Multivalent Interactions with Intrinsically Disordered Proteins Probed by Surface Plasmon Resonance.

Multivalent interactions underpin associations between intrinsically disordered proteins (IDPs) and their binding partners. This is a subject of considerable interest and governs how nuclear transport receptors (NTRs) orchestrate the nucleocytoplasmic transport (NCT) of signal-specific cargoes through nuclear pore complexes (NPCs) in eukaryotic cells. Specifically, IDPs termed phenylalanine-glycine nucleoporins (FG Nups) exert multivalent interactions with NTRs to facilitate their transport selectivity and speed through the NPC. Here, we document the use of surface plasmon resonance (SPR) to quantify the affinity and kinetics of NTR-FG Nup binding as a function of FG Nup surface density. Moreover, we describe an in situ method that measures conformational height changes that occur in a FG Nup layer following NTR-binding. Protocols by which the as-obtained SPR results are treated with respect to mass transport limitations are further described. Overall, the SPR methodology described here can be applied to studying multivalent interactions and the role of avidity in diverse biological and biointerfacial systems.

Ultrastructural features of the double capsulated connective tissue around silicone prostheses.

The implantation of breast prostheses for both aesthetic and reconstructive purposes has been growing exponentially in the last 20 years. Safety and prosthesis lifespan are majorly debated issues in relation to the correlated long-term complications. Mainly the periprosthetic capsule that develops around the implant is often the cause of complications and particularly for macrotextured silicone breast implants. Some reports have tried to elucidate the mechanism by which macrotextured silicone implants undergo damage and cause double capsule formation. In this study, we investigated the morphological characteristics of double capsule of macrotextured implants surgically removed from patients. With the use of microscopy techniques, this work analyzed the newly formed tissue observed in the interaction between synthetic and biological surfaces.

Site-specific proximity ligation provides molecular insights into biologically relevant interfaces of protein-protein interaction.

Dynamic protein-protein interactions (PPIs) are fundamental to spatiotemporal control of protein functions in biological systems. Dissecting binding interfaces in aqueous solution (i.e., biological interfaces) is of great importance for identifying molecular determinants that contribute to the affinity and specificity of PPIs. Herein, we describe a biochemical method, termed site-specific proximity ligation (SPL), that enables the identification and reconstruction of native binding interfaces distinct from those present in crystal structures and models from computational prediction. SPL involves the strategic incorporation of an aryl azide-containing unnatural amino acid (AZF) into residues of interest in a particular protein that forms a multiprotein complex. Depending on the interfacial role of a targeted residue, a photo-inducible highly reactive incorporated AZF moiety may react with neighboring functional groups to covalently capture an otherwise non-covalent or weak interaction with a specific partner protein, thereby revealing the landscape of biological interfaces. Using a heterotrimeric nuclear pore protein as a model, we show that the biological interfaces of the complex mapped by SPL provide new insight into dynamic molecular recognition that is missed by, or even in conflict with, static models.

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification.

BACKGROUND: Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems. RESULTS: In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA). CONCLUSION: In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier .

Integrating co-evolutionary signals and other properties of residue pairs to distinguish biological interfaces from crystal contacts.

It remains challenging to accurately discriminate between biological and crystal interfaces. Most existing analyses and algorithms focused on the features derived from a single side of the interface. However, less attention has been paid to the properties of residue pairs across protein interfaces. To address this problem, we defined a novel co-evolutionary feature for homodimers through integrating direct coupling analysis and image processing techniques. The residue pairs across biological homodimeric interfaces were significantly enriched in co-evolving residues compared to those across crystal contacts, resulting in a promising classification accuracy with area under the curves (AUCs) of >0.85. Considering the availability of co-evolutionary feature, we also designed other residue pair based features that were useful for both homodimers and heterodimers. The most informative residue pairs were identified to reflect the interaction preferences across protein interfaces. Regarding the other extant properties, we designed the new descriptors at the interface residue level as well as at the pairwise contact level. Extensive validation showed that these single properties can be used to identify biological interfaces with AUCs ranging from 0.60 to 0.88. By integrating co-evolutionary feature with other residue pair based properties, our final prediction model output excellent performance with AUCs of >0.91 on different datasets. Compared to existing methods, our algorithm not only yielded better or comparable results but also provided complementary information. An easy-to-use web server is freely accessible at http://liulab.hzau.edu.cn/RPAIAnalyst.