Shelling the Voronoi interface of protein-protein complexes predicts residue activity and conservation

The accurate description of protein-protein interfaces remains a challenging task. Traditional criteria, based on atomic contacts or changes in solvent accessibility, tend to over or underpredict the interface itself and cannot discriminate active from less relevant parts. A recent simulation study by Mihalek and co-authors (2007, JMB 369, 584-95) concluded that active residues tend to be `dry', that is, insulated from water fluctuations. We show that patterns of `dry' residues can, to a large extent, be predicted by a fast, parameter-free and purely geometric analysis of protein interfaces. We introduce the shelling order of Voronoi facets as a straightforward quantitative measure of an atom's depth inside an interface. We analyze the correlation between Voronoi shelling order, dryness, and conservation on a set of 54 protein-protein complexes. Residues with high shelling order tend to be dry; evolutionary conservation also correlates with dryness and shelling order but, perhaps not surprisingly, is a much less accurate predictor of either property. Voronoi shelling order thus seems a meaningful and efficient descriptor of protein interfaces. Moreover, the strong correlation with dryness suggests that water dynamics within protein interfaces may, in first approximation, be described by simple diffusion models

Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

RosettaBackrub--a web server for flexible backbone protein structure modeling and design.

The RosettaBackrub server (http://kortemmelab.ucsf.edu/backrub) implements the Backrub method, derived from observations of alternative conformations in high-resolution protein crystal structures, for flexible backbone protein modeling. Backrub modeling is applied to three related applications using the Rosetta program for structure prediction and design: (I) modeling of structures of point mutations, (II) generating protein conformational ensembles and designing sequences consistent with these conformations and (III) predicting tolerated sequences at protein-protein interfaces. The three protocols have been validated on experimental data. Starting from a user-provided single input protein structure in PDB format, the server generates near-native conformational ensembles. The predicted conformations and sequences can be used for different applications, such as to guide mutagenesis experiments, for ensemble-docking approaches or to generate sequence libraries for protein design

Two-dimensional protein crystallization via metal-ion coordination by naturally occurring surface histidines

A powerful and potentially general approach to the targeting and crystallization of proteins on lipid interfaces through coordination of surface histidine residues to lipid-chelated divalent metal ions is presented. This approach, which should be applicable to the crystallization of a wide range of naturally occurring or engineered proteins, is illustrated here by the crystallization of streptavidin on a monolayer of an iminodiacetate-Cu(II) lipid spread at the air-water interface. This method allows control of the protein orientation at interfaces, which is significant for the facile production of highly ordered protein arrays and for electron density mapping in structural analysis of two-dimensional crystals. Binding of native streptavidin to the iminodiacetate-Cu lipids occurs via His-87, located on the protein surface near the biotin binding pocket. The two-dimensional streptavidin crystals show a previously undescribed microscopic shape that differs from that of crystals formed beneath biotinylated lipids

Reversal of aging-induced increases in aortic stiffness by targeting cytoskeletal protein-protein interfaces

BACKGROUND: The proximal aorta normally functions as a critical shock absorber that protects small downstream vessels from damage by pressure and flow pulsatility generated by the heart during systole. This shock absorber function is impaired with age because of aortic stiffening. METHODS AND RESULTS: We examined the contribution of common genetic variation to aortic stiffness in humans by interrogating results from the AortaGen Consortium genome‐wide association study of carotid‐femoral pulse wave velocity. Common genetic variation in the N‐WASP (WASL) locus is associated with carotid‐femoral pulse wave velocity (rs600420, P=0.0051). Thus, we tested the hypothesis that decoy proteins designed to disrupt the interaction of cytoskeletal proteins such as N‐WASP with its binding partners in the vascular smooth muscle cytoskeleton could decrease ex vivo stiffness of aortas from a mouse model of aging. A synthetic decoy peptide construct of N‐WASP significantly reduced activated stiffness in ex vivo aortas of aged mice. Two other cytoskeletal constructs targeted to VASP and talin‐vinculin interfaces similarly decreased aging‐induced ex vivo active stiffness by on‐target specific actions. Furthermore, packaging these decoy peptides into microbubbles enables the peptides to be ultrasound‐targeted to the wall of the proximal aorta to attenuate ex vivo active stiffness. CONCLUSIONS: We conclude that decoy peptides targeted to vascular smooth muscle cytoskeletal protein‐protein interfaces and microbubble packaged can decrease aortic stiffness ex vivo. Our results provide proof of concept at the ex vivo level that decoy peptides targeted to cytoskeletal protein‐protein interfaces may lead to substantive dynamic modulation of aortic stiffness.Published versio

The Most Severe Test for Hydrophobicity Scales: Two Proteins with 88% Sequence Identity but Different Structure and Function

Protein-protein interactions (protein functionalities) are mediated by water, which compacts individual proteins and promotes close and temporarily stable large-area protein-protein interfaces. In their classic paper Kyte and Doolittle (KD) concluded that the "simplicity and graphic nature of hydrophobicity scales make them very useful tools for the evaluation of protein structures". In practice, however, attempts to develop hydrophobicity scales (for example, compatible with classical force fields (CFF) in calculating the energetics of protein folding) have encountered many difficulties. Here we suggest an entirely different approach, based on the idea that proteins are self-organized networks, subject to finite-scale criticality (like some network glasses). We test this proposal against two small proteins that are delicately balanced between alpha and alpha/beta structures, with different functions encoded with only 12% of their amino acids. This example explains why protein structure prediction is so challenging, and it provides a severe test for the accuracy and content of hydrophobicity scales. The new method confirms KD's evaluation, and at the same time suggests that protein structure, dynamics and function can be best discussed without using CFF

Protein-RNA interactions: a structural analysis

A detailed computational analysis of 32 protein-RNA complexes is presented. A number of physical and chemical properties of the intermolecular interfaces are calculated and compared with those observed in protein-double-stranded DNA and protein-single-stranded DNA complexes. The interface properties of the protein-RNA complexes reveal the diverse nature of the binding sites. van der Waals contacts played a more prevalent role than hydrogen bond contacts, and preferential binding to guanine and uracil was observed. The positively charged residue, arginine, and the single aromatic residues, phenylalanine and tyrosine, all played key roles in the RNA binding sites. A comparison between protein-RNA and protein-DNA complexes showed that whilst base and backbone contacts (both hydrogen bonding and van der Waals) were observed with equal frequency in the protein-RNA complexes, backbone contacts were more dominant in the protein-DNA complexes. Although similar modes of secondary structure interactions have been observed in RNA and DNA binding proteins, the current analysis emphasises the differences that exist between the two types of nucleic acid binding protein at the atomic contact level

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by Direct Coupling Analysis

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical Direct Coupling Analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure-prediction techniques to predict high-resolution models. By taking advantage of the on-going exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2000 PFAM protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies of different binding modes within one large PFAM family. Sequence derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many hetero-protein complexes in the future.Comment: 18 pages, 6 figures, to appears in PNA