A simple and efficient method for predicting protein-protein interaction sites

Computational methods for predicting protein-protein interaction sites based on structural data are characterized by an accuracy between 70 and 80%. Some experimental studies indicate that only a fraction of the residues, forming clusters in the center of the interaction site, are energetically important for binding. In addition, the analysis of amino acid composition has shown that residues located in the center of the interaction site can be better discriminated from the residues in other parts of the protein surface. In the present study, we implement a simple method to predict interaction site residues exploiting this fact and show that it achieves a very competitive performance compared to other methods using the same dataset and criteria for performance evaluation (success rate of 82.1%).     

The immunohistochemical detection of prostatic acid phosphatase: its possibilities and limitations in tumour histochemistry

Summary The immunological specificity of the Amsterdam rabbit antiserum against human prostatic and phosphatase was studied on paraffin sections of 200 prostatic carcinomas and 330 control tissues using an indirect peroxidase technique. Peripheral blood leucocyte smears were also investigated with a fluorescent technique. In a limited number of cases, the mixed aggregation immunocytochemical method was also applied as post-primary incubation procedure. The diaminobenzidine (DAB) final reaction product of the peroxidase technique, carried out under standard conditions, was quantified in some cases using the Leyden Television Analysis System (LEYTAS) with a built-in standard.A positive reaction was obtained in 96.5% of the prostatic carcinomas. Only 2.1% of the non-prostatic tumour cases (23 types) showed a positive reaction, namely six out of 10 insulomas and one out of 10 carcinoid tumours. The -cells of the normal islet of Langerhans and the leucocytes in the smears showed a positive reaction. The sensitivity of the peroxidase method, judged subjectively, is not only dependent on the circumstances of fixation, embedding and incubation but also on the degree of tumour differentiation. None of the three prostatic carcinomas studied reached the level of DAB staining intensity shown by the hyperplastic prostatic epithelium.     

Human pathways in animal models: possibilities and limitations

Animal models are crucial for advancing our knowledge about the molecular pathways involved in human diseases. However, it remains unclear to what extent tissue expression of pathways in healthy individuals is conserved between species. In addition, organism-specific information on pathways in animal models is often lacking. Within these limitations, we explore the possibilities that arise from publicly available data for the animal models mouse, rat, and pig. We approximate the animal pathways activity by integrating the human counterparts of curated pathways with tissue expression data from the models. Specifically, we compare whether the animal orthologs of the human genes are expressed in the same tissue. This is complicated by the lower coverage and worse quality of data in rat and pig as compared to mouse. Despite that, from 203 human KEGG pathways and the seven tissues with best experimental coverage, we identify 95 distinct pathways, for which the tissue expression in one animal model agrees better with human than the others. Our systematic pathway-tissue comparison between human and three animal modes points to specific similarities with human and to distinct differences among the animal models, thereby suggesting the most suitable organism for modeling a human pathway or tissue.     

Homology modelling and spectroscopy, a never-ending love story

Homology modelling is normally the technique of choice when experimental structure data are not available but three-dimensional coordinates are needed, for example, to aid with detailed interpretation of results of spectroscopic studies. Herein, the state of the art of homology modelling will be described in the light of a series of recent developments, and an overview will be given of the problems and opportunities encountered in this field. The major topic, the accuracy and precision of homology models, will be discussed extensively due to its influence on the reliability of conclusions drawn from the combination of homology models and spectroscopic data. Three real-world examples will illustrate how both homology modelling and spectroscopy can be beneficial for (bio)medical research.     

Homology inference of protein-protein interactions via conserved binding sites

The coverage and reliability of protein-protein interactions determined by high-throughput experiments still needs to be improved, especially for higher organisms, therefore the question persists, how interactions can be verified and predicted by computational approaches using available data on protein structural complexes. Recently we developed an approach called IBIS (Inferred Biomolecular Interaction Server) to predict and annotate protein-protein binding sites and interaction partners, which is based on the assumption that the structural location and sequence patterns of protein-protein binding sites are conserved between close homologs. In this study first we confirmed high accuracy of our method and found that its accuracy depends critically on the usage of all available data on structures of homologous complexes, compared to the approaches where only a non-redundant set of complexes is employed. Second we showed that there exists a trade-off between specificity and sensitivity if we employ in the prediction only evolutionarily conserved binding site clusters or clusters supported by only one observation (singletons). Finally we addressed the question of identifying the biologically relevant interactions using the homology inference approach and demonstrated that a large majority of crystal packing interactions can be correctly identified and filtered by our algorithm. At the same time, about half of biological interfaces that are not present in the protein crystallographic asymmetric unit can be reconstructed by IBIS from homologous complexes without the prior knowledge of crystal parameters of the query protein.     

3D-Garden: a system for modelling protein-protein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm

Motivation: Reliable structural modelling of protein–proteincomplexes has widespread application, from drug design to advancingour knowledge of protein interactions and function. This workaddresses three important issues in protein–protein docking:implementing backbone flexibility, incorporating prior indicationsfrom experiment and bioinformatics, and providing public accessvia a server. 3D-Garden (Global And Restrained Docking ExplorationNexus), our benchmarked and server-ready flexible docking system,allows sophisticated programming of surface patches by the uservia a facet representation of the interactors’ molecularsurfaces (generated with the marching cubes algorithm). Flexibilityis implemented as a weighted exhaustive conformer search foreach clashing pair of molecular branches in a set of 5000 modelsfiltered from around 340 000 initially. Results: In a non-global assessment, carried out strictly accordingto the protocols for number of models considered and model qualityof the Critical Assessment of Protein Interactions (CAPRI) experiment,over the widely-used Benchmark 2.0 of 84 complexes, 3D-Gardenidentifies a set of ten models containing an acceptable or bettermodel in 29/45 test cases, including one with large conformationalchange. In 19/45 cases an acceptable or better model is rankedfirst or second out of 340 000 candidates. Availability: http://www.sbg.bio.ic.ac.uk/3dgarden (server) Contact: v.lesk{at}ic.ac.uk Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Burkhard Rost     

Homology modelling of a sensor histidine kinase from Aeromonas hydrophila

Aeromonas hydrophila has been implicated in extra-intestinal infection and diarrhoea in humans. Targetting unique effectors of bacterial pathogens is considered a powerful strategy for drug design against bacterial variations to drug resistance. The two-component bacterial system involving sensor histidine kinase (SHK) and its response regulators is considered a lucrative target for drug design. This is the first report describing a three-dimensional (3D) structure for SHK of A. hydrophila. The model was constructed by homology modelling using the X-ray structure of PleD—a response regulator—in conjunction with cdiGMP (PDB code 1W25) and HemAT sensor domain (PDB code 1OR4)—a globin coupled sensor. A combination of homology modelling methodology and molecular dynamics (MD) simulations was applied to obtain a reasonable structure to understand the dynamic behaviour of SHK. Homology modelling was performed using MODELLER9v2 software. The structure was relaxed to eliminate bad atomic contacts. The final model obtained by molecular mechanics and dynamics methods was assessed using PROCHECK and VERIFY 3D graph, which confirmed that the final refined model is reliable. Until complete biochemical and structural data of SHK are determined by experimental means, this model can serve as a valuable reference for characterising the protein and could be explored for drug targetting by design of suitable inhibitors.     

Structural modelling and mutation analysis of a nociceptin receptor and its ligand complexes

We recently modelled and proposed four ligand-bound conformations for a G-protein-coupled receptor, namely, forms I, II, III and IV, based on the 3D structure and functional evidences for rhodopsin. In this study, the same strategy was applied to a human nociceptin receptor (NR), in order to predict ligand-bound receptor structures. Additionally, site-directed mutagenesis studies were carried out to evaluate these structures. A Thr138Ala mutant demonstrated the same affinity for [Phe(1)Psi(CH(2)-NH)Gly(2)]nociceptin(1-13)NH(2) as the wild-type receptor; however, the affinity of this mutant for nociceptin was 20-fold lower than that of the wild type. A Ser223Ala mutation showed the same characteristics as those of the wild type. On the other hand, a Gln280Ala mutation reduced the affinity to nociceptin by more than 60-folds. These results suggested that a change in the conformation of NR following agonist binding did not accompany the rigid-body rotation of the sixth transmembrane segment that was reported for an adrenergic receptor and a kappa-opioid receptor. NR is potently activated not only by nociceptin but also a synthetic peptide, i.e. Ac-RYYRIK-NH(2), although the amino acid sequences of both these ligands are completely different. The model explains why both the ligands activate NR and shows that their receptor-bound conformations have similar 3D structures.     

Computational prediction of protein-protein complexes

ABSTRACT: BACKGROUND: Protein-protein interactions form the core of several biological processes. With protein-protein interfaces being considered as drug targets, studies on their interactions and molecular mechanisms are gaining ground. As the number of protein complexes in databases is scarce as compared to a spectrum of independent protein molecules, computational approaches are being considered for speedier model derivation and assessment of a plausible complex. In this study, a good approach towards in silico generation of protein-protein heterocomplex and identification of the most probable complex among thousands of complexes thus generated is documented. This approach becomes even more useful in the event of little or no binding site information between the interacting protein molecules. FINDINGS: A plausible protein-protein hetero-complex was fished out from 10 docked complexes which are a representative set of complexes obtained after clustering of 2000 generated complexes using protein-protein docking softwares. The interfacial area for this complex was predicted by two "hotspot" prediction programs employing different algorithms. Further, this complex had the lowest energy and most buried surface area of all the complexes with the same interfacial residues. CONCLUSIONS: For the generation of a plausible protein heterocomplex, various software tools were employed. Prominent are the protein-protein docking methods, prediction of 'hotspots' which are the amino acid residues likely to be in an interface and measurement of buried surface area of the complexes. Consensus generated in their predictions lends credence to the use of the various softwares used.     

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html     

Electrostatic properties of protein-protein complexes

Statistical electrostatic analysis of 37 protein-protein complexes extracted from the previously developed database of protein complexes (ProtCom, http://www.ces.clemson.edu/compbio/protcom) is presented. It is shown that small interfaces have a higher content of charged and polar groups compared to large interfaces. In a vast majority of the cases the average pKa shifts for acidic residues induced by the complex formation are negative, indicating that complex formation stabilizes their ionizable states, whereas the histidines are predicted to destabilize the complex. The individual pKa shifts show the same tendency since 80% of the interfacial acidic groups were found to lower their pKas, whereas only 25% of histidines raise their pKa upon the complex formation. The interfacial groups have been divided into three sets according to the mechanism of their pKa shift, and statistical analysis of each set was performed. It was shown that the optimum pH values (pH of maximal stability) of the complex tend to be the same as the optimum pH values of the complex components. This finding can be used in the homology-based prediction of the 3D structures of protein complexes, especially when one needs to evaluate and rank putative models. It is more likely for a model to be correct if both components of the model complex and the entire complex have the same or at least similar values of the optimum pH.     

Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data

The number of structures of protein-protein complexes deposited to the Protein Data Bank is growing rapidly. These structures embed important information for predicting structures of new protein complexes. This motivated us to develop the PPISP method for predicting interface residues in protein-protein complexes. In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network. The network was trained on native interface residues collected from the Protein Data Bank. The prediction accuracy at the time was 70% with 47% coverage of native interface residues. Now we have extensively improved PPISP. The training set now consisted of 1156 nonhomologous protein chains. Test on a set of 100 nonhomologous protein chains showed that the prediction accuracy is now increased to 80% with 51% coverage. To solve the problem of over-prediction and under-prediction associated with individual neural network models, we developed a consensus method that combines predictions from multiple models with different levels of accuracy and coverage. Applied on a benchmark set of 68 proteins for protein-protein docking, the consensus approach outperformed the best individual models by 3-8 percentage points in accuracy. To demonstrate the predictive power of cons-PPISP, eight complex-forming proteins with interfaces characterized by NMR were tested. These proteins are nonhomologous to the training set and have a total of 144 interface residues identified by chemical shift perturbation. cons-PPISP predicted 174 interface residues with 69% accuracy and 47% coverage and promises to complement experimental techniques in characterizing protein-protein interfaces. .     

Detection of autophagy in tissue by standard immunohistochemistry: possibilities and limitations

Transmission electron microscopy (TEM) is currently the standard method to monitor autophagy in tissue. Because TEM is labor intensive, we recently questioned whether marker proteins could be found for unambiguous detection of autophagy in tissue using standard immunohistochemical techniques. Our findings indicated that the identification of autophagy-specific biomarkers for tissue is highly compromised due to lack of differential gene expression. In this respect, TEM remains an indispensable technique for evaluation of autophagy in situ. Nevertheless, immunohistochemical staining of microtubule-associated protein 1 light chain 3 (LC3) appeared to be a valuable technique to detect autophagosome formation in tissue but only when this protein is overexpressed, e.g., in GFP-LC3 transgenic animals. Furthermore, demonstration of granular cytoplasmic ubiquitin inclusions by immunohistochemistry may be an attractive technique to measure autophagic cell degeneration in some human pathologies such as neurodegenerative diseases, heart failure and atherosclerosis.     

Methods of molecular modelling of protein-protein interactions

This article reviews briefly theoretical methods attempting to predict the structure of protein aggregates from the structural features of their subunits. The authors discuss the problems of the solvent effect and the formation of protein structure. The existing methods of quaternary structure prediction are presented and an attempt at their classification is made at the end of this review.     

A novel NMR method for determining the interfaces of large protein-protein complexes

Identification of the interfaces of large (Mr > 50,000) protein-protein complexes in solution by high resolution NMR has typically been achieved using experiments involving chemical shift perturbation and/or hydrogen-deuterium exchange of the main chain amide groups of the proteins. Interfaces identified using these techniques, however, are not always identical to those revealed using X-ray crystallography. In order to identify the contact residues in a large protein-protein complex more accurately, we developed a novel NMR method that uses cross-saturation phenomena in combination with TROSY detection in an optimally deuterium labeled system.     

Reconstructing atmospheric carbon dioxide with stomata: possibilities and limitations of a botanical pCO2-sensor

Stomatal frequency is often observed to vary inversely with atmospheric CO₂ concentration (pCO₂). The response is due to (1) individual phenotypic plasticity and (2) evolutionary change, depending on the time scale. Evolutionary responses occur more frequently than individual responses and individual responses are more pronounced under subambient pCO₂ levels than under elevated pCO₂ (CO₂ ceiling). The evolutionary response appears therefore to be a valuable device for determining past pCO₂. Since tree leaves often represent a conspicuous and rich resource of fossil material, they are increasingly important in this respect. Additionally, certain tree species are considered to represent living fossils and therefore valuable sources of ancient stomatal data. There are, however, numerous difficulties which have to be considered such as: (1) high variance of the data, especially for fossil material, (2) interspecific differences of the response, (3) the CO₂ ceiling and (4) differences between short-term and long-term responses. Whereas the qualitative pCO₂ signal of stomatal frequency appears to be reliable, quantitative pCO₂ reconstruction has to be performed with caution. The results of a number of studies which used stomatal frequency as a pCO₂ sensor demonstrate good agreement with the results obtained with other proxy data. Current techniques are based on transfer functions which calibrate the fossil data with extant material. It is suggested that a mechanistic approach including physical as well as physiological processes could improve pCO₂ reconstruction. Furthermore, the topic of the influence of pCO₂ on stomatal frequency is significant not only for reconstructing past pCO₂ but also with respect to the climate-biosphere interrelationship.