App-based quantification of crystal phases and amorphous content in ZIF biocomposites.

The performance of zeolitic imidazolate frameworks (ZIFs) as protective hosts for proteins in drug delivery or biocatalysis strongly depends on the type of crystalline phase used for the encapsulation of the biomacromolecule (biomacromolecule@ZIF). Therefore, quantifying the different crystal phases and the amount of amorphous content of ZIFs is becoming increasingly important for a better understanding of the structure-property relationship. Typically, crystalline ZIF phases are qualitatively identified from diffraction patterns. However, accurate phase examinations are time-consuming and require specialized expertise. Here, we propose a calibration procedure (internal standard ZrO2) for the rapid and quantitative analysis of crystalline and amorphous ZIF phases from diffraction patterns. We integrated the procedure into a user-friendly web application, named ZIF Phase Analysis, which facilitates ZIF-based data analysis. As a result, it is now possible to quantify i) the relative amount of various common crystal phases (sodalite, diamondoid, ZIF-CO3-1, ZIF-EC-1, U12 and ZIF-L) in biomacromolecule@ZIF biocomposites based on Zn2+ and 2-methylimidazole (HmIM) and ii) the crystalline-to-amorphous ratio. This new analysis tool will advance the research on ZIF biocomposites for drug delivery and biocatalysis.

Evaluation of host-based molecular markers for the early detection of human sepsis.

We have identified 24 molecular markers, based on circulating nucleic acids (CNA) originating from the human genome, which in combination can be used in a quantitative real-time PCR (qPCR) assay to identify the presence of human sepsis, starting two to three days before the first clinical signs develop and including patients who meet the SEPSIS-3 criteria. The accuracy was more than 87 % inside of the same patient cohort for which the markers were developed and up to 81 % in blind studies of patient cohorts which were not included in the marker development. As our markers are host-based, they can be used to capture bacterial as well as fungal sepsis, unlike the current PCR-based tests, which require species-specific primer sets for each organism causing human sepsis. Our assay directly uses an aliquot of cell-free blood as the substrate for the PCR reaction, thus allowing to obtain the diagnostic results in three to four hours after the collection of the blood samples.

Method for optimizing coating properties based on an evolutionary algorithm approach.

In industry as well as many areas of scientific research, data collected often contain a number of responses of interest for a chosen set of exploratory variables. Optimization of such multivariable multiresponse systems is a challenge well suited to genetic algorithms as global optimization tools. One such example is the optimization of coating surfaces with the required absolute and relative sensitivity for detecting analytes using devices such as sensor arrays. High-throughput synthesis and screening methods can be used to accelerate materials discovery and optimization; however, an important practical consideration for successful optimization of materials for arrays and other applications is the ability to generate adequate information from a minimum number of experiments. Here we present a case study to evaluate the efficiency of a novel evolutionary model-based multiresponse approach (EMMA) that enables the optimization of a coating while minimizing the number of experiments. EMMA plans the experiments and simultaneously models the material properties. We illustrate this novel procedure for materials optimization by testing the algorithm on a sol-gel synthetic route for production and optimization of a well studied amino-methyl-silane coating. The response variables of the coating have been optimized based on application criteria for micro- and macro-array surfaces. Spotting performance has been monitored using a fluorescent dye molecule for demonstration purposes and measured using a laser scanner. Optimization is achieved by exploring less than 2% of the possible experiments, resulting in identification of the most influential compositional variables. Use of EMMA to optimize control factors of a product or process is illustrated, and the proposed approach is shown to be a promising tool for simultaneously optimizing and modeling multivariable multiresponse systems.

A new method to position and functionalize metal-organic framework crystals.

With controlled nanometre-sized pores and surface areas of thousands of square metres per gram, metal-organic frameworks (MOFs) may have an integral role in future catalysis, filtration and sensing applications. In general, for MOF-based device fabrication, well-organized or patterned MOF growth is required, and thus conventional synthetic routes are not suitable. Moreover, to expand their applicability, the introduction of additional functionality into MOFs is desirable. Here, we explore the use of nanostructured poly-hydrate zinc phosphate (α-hopeite) microparticles as nucleation seeds for MOFs that simultaneously address all these issues. Affording spatial control of nucleation and significantly accelerating MOF growth, these α-hopeite microparticles are found to act as nucleation agents both in solution and on solid surfaces. In addition, the introduction of functional nanoparticles (metallic, semiconducting, polymeric) into these nucleating seeds translates directly to the fabrication of functional MOFs suitable for molecular size-selective applications.

Massive non-natural proteins structure prediction using grid technologies.

BACKGROUND: The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is their ability to form a well defined three-dimensional structure. Thus, the structural study of NBPs can help to understand if natural protein sequences were selected for their peculiar properties or if they are just one of the possible stable and functional ensembles. METHODS: The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 x 10(4) sequences) was generated, discarding amino acid sequences with significant similarity to natural proteins, and the corresponding structures were predicted using Rosetta. Given the highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. RESULTS: The vast majority of NBPs, according to the Rosetta model, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in alpha helix-beta strands relative content and in hydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. CONCLUSION: The results obtained suggest that the ability to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt alpha helical folds indicate that all-alpha proteins may have emerged early in pre-biotic evolution. Further, the lower percentage of aromatic residues observed in natural proteins has important evolutionary implications as far as tolerance to mutations is concerned.