Structure-based method for analyzing protein–protein interfaces

Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (G1.5 kcal mol^-1), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation. Figure Key interaction grids at the interface between barnase and barstar. Key interaction grid for barnase and barstar are presented in one figure according to their coordinates. In order to distinguish the two proteins, different icons were assigned. Crosses represent key grids for barstar and dots represent key grids for barnase. The four residues in ball and stick are Asp40 in barstar and Arg83, Arg87, His102 in barnase.     

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets

Background  

In structural genomics, an important goal is the detection and classification of protein–protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein–protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue–residue interactions and a stepwise distance-dependence. We used increased computational ressources, however, constructing 290,000 decoys for 219 protein–protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment.     

Dynamic nuclear polarization of membrane proteins: covalently bound spin-labels at protein–protein interfaces

We demonstrate that dynamic nuclear polarization of membrane proteins in lipid bilayers may be achieved using a novel polarizing agent: pairs of spin labels covalently bound to a protein of interest interacting at an intermolecular interaction surface. For gramicidin A, nitroxide tags attached to the N-terminal intermolecular interface region become proximal only when bimolecular channels forms in the membrane. We obtained signal enhancements of sixfold for the dimeric protein. The enhancement effect was comparable to that of a doubly tagged sample of gramicidin C, with intramolecular spin pairs. This approach could be a powerful and selective means for signal enhancement in membrane proteins, and for recognizing intermolecular interfaces.     

An information-theoretic classification of amino acids for the assessment of interfaces in protein–protein docking

Docking represents a versatile and powerful method to predict the geometry of protein–protein complexes. However, despite significant methodical advances, the identification of good docking solutions among a large number of false solutions still remains a difficult task. We have previously demonstrated that the formalism of mutual information (MI) from information theory can be adapted to protein docking, and we have now extended this approach to enhance its robustness and applicability. A large dataset consisting of 22,934 docking decoys derived from 203 different protein–protein complexes was used for an MI-based optimization of reduced amino acid alphabets representing the protein–protein interfaces. This optimization relied on a clustering analysis that allows one to estimate the mutual information of whole amino acid alphabets by considering all structural features simultaneously, rather than by treating them individually. This clustering approach is fast and can be applied in a similar fashion to the generation of reduced alphabets for other biological problems like fold recognition, sequence data mining, or secondary structure prediction. The reduced alphabets derived from the present work were converted into a scoring function for the evaluation of docking solutions, which is available for public use via the web service score-MI: http://score-MI.biochem.uni-erlangen.de     

The crystal structure of human protein α1M reveals a chromophore-binding site and two putative protein–protein interfaces

Lipocalin α1-microglobulin (α1M) is a conserved glycoprotein present in plasma and in the interstitial fluids of all tissues. α1M is linked to a heterogeneous yellow–brown chromophore of unknown structure, and interacts with several target proteins, including α1-inhibitor-3, fibronectin, prothrombin and albumin. To date, there is little knowledge about the interaction sites between α1M and its partners. Here, we report the crystal structure of the human α1M. Due to the crystallization occurring in a low ionic strength solution, the unidentified chromophore with heavy electron density is observed at a hydrophobic inner tube of α1M. In addition, two conserved surface regions of α1M are proposed as putative protein–protein interface sites. Further study is needed to unravel the detailed information about the interaction between α1M and its partners.     

Cognitive-motor brain–machine interfaces

Brain–machine interfaces (BMIs) open new horizons for the treatment of paralyzed persons, giving hope for the artificial restoration of lost physiological functions. Whereas BMI development has mainly focused on motor rehabilitation, recent studies have suggested that higher cognitive functions can also be deciphered from brain activity, bypassing low level planning and execution functions, and replacing them by computer-controlled effectors. This review describes the new generation of cognitive-motor BMIs, focusing on three BMI types:

• 1.Speech BMI – reconstructing a person’s speech based on the neuronal activity.
• 2.Direct object control – controlling object movement without mimicking the limb movement that would yield the desired object movement.
• 3.Decoding internal processes, such as neuronal representations of sensory information and decision making.

By outlining recent progress in developing these BMI types, we aim to provide a unified view of contemporary research towards the replacement of behavioral outputs of cognitive processes by direct interaction with the brain.     

PRICE (PRotein Interface Conservation and Energetics): a server for the analysis of protein–protein interfaces

Residues in a protein–protein interface that are important for forming and stabilizing the interaction can usually be identified by looking at patterns of evolutionary conservation in groups of homologous proteins and also by the computational identification of binding hotspots. The PRICE (PRotein Interface Conservation and Energetics) server takes the coordinates of a protein–protein complex, dissects the interface into core and rim regions, and calculates (1) the degree of conservation (measured as the sequence entropy), as well as (2) the change in free energy of binding (∆∆G, due to alanine scanning mutagenesis) of interface residues. Results are displayed as color-coded plots and also made available for download. This enables the computational identification of binding hot spots, based on which further experiments can be designed. The method will aid in protein functional prediction by correct assignment of hot regions involved in binding. Consideration of sequence entropies for residues with large ∆∆G values may provide an indication of the biological relevance of the interface. Finally, the results obtained on a test set of alanine mutants has been compared to those obtained using other servers/methods. The PRICE server is a web application available at .     

Dissecting water binding sites at protein–protein interfaces: a lesson from the atomic structures in the Protein Data Bank

We dissect the protein–protein interfaces into water preservation (WP), water hydration (WH) and water dehydration (WD) sites by comparing the water-mediated hydrogen bonds (H-bond) in the bound and unbound states of the interacting subunits. Upon subunit complexation, if a H-bond between an interface water and a protein polar group is retained, we assign it as WP site; if it is lost, we assign it as WD site and if a new H-bond is created, we assign it as WH site. We find that the density of WD sites is highest followed by WH and WP sites except in antigen and (or) antibody complexes, where the density of WH sites is highest followed by WD and WP sites. Furthermore, we find that WP sites are the most conserved followed by WD and WH sites in all class of complexes except in antigen and (or) antibody complexes, where WD sites are the most conserved followed by WH and WP sites. A significant number of WP and WH sites are involved in water bridges that stabilize the subunit interactions. At WH sites, the residues involved in water bridges are significantly better conserved than the other residues. However, no such difference is observed at WP sites. Interestingly, WD sites are generally replaced with direct H-bonds upon subunit complexation. Significantly, we observe many water-mediated H-bonds remain preserved in spite of large conformational changes upon subunit complexation. These findings have implications in predicting and engineering water binding sites at protein–protein interfaces.     

Comparative mapping of host–pathogen protein–protein interactions

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Phospho-tyrosine dependent protein–protein interaction network

Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes.     

Scoring functions for protein–protein interactions

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Direct molecular fishing in molecular partners investigation in protein–protein and protein–peptide interactions

An original experimental method of direct molecular fishing has been developed for identification of potential partners of protein–protein and protein–peptide interactions. It is based on combination of surface plasmon resonance technology (SPR), size exclusion and affinity chromatography and mass spectrometric identification of proteins (LC-MS/MS). Previously, we demonstrated applicability of this method for protein interactomics using experimental model system, as well as in the pilot study in the frame of the Human Proteome Project (HPP). In the present paper, this method was successfully applied to identify possible molecular partners of 7 target proteins encoded by genes of 18 chromosome (also in the frame of the HPP). Fishing on the affinity sorbents with immobilized target proteins as ligands was carried out using total lysate of human liver tissue as well as pooled sets of fractions (individual for each bait-protein) obtained by means of a combination of size exclusion chromatography and SPR analysis for the presence of potential prey-proteins in each fraction. As a result we obtained lists of possible molecular partners of all 7 proteins and performed a comparative evaluation of direct fishing specificity for these target proteins. Direct molecular fishing was also successfully used for search of potential protein partners interacting with different isoforms of amyloid-beta peptide, playing a key role in the development of Alzheimer’s disease. The synthetic peptides that are analogues of the metal-binding domain isoforms of beta-amyloid were used as molecular baits and the fishing was performed in various fractions of immortalized human neural cells. As a result, 13 potential partner proteins were identified in the cytosol fraction of the cells by fishing on amyloid-beta peptide (1-16).     

PCS-based structure determination of protein–protein complexes

A simple and fast nuclear magnetic resonance method for docking proteins using pseudo-contact shift (PCS) and ¹H^N/¹⁵N chemical shift perturbation is presented. PCS is induced by a paramagnetic lanthanide ion that is attached to a target protein using a lanthanide binding peptide tag anchored at two points. PCS provides long-range (~40 Å) distance and angular restraints between the lanthanide ion and the observed nuclei, while the ¹H^N/¹⁵N chemical shift perturbation data provide loose contact-surface information. The usefulness of this method was demonstrated through the structure determination of the p62 PB1-PB1 complex, which forms a front-to-back 20 kDa homo-oligomer. As p62 PB1 does not intrinsically bind metal ions, the lanthanide binding peptide tag was attached to one subunit of the dimer at two anchoring points. Each monomer was treated as a rigid body and was docked based on the backbone PCS and backbone chemical shift perturbation data. Unlike NOE-based structural determination, this method only requires resonance assignments of the backbone ¹H^N/¹⁵N signals and the PCS data obtained from several sets of two-dimensional ¹⁵N-heteronuclear single quantum coherence spectra, thus facilitating rapid structure determination of the protein–protein complex.     

Molecular glues to stabilise protein–protein interactions

Targeting protein–protein interactions (PPIs) has become a common approach to tackle various diseases whose pathobiology is driven by their mis-regulation in important signalling pathways. Modulating PPIs has tremendous untapped therapeutic potential and different approaches can be used to modulate PPIs. Initially, therapeutic effects were mostly sought by inhibiting PPIs. However, by gaining insight in the mode of action of certain therapeutic compounds, it became clear that stabilising (i.e. enhancing) PPIs can also be useful. The latter strategy is recently gaining a lot of attention, as stabilising physiologic, or even inducing novel interactions of a target protein with E3 ubiquitin ligases forms the basis of the targeted protein degradation (TPD) approach. An emerging additional example for drug discovery based on PPI stabilisation are the 14-3-3 proteins, a family of regulatory proteins, which engages in many protein–protein interactions, some of which might become therapeutical targets.     

Predicting protein–protein interactions from protein sequences using meta predictor

A novel method is proposed for predicting protein–protein interactions (PPIs) based on the meta approach, which predicts PPIs using support vector machine that combines results by six independent state-of-the-art predictors. Significant improvement in prediction performance is observed, when performed on Saccharomyces cerevisiae and Helicobacter pylori datasets. In addition, we used the final prediction model trained on the PPIs dataset of S. cerevisiae to predict interactions in other species. The results reveal that our meta model is also capable of performing cross-species predictions. The source code and the datasets are available at     

Transient protein–protein complexes in base excision repair

Abstract

Transient protein–protein complexes are of great importance for organizing multiple enzymatic reactions into productive reaction pathways. Base excision repair (BER), a process of critical importance for maintaining genome stability against a plethora of DNA-damaging factors, involves several enzymes, including DNA glycosylases, AP endonucleases, DNA polymerases, DNA ligases and accessory proteins acting sequentially on the same damaged site in DNA. Rather than being assembled into one stable multisubunit complex, these enzymes pass the repair intermediates between them in a highly coordinated manner. In this review, we discuss the nature and the role of transient complexes arising during BER as deduced from structural and kinetic data. Almost all of the transient complexes are DNA-mediated, although some may also exist in solution and strengthen under specific conditions. The best-studied example, the interactions between DNA glycosylases and AP endonucleases, is discussed in more detail to provide a framework for distinguishing between stable and transient complexes based on the kinetic data.

Communicated by Ramaswamy H. Sarma