Secretion of the Intimin Passenger Domain Is Driven by Protein Folding.

Intimin is an essential adhesin of attaching and effacing organisms such as entropathogenic Escherichia coli It is also the prototype of type Ve secretion or inverse autotransport, where the extracellular C-terminal region or passenger is exported with the help of an N-terminal transmembrane ß-barrel domain. We recently reported a stalled secretion intermediate of intimin, where the passenger is located in the periplasm but the ß-barrel is already inserted into the membrane. Stalling of this mutant is due to the insertion of an epitope tag at the very N terminus of the passenger. Here, we examined how this insertion disrupts autotransport and found that it causes misfolding of the N-terminal immunoglobulin (Ig)-like domain D00. We could also stall the secretion by making an internal deletion in D00, and introducing the epitope tag into the second Ig-like domain, D0, also resulted in reduced passenger secretion. In contrast to many classical autotransporters, where a proximal folding core in the passenger is required for secretion, the D00 domain is dispensable, as the passenger of an intimin mutant lacking D00 entirely is efficiently exported. Furthermore, the D00 domain is slightly less stable than the D0 and D1 domains, unfolding at ∼200 piconewtons (pN) compared with ∼250 pN for D0 and D1 domains as measured by atomic force microscopy. Our results support a model where the secretion of the passenger is driven by sequential folding of the extracellular Ig-like domains, leading to vectorial transport of the passenger domain across the outer membrane in an N to C direction.

Type V Secretion Systems in Bacteria.

Type V secretion denotes a variety of secretion systems that cross the outer membrane in Gram-negative bacteria but that depend on the Sec machinery for transport through the inner membrane. They are possibly the simplest bacterial secretion systems, because they consist only of a single polypeptide chain (or two chains in the case of two-partner secretion). Their seemingly autonomous transport through the outer membrane has led to the term "autotransporters" for various subclasses of type V secretion. In this chapter, we review the structure and function of these transporters and review recent findings on additional factors involved in the secretion process, which have put the term "autotransporter" to debate.

Deciphering the signaling mechanisms of the plant cell wall degradation machinery in Aspergillus oryzae.

BACKGROUND: The gene expression and secretion of fungal lignocellulolytic enzymes are tightly controlled at the transcription level using independent mechanisms to respond to distinct inducers from plant biomass. An advanced systems-level understanding of transcriptional regulatory networks is required to rationally engineer filamentous fungi for more efficient bioconversion of different types of biomass. RESULTS: In this study we focused on ten chemically defined inducers to drive expression of cellulases, hemicellulases and accessory enzymes in the model filamentous fungus Aspergillus oryzae and shed light on the complex network of transcriptional activators required. The chemical diversity analysis of the inducers, based on 186 chemical descriptors calculated from the structure, resulted into three clusters, however, the global, metabolic and extracellular protein transcription of the A. oryzae genome were only partially explained by the chemical similarity of the enzyme inducers. Genes encoding enzymes that have attracted considerable interest such as cellobiose dehydrogenases and copper-dependent polysaccharide mono-oxygenases presented a substrate-specific induction. Several homology-model structures were derived using ab-initio multiple threading alignment in our effort to elucidate the interplay of transcription factors involved in regulating plant-deconstructing enzymes and metabolites. Systematic investigation of metabolite-protein interactions, using the 814 unique reactants involved in 2360 reactions in the genome scale metabolic network of A. oryzae, was performed through a two-step molecular docking against the binding pockets of the transcription factors AoXlnR and AoAmyR. A total of six metabolites viz., sulfite (H2SO3), sulfate (SLF), uroporphyrinogen III (UPGIII), ethanolamine phosphate (PETHM), D-glyceraldehyde 3-phosphate (T3P1) and taurine (TAUR) were found as strong binders, whereas the genes involved in the metabolic reactions that these metabolites appear were found to be significantly differentially expressed when comparing the inducers with glucose. CONCLUSIONS: Based on our observations, we believe that specific binding of sulfite to the regulator of the cellulase gene expression, AoXlnR, may be the molecular basis for the connection of sulfur metabolism and cellulase gene expression in filamentous fungi. Further characterization and manipulation of the regulatory network components identified in this study, will enable rational engineering of industrial strains for improved production of the sophisticated set of enzymes necessary to break-down chemically divergent plant biomass.

Multiple nucleophilic elbows leading to multiple active sites in a single module esterase from Sorangium cellulosum.

The catalytic residues in carbohydrate esterase enzyme families constitute a highly conserved triad: serine, histidine and aspartic acid. This catalytic triad is generally located in a very sharp turn of the protein backbone structure, called the nucleophilic elbow and identified by the consensus sequence GXSXG. An esterase from Sorangium cellulosum Soce56 that contains five nucleophilic elbows was cloned and expressed in Escherichia coli and the function of each nucleophilic elbowed site was characterized. In order to elucidate the function of each nucleophilic elbow, site directed mutagenesis was used to generate variants with deactivated nucleophilic elbows and the functional promiscuity was analyzed. In silico analysis together with enzymological characterization interestingly showed that each nucleophilic elbow formed a local active site with varied substrate specificities and affinities. To our knowledge, this is the first report presenting the role of multiple nucleophilic elbows in the catalytic promiscuity of an esterase. Further structural analysis at protein unit level indicates the new evolutionary trajectories in emerging promiscuous esterases.

Targeted metabolic engineering guided by computational analysis of single-nucleotide polymorphisms (SNPs).

The non-synonymous SNPs, the so-called non-silent SNPs, which are single-nucleotide variations in the coding regions that give "birth" to amino acid mutations, are often involved in the modulation of protein function. Understanding the effect of individual amino acid mutations on a protein/enzyme function or stability is useful for altering its properties for a wide variety of engineering studies. Since measuring the effects of amino acid mutations experimentally is a laborious process, a variety of computational methods have been discussed here that aid to extract direct genotype to phenotype information.

Common and distant structural characteristics of feruloyl esterase families from Aspergillus oryzae.

BACKGROUND: Feruloyl esterases (FAEs) are important biomass degrading accessory enzymes due to their capability of cleaving the ester links between hemicellulose and pectin to aromatic compounds of lignin, thus enhancing the accessibility of plant tissues to cellulolytic and hemicellulolytic enzymes. FAEs have gained increased attention in the area of biocatalytic transformations for the synthesis of value added compounds with medicinal and nutritional applications. Following the increasing attention on these enzymes, a novel descriptor based classification system has been proposed for FAEs resulting into 12 distinct families and pharmacophore models for three FAE sub-families have been developed. METHODOLOGY/PRINCIPAL FINDINGS: The feruloylome of Aspergillus oryzae contains 13 predicted FAEs belonging to six sub-families based on our recently developed descriptor-based classification system. The three-dimensional structures of the 13 FAEs were modeled for structural analysis of the feruloylome. The three genes coding for three enzymes, viz., A.O.2, A.O.8 and A.O.10 from the feruloylome of A. oryzae, representing sub-families with unknown functional features, were heterologously expressed in Pichia pastoris, characterized for substrate specificity and structural characterization through CD spectroscopy. Common feature-based pharamacophore models were developed according to substrate specificity characteristics of the three enzymes. The active site residues were identified for the three expressed FAEs by determining the titration curves of amino acid residues as a function of the pH by applying molecular simulations. CONCLUSIONS/SIGNIFICANCE: Our findings on the structure-function relationships and substrate specificity of the FAEs of A. oryzae will be instrumental for further understanding of the FAE families in the novel classification system. The developed pharmacophore models could be applied for virtual screening of compound databases for short listing the putative substrates prior to docking studies or for post-processing docking results to remove false positives. Our study exemplifies how computational predictions can complement to the information obtained through experimental methods.

How well do the substrates KISS the enzyme? Molecular docking program selection for feruloyl esterases.

Molecular docking is the most commonly used technique in the modern drug discovery process where computational approaches involving docking algorithms are used to dock small molecules into macromolecular target structures. Over the recent years several evaluation studies have been reported by independent scientists comparing the performance of the docking programs by using default 'black box' protocols supplied by the software companies. Such studies have to be considered carefully as the docking programs can be tweaked towards optimum performance by selecting the parameters suitable for the target of interest. In this study we address the problem of selecting an appropriate docking and scoring function combination (88 docking algorithm-scoring functions) for substrate specificity predictions for feruloyl esterases, an industrially relevant enzyme family. We also propose the 'Key Interaction Score System' (KISS), a more biochemically meaningful measure for evaluation of docking programs based on pose prediction accuracy.

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases.

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs.

Yeast biological networks unfold the interplay of antioxidants, genome and phenotype, and reveal a novel regulator of the oxidative stress response.

BACKGROUND: Identifying causative biological networks associated with relevant phenotypes is essential in the field of systems biology. We used ferulic acid (FA) as a model antioxidant to characterize the global expression programs triggered by this small molecule and decipher the transcriptional network controlling the phenotypic adaptation of the yeast Saccharomyces cerevisiae. METHODOLOGY/PRINCIPAL FINDINGS: By employing a strict cut off value during gene expression data analysis, 106 genes were found to be involved in the cell response to FA, independent of aerobic or anaerobic conditions. Network analysis of the system guided us to a key target node, the FMP43 protein, that when deleted resulted in marked acceleration of cellular growth (∼15% in both minimal and rich media). To extend our findings to human cells and identify proteins that could serve as drug targets, we replaced the yeast FMP43 protein with its human ortholog BRP44 in the genetic background of the yeast strain Δfmp43. The conservation of the two proteins was phenotypically evident, with BRP44 restoring the normal specific growth rate of the wild type. We also applied homology modeling to predict the 3D structure of the FMP43 and BRP44 proteins. The binding sites in the homology models of FMP43 and BRP44 were computationally predicted, and further docking studies were performed using FA as the ligand. The docking studies demonstrated the affinity of FA towards both FMP43 and BRP44. CONCLUSIONS: This study proposes a hypothesis on the mechanisms yeast employs to respond to antioxidant molecules, while demonstrating how phenome and metabolome yeast data can serve as biomarkers for nutraceutical discovery and development. Additionally, we provide evidence for a putative therapeutic target, revealed by replacing the FMP43 protein with its human ortholog BRP44, a brain protein, and functionally characterizing the relevant mutant strain.