophytes of Two Woodferns

Ferns and lycophytes have received scant molecular attention in comparison to angiosperms. The advent of high-throughput technologies allowed an advance towards a greater knowledge of their elusive genomes. In this work, proteomic analyses of heart-shaped gametophytes of two ferns were performed: the apomictic Dryopteris affinis ssp. affinis and its sexual relative Dryopteris oreades. In total, a set of 218 proteins shared by these two gametophytes were analyzed using the STRING database, and their proteome associated with metabolism, genetic information processing, and responses to abiotic stress is discussed. Specifically, we report proteins involved in the metabolism of carbohydrates, lipids, and nucleotides, the biosynthesis of amino acids and secondary compounds, energy, oxide-reduction, transcription, translation, protein folding, sorting and degradation, and responses to abiotic stresses. The interactome of this set of proteins represents a total network composed of 218 nodes and 1792 interactions, obtained mostly from databases and text mining. The interactions among the identified proteins of the ferns D. affinis and D. oreades, together with the description of their biological functions, might contribute to a better understanding of the function and development of ferns as well as fill knowledge gaps in plant evolution

MOLECULAR BASES OF SVP REGULATORY FUNCTIONS IN ARABIDOPSIS THALIANA

Flowering time regulation has a strong impact on plant life cycle, since it allows plants to flower and to reproduce under environmental permissive conditions. Several genes are involved in the regulatory pathways that determine the floral transition step, i.e. the switch from the plant vegetative phase to the reproductive phase and the consequent flower formation and fruit set. Among those genes, SHORT VEGETATIVE PHASE (SVP), a MADS box transcription factor, acts as strong repressor of the so called florigen promoting genes, FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1). Moreover, SVP has been also reported to act as a repressor of flower homeotic gene expression, thus ensuring the correct maintenance of floral meristem identity. Due to the relevance of SVP in both such important plant developmental stages, during my Ph.D. research program I tried to elucidate the molecular mechanisms at the basis of SVP activities. That has been done through different and complementary strategies that had the dual aim to identify SVP protein partners and to move the first steps towards the comprehension of the role of chloroplasts and chloroplast-nucleus signaling pathways in SVP functions. Co-immunoprecipitation assays followed by Mass Spectrometry analyses have allowed to draw up a list of Arabidopsis putative robust SVP interactors involved, at different levels, in chromatin organization and histone modification. Interestingly, the detailed characterization of the major Arabidopsis trimethyltransferase enzyme, SET DOMAIN GROUP 2 (SDG2), has revealed the existence of an SVP-SDG2 containing protein complex able to regulate the expression of SVP gene at the vegetative and reproductive meristems, by affecting the H3K4 methylation pattern within the first exon of SVP. Furthermore, our interests on the role of chloroplast-nucleus communication and its possible interactions with the flowering time regulation, have been met through the detailed characterization of two chloroplast-located PENTATRICO-PEPTIDE-REPEAT (PPR) containing proteins, which share three main features: i) they are part of the chloroplast gene expression machinery, ii) they are involved in chloroplast-nucleus communication, iii) they have been reported to be target genes of SVP by ChiP-seq assays. The detailed characterization of the Arabidopsis PPR proteins, GENOME UNCOUPLED 1 (GUN1) and CHLOROPLAST RNA PROCESSING 1 (AtCRP1), has provided the first preliminary insights into how chloroplast-nucleus signaling mechanisms may enable higher plants to more effectively adapt to the ever-changing internal and external conditions and mitigate detrimental effects to fitness

Modeling growth and adaptation in bacteria

Bakterielle Wirte wie Escherichia coli dienen der Produktion industrieller rekombinanter Proteine. Dieser Prozess verursacht systemischen Stress und führt zu umfangreichen Veränderungen in mRNA- und Proteinexpression. In meiner Arbeit analysiere ich Regulationsmechanismen der zellulären Reaktion auf diesen Stress. Zudem untersuche ich die zelluläre Ressourcenallokation mittels eines stationären Ganzzellmodells von E. coli, basierend auf der Resource Balance Analysis. Das Modell berücksichtigt Kosten zellulärer Prozesse und Einschränkungen wie Energie, Effizienz und Raum. Es unterstützt die Experimentplanung in der Bioproduktion. Weiterhin habe ich an der Entwicklung von RBApy mitgewirkt, einer Software zur Erstellung und Simulation von RBA-Modellen. Schließlich entwickle ich ein Modell zur Untersuchung der Regulation von Stressreaktionen durch die Tendenz der Zelle, wachstumsoptimale Ressourcenstrategien anzuwenden. Das Modell berücksichtigt zelluläre Beschränkungen und zeigt, dass die erhaltene Stressreaktion der experimentell ermittelten Reaktion ähnelt. Die Integration von Ressourcenzuteilung in Zellmodelle ermöglicht Einsichten in regulatorische Ereignisse und Anpassungen während der Bioproduktion, was zur Optimierung der rekombinanten Proteinexpression in Escherichia coli beiträgt.Bacterial hosts such as Escherichia coli are used for the production of industrial recombinant proteins. This process causes systemic stress and leads to extensive changes in mRNA and protein expression. In my work, I analyze regulatory mechanisms of the cellular response to this stress. In addition, I investigate cellular resource allocation using a steady-state whole-cell model of E. coli based on resource balance analysis. The model accounts for costs of cellular processes and constraints such as energy, efficiency, and space. It supports experiment design in bioproduction. Furthermore, I contributed to the development of RBApy, a software to create and simulate RBA models. Finally, I developed a model to study the regulation of stress responses by the tendency of the cell to adopt growth-optimal resource strategies. The model accounts for cellular constraints and shows that the obtained stress response resembles the experimentally determined response. Integrating resource allocation into cell models provides insights into regulatory events and adaptations during bioproduction, which contributes to the optimization of recombinant protein expression in Escherichia coli