SHARP2: protein-protein interaction predictions using patch analysis

SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY: http://www.bioinformatics.sussex.ac.uk/SHARP2.     

False positive reduction in protein-protein interaction predictions using gene ontology annotations

Background  

Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.     

Assessing predictions of protein-protein interaction: the CAPRI experiment

The Critical Assessment of PRedicted Interactions (CAPRI) experiment was designed in 2000 to test protein docking algorithms in blind predictions of the structure of protein-protein complexes. In four years, 17 complexes offered by crystallographers as targets prior to publication, have been subjected to structure prediction by docking their two components. Models of these complexes were submitted by predictor groups and assessed by comparing their geometry to the X-ray structure and by evaluating the quality of the prediction of the regions of interaction and of the pair wise residue contacts. Prediction was successful on 12 of the 17 targets, most of the failures being due to large conformation changes that the algorithms could not cope with. Progress in the prediction quality observed in four years indicates that the experiment is a powerful incentive to develop new procedures that allow for flexibility during docking and incorporate nonstructural information. We therefore call upon structural biologists who study protein-protein complexes to provide targets for further rounds of CAPRI predictions.     

SPR identification of mild elution conditions for affinity purification of E6 oncoprotein, using a multivariate experimental design

The purification of "difficult" proteins for structural and functional studies remains a challenge. A widely used approach is their production as fusions with an affinity tag, so that a generic tag-based purification protocol can be applied. Alternatively, immuno-affinity using a protein-specific antibody allows purification of unmodified proteins in a single step, if mild elution conditions can be identified for dissociating the complex without disrupting the folding of the protein. Here, we describe a quantitative structure activity relationship (QSAR) strategy to predict optimized elution conditions from a mathematical model that relates target/antibody dissociation to environmental changes. We illustrate the strategy with the E6 protein of the human papilloma virus (HPV) 16, a highly unstable protein central to HPV-induced carcinogenesis. Surface plasmon resonance (SPR) was used to measure the kinetics of dissociation of an E6 peptide from an E6-specific antibody in a set of multivariate conditions, where three environmental factors (pH, NaCl concentration, and temperature) were varied. The QSAR model indicated that dissociation is favored at pH < 5, which is detrimental to E6 folding, and also at pH > or = 10 if the temperature is high. We verified that the conclusions of the QSAR study with the peptide were valid for the scFv1F4/E6 protein complex, and that the recovered protein was capable of mediating p53 degradation. Finally, we demonstrated that the optimized elution conditions (pH 10, 35 degrees C) were adequate for purifying the recombinant E6 protein from crude cell extracts.     

Structural basis of affinity maturation and intramolecular cooperativity in a protein-protein interaction

Although protein-protein interactions are involved in nearly all cellular processes, general rules for describing affinity and selectivity in protein-protein complexes are lacking, primarily because correlations between changes in protein structure and binding energetics have not been well determined. Here, we establish the structural basis of affinity maturation for a protein-protein interaction system that we had previously characterized energetically. This model system exhibits a 1500-fold affinity increase. Also, its affinity maturation is restricted by negative intramolecular cooperativity. With three complex and six unliganded variant X-ray crystal structures, we provide molecular snapshots of protein interface remodeling events that span the breadth of the affinity maturation process and present a comprehensive structural view of affinity maturation. Correlating crystallographically observed structural changes with measured energetic changes reveals molecular bases for affinity maturation, intramolecular cooperativity, and context-dependent binding.     

Molecular basis of a million-fold affinity maturation process in a protein-protein interaction

Protein engineering is becoming increasingly important for pharmaceutical applications where controlling the specificity and affinity of engineered proteins is required to create targeted protein therapeutics. Affinity increases of several thousand-fold are now routine for a variety of protein engineering approaches, and the structural and energetic bases of affinity maturation have been investigated in a number of such cases. Previously, a 3-million-fold affinity maturation process was achieved in a protein-protein interaction composed of a variant T-cell receptor fragment and a bacterial superantigen. Here, we present the molecular basis of this affinity increase. Using X-ray crystallography, shotgun reversion/replacement scanning mutagenesis, and computational analysis, we describe, in molecular detail, a process by which extrainterfacial regions of a protein complex can be rationally manipulated to significantly improve protein engineering outcomes.     

Effect of sampling on topology predictions of protein-protein interaction networks

Currently available protein-protein interaction (PPI) network or 'interactome' maps, obtained with the yeast two-hybrid (Y2H) assay or by co-affinity purification followed by mass spectrometry (co-AP/MS), only cover a fraction of the complete PPI networks. These partial networks display scale-free topologies--most proteins participate in only a few interactions whereas a few proteins have many interaction partners. Here we analyze whether the scale-free topologies of the partial networks obtained from Y2H assays can be used to accurately infer the topology of complete interactomes. We generated four theoretical interaction networks of different topologies (random, exponential, power law, truncated normal). Partial sampling of these networks resulted in sub-networks with topological characteristics that were virtually indistinguishable from those of currently available Y2H-derived partial interactome maps. We conclude that given the current limited coverage levels, the observed scale-free topology of existing interactome maps cannot be confidently extrapolated to complete interactomes.     

CRF-based models of protein surfaces improve protein-protein interaction site predictions

Background

The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved.

Results

We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers.

Conclusions

Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.     

Structure-based protein-protein interaction networks and drug design

Proteins carry out their functions by interacting with other proteins and small molecules, forming a complex interaction network. In this review, we briefly introduce classical graph theory based protein-protein interaction networks. We also describe the commonly used experimental methods to construct these networks, and the insights that can be gained from these networks. We then discuss the recent transition from graph theory based networks to structure based protein-protein interaction networks and the advantages of the latter over the former, using two networks as examples. We further discuss the usefulness of structure based protein-protein interaction networks for drug discovery, with a special emphasis on drug repositioning.     

SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments

We present a statistical method SAINT-MS1 for scoring protein-protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range.     

Computational predictions and experimental affinity distributions for a homovanillic acid molecularly imprinted polymer

Density Functional Theory calculations have been used to select, among a set of chemicals traditionally used in the formulation of non-covalent molecularly imprinted polymers (MIPs), the best functional monomer and porogenic solvent for the construction of a recognition element for the dopamine metabolite homovanillic acid (HVA). Theoretical predictions were confirmed through batch binding assays and voltammetric detection. The computational method predicts that trifluoromethacrylic acid and toluene are the monomer and solvent rendering the highest stabilization energy for the pre-polymerization adducts. HVA-MIP prepared using this formulation gives rise to a binding isotherm that is accurately modelled by the Freundlich isotherm. The binding properties of this polymer were estimated using affinity distribution analysis. An apparent number of sites of 13 micromol g(-1) with an average affinity constant of 2 x 10(4) M(-1) was obtained in the concentration window studied.     

Investigating protein-protein interaction surfaces using a reduced stereochemical and electrostatic model

A method of calculating the electrostatic potential energy between two molecules, using finite difference potential, is presented. A reduced charge set is used so that the interaction energy can be calculated as the two static molecules explore their full six-dimensional configurational space. The energies are contoured over surfaces fixed to each molecule with an interactive computer graphics program. For two crystal structures (trypsin-trypsin inhibitor and anti-lysozyme Fab-lysozyme), it is found that the complex corresponds to highly favourable interacting regions in the contour plots. These matches arise from a small number of protruding basic residues interacting with enhanced negative potential in each case. The redox pair cytochrome c peroxidase-cytochrome c exhibits an extensive favourably interacting surface within which a possible electron transfer complex may be defined by an increased electrostatic complementarity, but a decreased electrostatic energy. A possible substrate transfer configuration for the glycolytic enzyme pair glyceraldehyde phosphate dehydrogenase-phosphoglycerate kinase is presented.     

Filtering high-throughput protein-protein interaction data using a combination of genomic features

Background  

Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.     

Study of protein-protein interaction using conformational space annealing

We apply conformational space annealing (CSA), an efficient global optimization method, to the study of protein-protein interaction. The CSA is incorporated into the Tinker molecular modeling package along with a B-spline method for CAPRI Round 5 experiments. We have used an energy function for the protein-protein interaction that consists of electrostatic interaction, van der Waals interaction, and solvation energy terms represented by the occupancy desolvation method. The parameters of the AMBER94 all-atom empirical force field are used. Each energy term is calculated by precalculated grid potentials and B-spline method approximation. The ligand protein is placed inside a sphere of 50 A radius centered at an appropriate location, and the CSA rigid docking studies are carried out to find stable complexes. Up to 10 complexes are selected using the K-mean clustering method and biological information when available. These complexes are energy-minimized for further refinement by considering the flexibility of interacting proteins. The results show that the CSA method has a potential for the study of protein-protein interaction.     

Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking

MOTIVATION: Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation. RESULTS: A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered 'valid' by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described. AVAILABILITY: http://bioinformatics.leeds.ac.uk/~bmb4sjc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Kinetic studies of protein-protein interactions

The structure of a protein-protein interaction, its affinity and thermodynamic characteristics depict a 'frozen' state of a complex. This picture ignores the kinetic nature of complex formation and dissociation, which are of major biological and biophysical interest. This review highlights recent advances in deciphering the kinetic pathway of protein-protein complexation, the nature of the encounter complex, transition state and intermediate along the reaction, and the effects of mutation, viscosity, pH and salt on association.     

Identification of novel protein-protein interactions using a versatile mammalian tandem affinity purification expression system

Identification of protein-protein interactions is essential for elucidating the biochemical mechanism of signal transduction. Purification and identification of individual proteins in mammalian cells have been difficult, however, due to the sheer complexity of protein mixtures obtained from cellular extracts. Recently, a tandem affinity purification (TAP) method has been developed as a tool that allows rapid purification of native protein complexes expressed at their natural level in engineered yeast cells. To adapt this method to mammalian cells, we have created a TAP tag retroviral expression vector to allow stable expression of the TAP-tagged protein at close to physiological levels. To demonstrate the utility of this vector, we have fused a TAP tag, consisting of a protein A tag, a cleavage site for the tobacco etch virus (TEV) protease, and the FLAG epitope, to the N terminus of human SMAD3 and SMAD4. We have stably expressed these proteins in mammalian cells at desirable levels by retroviral gene transfer and purified native SMAD3 protein complexes from cell lysates. The combination of two different affinity tags greatly reduced the number of nonspecific proteins in the mixture. We have identified HSP70 as a specific interacting protein of SMAD3. We demonstrated that SMAD3, but not SMAD1, binds HSP70 in vivo, validating the TAP purification approach. This method is applicable to virtually any protein and provides an efficient way to purify unknown proteins to homogeneity from the complex mixtures found in mammalian cell lysates in preparation for identification by mass spectrometry.     

Prediction of protein-protein interaction sites using an ensemble method

Background  

Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved.