Structure-Property Relationships of Elastin-like Polypeptides: A Review of Experimental and Computational Studies.

Elastin is a structural protein with outstanding mechanical properties (e.g., elasticity and resilience) and biologically relevant functions (e.g., triggering responses like cell adhesion or chemotaxis). It is formed from its precursor tropoelastin, a 60-72 kDa water-soluble and temperature-responsive protein that coacervates at physiological temperature, undergoing a phenomenon termed lower critical solution temperature (LCST). Inspired by this behavior, many scientists and engineers are developing recombinantly produced elastin-inspired biopolymers, usually termed elastin-like polypeptides (ELPs). These ELPs are generally comprised of repetitive motifs with the sequence VPGXG, which corresponds to repeats of a small part of the tropoelastin sequence, X being any amino acid except proline. ELPs display LCST and mechanical properties similar to tropoelastin, which renders them promising candidates for the development of elastic and stimuli-responsive protein-based materials. Unveiling the structure-property relationships of ELPs can aid in the development of these materials by establishing the connections between the ELP amino acid sequence and the macroscopic properties of the materials. Here we present a review of the structure-property relationships of ELPs and ELP-based materials, with a focus on LCST and mechanical properties and how experimental and computational studies have aided in their understanding.

Sequence Control of the Self-Assembly of Elastin-Like Polypeptides into Hydrogels with Bespoke Viscoelastic and Structural Properties.

The biofabrication of structural proteins with controllable properties via amino acid sequence design is interesting for biomedicine and biotechnology, yet a complete framework that connects amino acid sequence to material properties is unavailable, despite great progress to establish design rules for synthesizing peptides and proteins with specific conformations (e.g., unfolded, helical, ß-sheets, or ß-turns) and intermolecular interactions (e.g., amphipathic peptides or hydrophobic domains). Molecular dynamics (MD) simulations can help in developing such a framework, but the lack of a standardized way of interpreting the outcome of these simulations hinders their predictive value for the design of de novo structural proteins. To address this, we developed a model that unambiguously classifies a library of de novo elastin-like polypeptides (ELPs) with varying numbers and locations of hydrophobic/hydrophilic and physical/chemical-cross-linking blocks according to their thermoresponsiveness at physiological temperature. Our approach does not require long simulation times or advanced sampling methods. Instead, we apply (un)supervised data analysis methods to a data set of molecular properties from relatively short MD simulations (150 ns). We also experimentally investigate hydrogels of those ELPs from the library predicted to be thermoresponsive, revealing several handles to tune their mechanical and structural properties: chain hydrophilicity/hydrophobicity or block distribution control the viscoelasticity and thermoresponsiveness, whereas ELP concentration defines the network permeability. Our findings provide an avenue to accelerate the design of de novo ELPs with bespoke phase behavior and material properties.

Systems metabolic engineering in an industrial setting.

Systems metabolic engineering is based on systems biology, synthetic biology, and evolutionary engineering and is now also applied in industry. Industrial use of systems metabolic engineering focuses on strain and process optimization. Since ambitious yields, titers, productivities, and low costs are key in an industrial setting, the use of effective and robust methods in systems metabolic engineering is becoming very important. Major improvements in the field of proteomics and metabolomics have been crucial in the development of genome-wide approaches in strain and process development. This is accompanied by a rapid increase in DNA sequencing and synthesis capacity. These developments enable the use of systems metabolic engineering in an industrial setting. Industrial systems metabolic engineering can be defined as the combined use of genome-wide genomics, transcriptomics, proteomics, and metabolomics to modify strains or processes. This approach has become very common since the technology for generating large data sets of all levels of the cellular processes has developed quite fast into robust, reliable, and affordable methods. The main challenge and scope of this mini review is how to translate these large data sets in relevant biological leads which can be tested for strain or process improvements. Experimental setup, heterogeneity of the culture, and sample pretreatment are important issues which are easily underrated. In addition, the process of structuring, filtering, and visualization of data is important, but also, the availability of a genetic toolbox and equipment for medium/high-throughput fermentation is a key success factor. For an efficient bioprocess, all the different components in this process have to work together. Therefore, mutual tuning of these components is an important strategy.

Sample preparation and biostatistics for integrated genomics approaches.

Genomics is based on the ability to determine the transcriptome, proteome, and metabolome of a cell. These technologies only have added value when they are integrated and based on robust and reproducible workflows. This chapter describes the experimental design, sampling, sample pretreatment, data evaluation, integration, and interpretation. The actual generation of the data is not covered in this chapter since it is highly depended on available equipment and infrastructure. The enormous amount of data generated by these technologies are integrated and interpreted inorder to generate leads for strain and process improvement. Biostatistics are becoming very important for the whole work flow therefore, some general recommendations how to set up experimental design and how to use biostatistics in enhancing the quality of the data and the selection of biological relevant leads for strain engineering and target identification are described.

Peroxicretion: a novel secretion pathway in the eukaryotic cell.

BACKGROUND: Enzyme production in microbial cells has been limited to secreted enzymes or intracellular enzymes followed by expensive down stream processing. Extracellular enzymes consists mainly of hydrolases while intracellular enzymes exhibit a much broader diversity. If these intracellular enzymes could be secreted by the cell the potential of industrial applications of enzymes would be enlarged. Therefore a novel secretion pathway for intracellular proteins was developed, using peroxisomes as secretion vesicles. RESULTS: Peroxisomes were decorated with a Golgi derived v-SNARE using a peroxisomal membrane protein as an anchor. This allowed the peroxisomes to fuse with the plasma membrane. Intracellular proteins were transported into the peroxisomes by adding a peroxisomal import signal (SKL tag). The proteins which were imported in the peroxisomes, were released into the extra-cellular space through this artificial secretion pathway which was designated peroxicretion. This concept was supported by electron microscopy studies. CONCLUSION: Our results demonstrate that it is possible to reroute the intracellular trafficking of vesicles by changing the localisation of SNARE molecules, this approach can be used in in vivo biological studies to clarify the different control mechanisms regulating intracellular membrane trafficking. In addition we demonstrate peroxicretion of a diverse set of intracellular proteins. Therefore, we anticipate that the concept of peroxicretion may revolutionize the production of intracellular proteins from fungi and other microbial cells, as well as from mammalian cells.

Effective lead selection for improved protein production in Aspergillus niger based on integrated genomics.

The filamentous fungus Aspergillus niger is widely exploited for industrial production of enzymes and organic acids. An integrated genomics approach was developed to determine cellular responses of A. niger to protein production in well-controlled fermentations. Different protein extraction methods in combination with automated sample processing and protein identification allowed quantitative analysis of 898 proteins. Three different enzyme overproducing strains were compared to their isogenic fungal host strains. Clear differences in response to the amount and nature of the overproduced enzymes were observed. The corresponding genes of the differentially expressed proteins were studied using transcriptomics. Genes that were up-regulated both at the proteome and transcriptome level were selected as leads for generic strain improvement. Up-regulated proteins included proteins involved in carbon and nitrogen metabolism as well as (oxidative) stress response, and proteins involved in protein folding and endoplasmic reticulum-associated degradation (ERAD). Reduction of protein degradation through the removal of the ERAD factor doaA combined with overexpression of the oligosaccharyl transferase sttC in A. niger overproducing beta-glucuronidase (GUS) strains indeed resulted in a small increase in GUS expression.