Within this research, the CrdA protein from Helicobacter pylori (HpCrdA), a putative copper-binding protein important for the survival of bacterium, was biophysically characterized in a solution, and its binding affinity toward copper was experimentally determined. Incubation of HpCrdA with Cu(II) ions favors the formation of the monomeric species in the solution. The modeled HpCrdA structure shows a conserved methionine-rich region, a potential binding site for Cu(I), as in the structures of similar copper-binding proteins, CopC and PcoC, from Pseudomonas syringae and from Escherichia coli, respectively. Within the conserved amino acid motif, HpCrdA contains two additional methionines and two glutamic acid residues (MMXEMPGMXXMXEM) in comparison to CopC and PcoC but lacks the canonical Cu(II) binding site (two His) since the sequence has no His residues. The methionine-rich site is in a flexible loop and can adopt different geometries for the two copper oxidation states. It could bind copper in both oxidation states (I and II), but with different binding affinities, micromolar was found for Cu(II), and less than nanomolar is proposed for Cu(I). Considering that CrdA is a periplasmic protein involved in chaperoning copper export and delivery in the H. pylori cell and that the affinity of the interaction corresponds to a middle or strong metal–protein interaction depending on the copper oxidation state, we conclude that the interaction also occurs in vivo and is physiologically relevant for H. pylori.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of novel method for protein identification in a microfluidic format.

Protein identification and profiling is critical for the advancement of cell and molecular biology as well as medical diagnostics. Although mass spectrometry and protein microarrays are commonly used for protein identification, both methods require extensive experimental steps and long data analysis times. Here we present a microfluidic top down proteomics platform giving multidimensional read outs of the essential amino acids of proteins. We obtain hydrodynamic radius and fluorescence signals relating to the content of tryptophans, tyrosines and lysines of proteins using a combination of diffusional sizing of proteins, label-free detection and on-chip labelling of proteins with a latent fluorophore in the solution phase. We thereby achieve identification of proteins on a single microfluidic chip by separating and mapping proteins in multidimensional space based on their characteristic physical parameters. Our results have significant implications in the development of easy and rapid platforms to use for native protein identification in clinical and laboratory settings.

In recent years, significant advancements have been made in the understanding of the population distributions and dynamic oligomeric states of the molecular chaperone αB-crystallin and its core domain variants. In this work, we provide solution-phase evidence of the polydispersity of αB-crystallin using microfluidic methods, used for separating the oligomeric species present in solution according to their different electrophoretic mobilities on-chip in a matter of seconds. We in particular demonstrate that microfluidic high-field electrophoresis and diffusion can detect the oligomerisation of these highly dynamic molecular chaperones and characterise the dominant oligomeric species present. We thereby provide a robust microfluidic method for characterising the individual species within complex protein mixtures of biological relevance.

Optical detection has become a convenient and scalable approach to read out information from microfluidic systems. For the study of many key biomolecules, however, including peptides and proteins, which have low fluorescence emission efficiencies at visible wavelengths, this approach typically requires labeling of the species of interest with extrinsic fluorophores to enhance the optical signal obtained - a process which can be time-consuming, requires purification steps, and has the propensity to perturb the behavior of the systems under study due to interactions between the labels and the analyte molecules. As such, the exploitation of the intrinsic fluorescence of protein molecules in the UV range of the electromagnetic spectrum is an attractive path to allow the study of unlabeled proteins. However, direct visualization using 280 nm excitation in microfluidic devices has to date commonly required the use of coherent sources with frequency multipliers and devices fabricated out of materials that are incompatible with soft lithography techniques. Here, we have developed a simple, robust, and cost-effective 280 nm LED platform that allows real-time visualization of intrinsic fluorescence from both unlabeled proteins and protein complexes in polydimethylsiloxane microfluidic channels fabricated through soft lithography. Using this platform, we demonstrate intrinsic fluorescence visualization of proteins at nanomolar concentrations on chip and combine visualization with micron-scale diffusional sizing to measure the hydrodynamic radii of individual proteins and protein complexes under their native conditions in solution in a label-free manner.

Many molecular chaperones exist as oligomeric complexes in their functional states, yet the physical determinants underlying such self-assembly behavior, as well as the role of oligomerization in the activity of molecular chaperones in inhibiting protein aggregation, have proven to be difficult to define. Here, we demonstrate direct measurements under native conditions of the changes in the average oligomer populations of a chaperone system as a function of concentration and time and thus determine the thermodynamic and kinetic parameters governing the self-assembly process. We access this self-assembly behavior in real time under native-like conditions by monitoring the changes in the micrometer-scale diffusion of the different complexes in time and space using a microfluidic platform. Using this approach, we find that the oligomerization mechanism of the Hsp70 subdomain occurs in a cooperative manner and involves structural constraints that limit the size of the species formed beyond the limits imposed by mass balance. These results illustrate the ability of microfluidic methods to probe polydisperse protein self-assembly in real time in solution and to shed light on the nature and dynamics of oligomerization processes.

Reference EPFL-CONF-214088doi:10.1002/pro.2823View record in Web of Science Record created on 2015-12-02, modified on 2017-05-12

Molecular chaperones are key components of the arsenal of cellular defence mechanisms active against protein aggregation. In addition to their established role in assisting protein folding, increasing evidence indicates that molecular chaperones are able to protect against a range of potentially damaging aspects of protein behaviour, including misfolding and aggregation events that can result in the generation of aberrant protein assemblies whose formation is implicated in the onset and progression of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. The interactions between molecular chaperones and different amyloidogenic protein species are difficult to study owing to the inherent heterogeneity of the aggregation process as well as the dynamic nature of molecular chaperones under physiological conditions. As a consequence, understanding the detailed microscopic mechanisms underlying the nature and means of inhibition of aggregate formation remains challenging yet is a key objective for protein biophysics. In this review, we discuss recent results from biophysical studies on the interactions between molecular chaperones and protein aggregates. In particular, we focus on the insights gained from current experimental techniques into the dynamics of the oligomerisation process of molecular chaperones, and highlight the opportunities that future biophysical approaches have in advancing our understanding of the great variety of biological functions of this important class of proteins.