Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally-informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure leaving questions of unintentional bias and applicability to unknown structures unresolved. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

Single Molecule Measurements of the Accessibility of Molecular Surfaces

An important measure of the conformation of protein molecules is the degree of surface exposure of its specific segments. However, this is hard to measure at the level of individual molecules. Here, we combine single molecule photobleaching (smPB, which resolves individual photobleaching steps of single molecules) and fluorescence quenching techniques to measure the accessibility of individual fluorescently labeled protein molecules to quencher molecules in solution. A quencher can reduce the time a fluorophore spends in the excited state, increasing its photostability under continuous irradiation. Consequently, the photo-bleaching step length would increase, providing a measure for the accessibility of the fluorophore to the solvent. We demonstrate the method by measuring the bleaching step-length increase in a lipid, and also in a lipid-anchored peptide (both labelled with rhodamine-B and attached to supported lipid bilayers). The fluorophores in both molecules are expected to be solvent-exposed. They show a near two-fold increase in the step length upon incubation with 5 mM tryptophan (a quencher of rhodamine-B), validating our approach. A population distribution plot of step lengths before and after addition of tryptophan show that the increase is not always homogenous. Indeed there are different species present with differential levels of exposure. We then apply this technique to determine the solvent exposure of membrane-attached N-terminus labelled amylin (h-IAPP, an amyloid associated with Type II diabetes) whose interaction with lipid bilayers is poorly understood. hIAPP shows a much smaller increase of the step length, signifying a lower level of solvent exposure of its N-terminus. Analysis of results from individual molecules and step length distribution reveal that there are at least two different conformers of amylin in the lipid bilayer. Our results show that our method (“Q-SLIP”, Quenching-induced Step Length increase in Photobleaching) provides a simple route to probe the conformational states of membrane proteins at a single molecule level.

Understanding and optimizing Evolon® CR for varnish removal from oil paintings

Evolon$$^\circledR$$ ® CR is increasingly used in paintings conservation for varnish removal from oil paintings. Its key benefits over traditional cotton swabs are limiting solvent exposure and reducing mechanical action on the paint surface. However, this non-woven microfilament textile was not originally engineered for conservation use and little is known about its chemical stability towards organic solvents. Moreover, the physical processes of solvent loading and release by Evolon$$^\circledR$$ ® CR, as well as solvent retention inside paint after cleaning, have not been studied. These three topics were investigated using a multi-analytical approach, aiming for an improved understanding and optimized use of Evolon$$^\circledR$$ ® CR for varnish removal. Our results show that the tissue is generally chemically and physically stable to organic solvents when exposed on timescales that are typical in conservation practice. However, a pre-treatment step of Evolon$$^\circledR$$ ® CR is necessary to avoid the release of unwanted saturated fatty acids into the paint during varnish removal. We show that the primary mechanism of solvent uptake by the fibers is adsorption rather than absorption and that the dominant factor dictating the maximum solvent load is the volume of the voids between the fibers. Finally, solvent induced dynamics after application of solvent-loaded Evolon$$^\circledR$$ ® CR within the paint film was monitored using portable laser speckle imaging on model paints. A method to quantify solvent-retention in real-time was developed and revealed that the presence of varnish on paintings results in lower dynamics of solvents within the paint in comparison to unvarnished paint. Comparing various solvents, it was found that cleaning with acetone resulted in a roughly six-fold increase in dynamics compared to ethanol and isopropanol.

Solvent-structured PEDOT:PSS surfaces: fabrication strategies and nanoscale properties

We present the preparation of nanostructured conducting PEDOT:PSS thin films by solvent vapor annealing (SVA), using the low boiling point solvent tetrahydrofuran (THF). An Atomic Force Microscopy (AFM) study allowed the observation of distinct nanostructure development as a function of solvent exposure time. Moreover, the nanostructures’ physical properties were evaluated by nanomechanical, nanoelectrical, and nano-FTIR measurements. In this way, we were able to differentiate the local response of the developed phases and to identify their chemical nature. The combination of these techniques allowed to demonstrate that exposure to THF is a facile method to effectively and selectively modify the surface nanostructure of PEDOT:PSS, and thereafter its final properties. Moreover, our nanoscale studies provided evidence about the molecular rearrangements that PEDOT:PSS suffers during nanostructure fabrication, a fundamental fact in order to expand the potential applications of this polymer in thermoelectric and optoelectronic devices.

The structural determinants of intra-protein compensatory substitutions

Compensating substitutions happen when one mutation is advantageously selected because it restores the loss of fitness induced by a previous deleterious mutation. How frequent such mutations occur in evolution and what is the structural and functional context permitting their emergence remain open questions. We built an atlas of intra-protein compensatory substitutions using a phylogenetic approach and a dataset of 1,630 bacterial protein families for which high-quality sequence alignments and experimentally derived protein structures were available. We identified more than 51,000 positions coevolving by the mean of predicted compensatory mutations. Using the evolutionary and structural properties of the analyzed positions, we demonstrate that compensatory mutations are scarce (typically only a few in the protein history) but widespread (the majority of proteins experienced at least one). Typical coevolving residues are evolving slowly, are located in the protein core outside secondary structure motifs, and are more often in contact than expected by chance, even after accounting for their evolutionary rate and solvent exposure. An exception to this general scheme are residues coevolving for charge compensation, which are evolving faster than non-coevolving sites, in contradiction with predictions from simple coevolutionary models, but similar to stem pairs in RNA. While sites with a significant pattern of coevolution by compensatory mutations are rare, the comparative analysis of hundreds of structures ultimately permits a better understanding of the link between the three-dimensional structure of a protein and its fitness landscape.

3D Interaction Homology: Computational Titration of Aspartic Acid, Glutamic Acid and Histidine Can Create pH-Tunable Hydropathic Environment Maps

Aspartic acid, glutamic acid and histidine are ionizable residues occupying various protein environments and perform many different functions in structures. Their roles are tied to their acid/base equilibria, solvent exposure, and backbone conformations. We propose that the number of unique environments for ASP, GLU and HIS is quite limited. We generated maps of these residue's environments using a hydropathic scoring function to record the type and magnitude of interactions for each residue in a 2703-protein structural dataset. These maps are backbone-dependent and suggest the existence of new structural motifs for each residue type. Additionally, we developed an algorithm for tuning these maps to any pH, a potentially useful element for protein design and structure building. Here, we elucidate the complex interplay between secondary structure, relative solvent accessibility, and residue ionization states: the degree of protonation for ionizable residues increases with solvent accessibility, which in turn is notably dependent on backbone structure.

pKAI: a fast and interpretable deep learning approach for accurate electrostatics-driven pKa predictions

The pKa values of ionizable residues influence protein folding, stability and biological function. The pKa in bulk water is known for all residues, however, in a protein environment, it can significantly be affected by confinement and electrostatics. Existing computational methods to estimate pKa shifts rely on theoretical approximations and lengthy computations. Furthermore, the amount of experimentally determined pKa values is still very limited, hindering the development of faster machine learning-based methods. In this work, we use a data set of 6 million pKa shifts — determined by PypKa, a continuum electrostatics method — to train deep learning models that are shown to rival the physics-based predictor. On ~750 experimentally determined data points, our model displays the best accuracy and it is the only one that breaks the 1 pK unit RMSE barrier of this considerably difficult test set. Although trained using a very simplified view of the surroundings of the titratable group (namely, atom types and distances to other titratable groups within a given radius), the models are shown to assign proper electrostatic charges to chemical groups, to keep the known correlation between solvent exposure and pKa shift magnitude, and to grasp the importance of close interactions, including hydrogen bonds. Inference times allow speedups of more than 1000 times faster than physics-based methods, especially for large proteins. By combining speed, accuracy and a reasonable understanding of the theoretical basis for pKa shifts, our models provide a game-changing solution for fast estimations of macroscopic pKa from ensembles of microscopic (pKhalf) values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.

Molecular and Structural Parallels between Gluten Pathogenic Peptides and Bacterial-Derived Proteins by Bioinformatics Analysis

Gluten-related disorders (GRDs) are a group of diseases that involve the activation of the immune system triggered by the ingestion of gluten, with a worldwide prevalence of 5%. Among them, Celiac disease (CeD) is a T-cell-mediated autoimmune disease causing a plethora of symptoms from diarrhea and malabsorption to lymphoma. Even though GRDs have been intensively studied, the environmental triggers promoting the diverse reactions to gluten proteins in susceptible individuals remain elusive. It has been proposed that pathogens could act as disease-causing environmental triggers of CeD by molecular mimicry mechanisms. Additionally, it could also be possible that unrecognized molecular, structural, and physical parallels between gluten and pathogens have a relevant role. Herein, we report sequence, structural and physical similarities of the two most relevant gluten peptides, the 33-mer and p31-43 gliadin peptides, with bacterial pathogens using bioinformatics going beyond the molecular mimicry hypothesis. First, a stringent BLASTp search using the two gliadin peptides identified high sequence similarity regions within pathogen-derived proteins, e.g., extracellular proteins from Streptococcus pneumoniae and Granulicatella sp. Second, molecular dynamics calculations of an updated α-2-gliadin model revealed close spatial localization and solvent-exposure of the 33-mer and p31-43 peptide, which was compared with the pathogen-related proteins by homology models and localization predictors. We found putative functions of the identified pathogen-derived sequence by identifying T-cell epitopes and SH3/WW-binding domains. Finally, shape and size parallels between the pathogens and the superstructures of gliadin peptides gave rise to novel hypotheses about activation of innate immunity and dysbiosis. Based on our structural findings and the similarities with the bacterial pathogens, evidence emerges that these pathologically relevant gluten-derived peptides could behave as non-replicating pathogens opening new research questions in the interface of innate immunity, microbiome, and food research.