A fast method to predict protein interaction sites from sequences

Characterization of SLC26A9 in patients with CF-like lung disease

Diffuse bronchiectasis is a common problem in respiratory clinics. We hypothesized that mutations in the solute carrier 26A9 (SLC26A9) gene, encoding for a chloride (Cl(-)) transporter mainly expressed in lungs, may lead to defects in mucociliary clearance. We describe two missense variants in the SLC26A9 gene in heterozygote patients presenting with diffuse idiopathic bronchiectasis : p.Arg575Trp, identified in a patient also heterozygote for p.Phe508del in the CFTR gene; and p.Val486Ile. Expression of both mutants in Xenopus laevis oocytes abolished SLC26A9-mediated Cl(-) conductance without decreasing protein membrane expression. Coexpression of CFTR with SLC26A9-p.Val486Ile resulted in a significant increase in the Cl(-) current induced by PKA stimulation, similar to that obtained in oocytes expressing CFTR and SLC26A9-WT. In contrast, coexpression of CFTR with SLC26A9-p.Arg575Trp inhibited SLC26A9-enhanced CFTR activation upon PKA. Further structure-function analyses led us to propose a site encompassing Arg575 in the SLC26A9-STAS domain for CFTR-SLC26A9 interaction. We hypothesize that SLC26A9-p.Arg575Trp prevented SLC26A9-mediated functional activation of CFTR by altering SLC26A9-CFTR interaction. Although we cannot confirm that these mutations by themselves are deleterious, we propose that they trigger the pathogenic role of a single CFTR mutation and provide insight into a novel mechanism of Cl(-) transport alteration across the respiratory mucosa, based on functional inhibition of CFTR.

Aging-dependent reduction in glyoxalase 1 delays wound healing

Methylglyoxal (MG), the major dicarbonyl substrate of the enzyme glyoxalase 1 (GLO1), is a reactive metabolite formed via glycolytic flux. Decreased GLO1 activity in situ has been shown to result in an accumulation of MG and increased formation of advanced glycation endproducts, both of which can accumulate during physiological aging and at an accelerated rate in diabetes and other chronic degenerative diseases. To determine the physiological consequences which result from elevated MG levels and the role of MG and GLO1 in aging, wound healing in young (≤12 weeks) and old (≥52 weeks) wild-type mice was studied. Old mice were found to have a significantly slower rate of wound healing compared to young mice (74.9 ± 2.2 vs. 55.4 ± 1.5% wound closure at day 6; 26% decrease; p < 0.0001). This was associated with decreases in GLO1 transcription, expression and activity. The importance of GLO1 was confirmed in mice by inhibition of GLO1. Direct application of MG to the wounds of young mice, decreased wound healing by 24% compared to untreated mice, whereas application of BSA modified minimally by MG had no effect. Treatment of either young or old mice with aminoguanidine, a scavenger of free MG, significantly increased wound closure by 16% (66.8 ± 1.6 vs. 77.2 ± 3.1%; p < 0.05) and 64% (40.4 ± 7.9 vs. 66.4 ± 5.2%; p < 0.05), respectively, by day 6. As a result of the aminoguanidine treatment, the overall rate of wound healing in the old mice was restored to the level observed in the young mice. These findings were confirmed in vitro, as MG reduced migration and proliferation of fibroblasts derived from young and old, wild-type mice. The data demonstrate that the balance between MG and age-dependent GLO1 downregulation contributes to delayed wound healing in old mice.

Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction.

In vivo relevance of citrullinated proteins and the challenges in their detection

Citrullination is a posttranslational modification of arginine. It plays both a physiological role, for instance during apoptosis and epigenetics, and a pathological role in cancer or diseases of the central nervous system. Most research on citrullination to date focuses on its role in auto-immune diseases such as multiple sclerosis and rheumatoid arthritis. In this context, the exact knowledge of citrullination sites in a protein can provide invaluable information about the etiological importance of these citrullinated proteins. However, few techniques exist that can accurately detect citrullination on the peptide level. This review aims to give an overview of the different methods available to date for the detection of citrullinated proteins and peptides. These include 2D-SDS-PAGE and immunodetection, as well as specific mass spectrometry (MS) approaches, both labeled and unlabeled. These MS approaches have been developed to pinpoint the exact location of citrullination on the peptide level. Improving the currently existing detection strategies while focusing on the role of citrullinated proteins will be invaluable to elucidate the importance of this posttranslational modification in vivo.

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

A unified multitask architecture for predicting local protein properties

A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance.

Unbinding forces of single pertussis toxin-antibody complexes measured by atomic force spectroscopy correlate with their dissociation rates determined by surface plasmon resonance

An inactivated form of pertussis toxin (PTX) is the primary component of currently available acellular vaccines against Bordetella pertussis, the causative agent of whooping cough. The PTX analyzed here is purified at industrial scale and is subsequently inactivated using glutaraldehyde. The influence of this treatment on antibody recognition is of crucial importance and is analyzed in this study. Surface plasmon resonance (SPR) experiments using PTX and its inactivated form (toxoid) with 10 different monoclonal antibodies were conducted. PTX was found to recognize the antibodies with an average affinity of 1.34 ± 0.50 nM, and chemical inactivation caused only a modest decrease in affinity by a factor of approximately 4.5. However, glutaraldehyde treatment had contrary effects on the kinetic association constant k(a) and the dissociation constant k(d) . A significant reduction in k(a) was observed, whereas the dissociation of the toxoid from the bound antibody occurred slower than PTX. These data indicate that the chemical inactivation of PTX not only reduces the velocity of antibody recognition but also stabilizes the interaction with antibodies as shown by a reduction in k(d) . The same interactions were also studied by dynamic force spectroscopy (DFS). Data reveal a correlation between the k(d) values determined by SPR and the mean unbinding force as measured by DFS. The unbinding forces of one complex were determined as a function of the loading rate to directly estimate the k(d) value. Several interactions were impossible to be analyzed using SPR because of ultratight binding. Using DFS, the unbinding forces of these interactions were determined, which in turn could be used to estimate k(d) values. The use of DFS as a technique to study ultratight binding is discussed.

A single amino acid substitution in IIIf subfamily of basic helix-loop-helix transcription factor AtMYC1 leads to trichome and root hair patterning defects by abolishing its interaction with partner proteins in Arabidopsis

Plant trichomes and root hairs are powerful models for the study of cell fate determination. In Arabidopsis thaliana, trichome and root hair initiation requires a combination of three groups of proteins, including the WD40 repeat protein transparent TESTA GLABRA1 (TTG1), R2R3 repeat MYB protein GLABRA1 (GL1), or werewolf (WER) and the IIIf subfamily of basic helix-loop-helix (bHLH) protein GLABRA3 (GL3) or enhancer of GLABRA3 (EGL3). The bHLH component acts as a docking site for TTG1 and MYB proteins. Here, we isolated a mutant showing defects in trichome and root hair patterning that carried a point mutation (R173H) in AtMYC1 that encodes the fourth member of IIIf bHLH family protein. Genetic analysis revealed partial redundant yet distinct function between AtMYC1 and GL3/EGL3. GLABRA2 (GL2), an important transcription factor involved in trichome and root hair control, was down-regulated in Atmyc1 plants, suggesting the requirement of AtMYC1 for appropriate GL2 transcription. Like its homologs, AtMYC1 formed a complex with TTG1 and MYB proteins but did not dimerized. In addition, the interaction of AtMYC1 with MYB proteins and TTG1 was abrogated by the R173H substitution in Atmyc1-1. We found that this amino acid (Arg) is conserved in the AtMYC1 homologs GL3/EGL3 and that it is essential for their interaction with MYB proteins and for their proper functions. Our findings indicate that AtMYC1 is an important regulator of trichome and root hair initiation, and they reveal a novel amino acid necessary for protein-protein interactions and gene function in IIIf subfamily bHLH transcription factors.

Glucose autoxidation induces functional damage to proteins via modification of critical arginine residues

Nonenzymatic modification of proteins in hyperglycemia is a major mechanism causing diabetic complications. These modifications can have pathogenic consequences when they target active site residues, thus affecting protein function. In the present study, we examined the role of glucose autoxidation in functional protein damage using lysozyme and RGD-α3NC1 domain of collagen IV as model proteins in vitro. We demonstrated that glucose autoxidation induced inhibition of lysozyme activity as well as NC1 domain binding to α(V)β(3) integrin receptor via modification of critical arginine residues by reactive carbonyl species (RCS) glyoxal (GO) and methylglyoxal while nonoxidative glucose adduction to the protein did not affect protein function. The role of RCS in protein damage was confirmed using pyridoxamine which blocked glucose autoxidation and RCS production, thus protecting protein function, even in the presence of high concentrations of glucose. Glucose autoxidation may cause protein damage in vivo since increased levels of GO-derived modifications of arginine residues were detected within the assembly interface of collagen IV NC1 domains isolated from renal ECM of diabetic rats. Since arginine residues are frequently present within protein active sites, glucose autoxidation may be a common mechanism contributing to ECM protein functional damage in hyperglycemia and oxidative environment. Our data also point out the pitfalls in functional studies, particularly in cell culture experiments, that involve glucose treatment but do not take into account toxic effects of RCS derived from glucose autoxidation.

Binding site prediction for protein-protein interactions and novel motif discovery using re-occurring polypeptide sequences

Background

While there are many methods for predicting protein-protein interaction, very few can determine the specific site of interaction on each protein. Characterization of the specific sequence regions mediating interaction (binding sites) is crucial for an understanding of cellular pathways. Experimental methods often report false binding sites due to experimental limitations, while computational methods tend to require data which is not available at the proteome-scale. Here we present PIPE-Sites, a novel method of protein specific binding site prediction based on pairs of re-occurring polypeptide sequences, which have been previously shown to accurately predict protein-protein interactions. PIPE-Sites operates at high specificity and requires only the sequences of query proteins and a database of known binary interactions with no binding site data, making it applicable to binding site prediction at the proteome-scale.

Results

PIPE-Sites was evaluated using a dataset of 265 yeast and 423 human interacting proteins pairs with experimentally-determined binding sites. We found that PIPE-Sites predictions were closer to the confirmed binding site than those of two existing binding site prediction methods based on domain-domain interactions, when applied to the same dataset. Finally, we applied PIPE-Sites to two datasets of 2347 yeast and 14,438 human novel interacting protein pairs predicted to interact with high confidence. An analysis of the predicted interaction sites revealed a number of protein subsequences which are highly re-occurring in binding sites and which may represent novel binding motifs.

Conclusions

PIPE-Sites is an accurate method for predicting protein binding sites and is applicable to the proteome-scale. Thus, PIPE-Sites could be useful for exhaustive analysis of protein binding patterns in whole proteomes as well as discovery of novel binding motifs. PIPE-Sites is available online at http://pipe-sites.cgmlab.org/.

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

BACKGROUND

Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.

RESULTS

We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.

CONCLUSIONS

The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.

AVAILABILITY

Datasets and software are available at http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm.

Methylglyoxal modulates immune responses: relevance to diabetes

Increased methylglyoxal (MG) concentrations and formation of advanced glycation end-products (AGEs) are major pathways of glycaemic damage in diabetes, leading to vascular and neuronal complications. Diabetes patients also suffer increased susceptibility to many common infections, the underlying causes of which remain elusive. We hypothesized that immune glycation damage may account for this increased susceptibility. We previously showed that the reaction mixture (RM) for MG glycation of peptide blocks up regulation of CD83 in myeloid cells and inhibits primary stimulation of T cells. Here, we continue to investigate immune glycation damage, assessing surface and intracellular cytokine protein expression by flow cytometry, T-cell proliferation using a carboxyfluorescein succinimidyl ester assay, and mRNA levels by RT-PCR. We show that the immunomodulatory component of this RM was MG itself, with MG alone causing equivalent block of CD83 and loss of primary stimulation. Block of CD83 expression could be reversed by MG scavenger N-acetyl cysteine. Further, MG within RM inhibited stimulated production of interleukin (IL)-10 protein from myeloid cells plus interferon (IFN)-gamma and tumour necrosis factor (TNF)-alpha from T cells. Loss of IL-10 and IFN-gamma was confirmed by RT-PCR analysis of mRNA, while TNF-alpha message was raised. Loss of TNF-alpha protein was also shown by ELISA of culture supernatants. In addition, MG reduced major histocompatibility complex (MHC) class I expression on the surface of myeloid cells and increased their propensity to apoptose. We conclude that MG is a potent suppressor of myeloid and T-cell immune function and may be a major player in diabetes-associated susceptibility to infection.

Methylglyoxal: possible link between hyperglycaemia and immune suppression?

No matter the cause of diabetes, the result is always hyperglycaemia. This excess glucose metabolism drives several damage pathways and raises concentrations of the reactive dicarbonyl, methylglyoxal (MG). MG can modify the structure and function of target molecules by forming advanced glycation end-products (AGEs) that act through their receptor (RAGE) to perpetuate vascular and neuronal injury responsible for long-term complications of diabetes. Diabetes patients also suffer lower resistance to many common infections, although the cause(s) for this lower resistance remains elusive. Here, we review recent evidence concerning immune suppression in diabetes and discuss the effects of MG on components of the immune system. We suggest that MG could be a missing link between hyperglycaemia and immune suppression in diabetes.

Exosites mediate the anti-inflammatory effects of a multifunctional serpin from the saliva of the tick Ixodes ricinus

Serine protease inhibitors (serpins) are a structurally related but functionally diverse family of ubiquitous proteins. We previously described Ixodes ricinus immunosuppressor (Iris) as a serpin from the saliva of the tick I. ricinus displaying high affinity for human leukocyte elastase. Iris also displays pleotropic effects because it interferes with both the immune response and hemostasis of the host. It thus inhibits lymphocyte proliferation and the secretion of interferon-gamma or tumor necrosis factor-alpha by peripheral blood mononuclear cells, and also platelet adhesion, coagulation and fibrinolysis. Its ability to interfere with coagulation and fibrinolysis, but not platelet adhesion, depends on the integrity of its antiproteolytic reactive center loop domain. Here, we dissect the mechanisms underlying the interaction of recombinant Iris with peripheral blood mononuclear cells. We show that Iris binds to monocytes/macrophages and inhibits their ability to secrete tumor necrosis factor-alpha. Recombinant Iris also has a protective role in endotoxemic shock. The anti-inflammatory ability of Iris does not depend on its antiprotease activity. Moreover, we pinpoint the exosites involved in this activity.

Progress and challenges in predicting protein-protein interaction sites

The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

Prediction of protein-protein interaction sites in sequences and 3D structures by random forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

In their active state, proteins—the workhorses of a living cell—need to have a defined 3D structure. The majority of functions in the living cell are performed through protein interactions that occur through specific, often unknown, residues on their surfaces. We can study protein interactions either qualitatively (interaction: yes/no) using large-scale, high-throughput experiments or determine specific interaction sites by using biophysical techniques, such as, for example, X-ray crystallography, that are much more laborious and yet unable to provide us with a complete interaction map within the cell. This paper presents the machine learning classification method termed “Random Forests” in its application to predicting interaction sites. We use interaction data from available experimental evidence to train the classifier and predict the interacting residues on proteins with unknown 3D structures. Using this approach, we are able to predict many more interactions in greater detail (i.e., to accurately predict most of the binding site) and with that to infer knowledge about the functions of unknown proteins.

Prediction of protein-protein interactions: a study of the co-evolution model

The concept of molecular co-evolution drew attention in recent years as the basis for several algorithms for the prediction of protein-protein interactions. While being successful on specific data, the concept has never been tested on a large set of proteins. In this chapter we analyze the feasibility of the co-evolution principle for protein-protein interaction prediction through one of its derivatives, the correlated divergence model. Given two proteins, the model compares the patterns of divergence of their families and assigns a score based on the correlation between the two. The working hypothesis of the model postulates that the stronger the correlation the more likely is that the two proteins interact. Several novel variants of this model are considered, including algorithms that attempt to identify the subset of the database proteins (the homologs of the query proteins) that are more likely to interact. We test the models over a large set of protein interactions extracted from several sources, including BIND, DIP, and HPRD.

Protein glycation in vivo: functional and structural effects on yeast enolase

Protein glycation is involved in structure and stability changes that impair protein functionality, which is associated with several human diseases, such as diabetes and amyloidotic neuropathies (Alzheimer's disease, Parkinson's disease and Andrade's syndrome). To understand the relationship of protein glycation with protein dysfunction, unfolding and β-fibre formation, numerous studies have been carried out in vitro. All of these previous experiments were conducted in non-physiological or pseudo-physiological conditions that bear little to no resemblance to what may happen in a living cell. In vivo, glycation occurs in a crowded and organized environment, where proteins are exposed to a steady-state of glycation agents, namely methylglyoxal, whereas in vitro, a bolus of a suitable glycation agent is added to diluted protein samples. In the present study, yeast was shown to be an ideal model to investigate glycation in vivo since it shows different glycation phenotypes and presents specific protein glycation targets. A comparison between in vivo glycated enolase and purified enolase glycated in vitro revealed marked differences. All effects regarding structure and stability changes were enhanced when the protein was glycated in vitro. The same applies to enzyme activity loss, dimer dissociation and unfolding. However, the major difference lies in the nature and location of specific advanced glycation end-products. In vivo, glycation appears to be a specific process, where the same residues are consistently modified in the same way, whereas in vitro several residues are modified with different advanced glycation end-products.

The interactome: predicting the protein-protein interactions in cells

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

Effects of six APOA5 variants, identified in patients with severe hypertriglyceridemia, on in vitro lipoprotein lipase activity and receptor binding

OBJECTIVE

The purpose of this study was to identify rare APOA5 variants in 130 severe hypertriglyceridemic patients by sequencing, and to test their functionality, since no patient recall was possible.

METHODS AND RESULTS

We studied the impact in vitro on LPL activity and receptor binding of 3 novel heterozygous variants, apoAV-E255G, -G271C, and -H321L, together with the previously reported -G185C, -Q139X, -Q148X, and a novel construct -Delta139 to 147. Using VLDL as a TG-source, compared to wild type, apoAV-G255, -L321 and -C185 showed reduced LPL activation (-25% [P=0.005], -36% [P<0.0001], and -23% [P=0.02]), respectively). ApoAV-C271, -X139, -X148, and Delta139 to 147 had little affect on LPL activity, but apoAV-X139, -X148, and -C271 showed no binding to LDL-family receptors, LR8 or LRP1. Although the G271C proband carried no LPL and APOC2 mutations, the H321L carrier was heterozygous for LPL P207L. The E255G carrier was homozygous for LPL W86G, yet only experienced severe hypertriglyceridemia when pregnant.

CONCLUSIONS

The in vitro determined function of these apoAV variants only partly explains the high TG levels seen in carriers. Their occurrence in the homozygous state, coinheritance of LPL variants or common APOA5 TG-raising variant in trans, appears to be essential for their phenotypic expression.

Protein and nucleotide damage by glyoxal and methylglyoxal in physiological systems--role in ageing and disease

Glycation of proteins, nucleotides and basic phospholipids by glyoxal and methylglyoxal--physiological substrates of glyoxalase 1--is potentially damaging to the proteome, genome and lipidome. Glyoxalase 1 suppresses glycation by these alpha-oxoaldehyde metabolites and thereby represents part of the enzymatic defence against glycation. Albert Szent-Györgyi pioneered and struggled to understand the physiological function of methylglyoxal and the glyoxalase system. We now appreciate that glyoxalase 1 protects against dicarbonyl modifications of the proteome, genome and lipome. Latest research suggests there are functional modifications of this process--implying a role in cell signalling, ageing and disease.

A hidden Markov model for predicting protein interfaces

Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.

Statistical analysis of physical-chemical properties and prediction of protein-protein interfaces

We have developed a fully automated method, InterProSurf, to predict interacting amino acid residues on protein surfaces of monomeric 3D structures. Potential interacting residues are predicted based on solvent accessible surface areas, a new scale for interface propensities, and a cluster algorithm to locate surface exposed areas with high interface propensities. Previous studies have shown the importance of hydrophobic residues and specific charge distribution as characteristics for interfaces. Here we show differences in interface and surface regions of all physical chemical properties of residues as represented by five quantitative descriptors. In the current study a set of 72 protein complexes with known 3D structures were analyzed to obtain interface propensities of residues, and to find differences in the distribution of five quantitative descriptors for amino acid residues. We also investigated spatial pair correlations of solvent accessible residues in interface and surface areas, and compared log-odds ratios for interface and surface areas. A new scoring method to predict potential functional sites on the protein surface was developed and tested for a new dataset of 21 protein complexes, which were not included in the original training dataset. Empirically we found that the algorithm achieves a good balance in the accuracy of precision and sensitivity by selecting the top eight highest scoring clusters as interface regions. The performance of the method is illustrated for a dimeric ATPase of the hyperthermophile, Methanococcus jannaschii, and the capsid protein of Human Hepatitis B virus. An automated version of the method can be accessed from our web server at http://curie.utmb.edu/prosurf.html.

Mutational analysis of the TRE2 oncogene encoding an inactive RabGAP

The TRE2 oncoprotein is structurally related to the RabGAP (GTPase-activating protein) family. However, TRE2 seems enzymatically inactive. Two regions are important for its lack of GAP activity. First, the TBC domain, forming the catalytically active domain of RabGAPs, is non-functional in the oncoprotein. Also involved in TRE2 inactivity is the 93-aa region flanking the TBC domain on the C-terminal side. In order to identify the residues responsible for non-functionality, we performed hydrophobic cluster analysis of the oncoprotein sequence, combined with secondary structure prediction, receptor-binding domain analysis, and a tilted peptide calculation. These analyses were complemented with site-directed and random mutagenesis experiments. On the basis of our data, we hypothesize that the lack of secondary structure of the region flanking the TBC domain in TRE2 may explain why this region plays a role in the lack of GAP activity, even when a potentially functional TBC domain is present.

Computational prediction of protein-protein interactions

Recently a number of computational approaches have been developed for the prediction of protein-protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.

Protein-protein interaction hotspots carved into sequences

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.

High-throughput identification of interacting protein-protein binding sites

Background

With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Therefore, methods that are able to identify whether these binding sites interact are needed.

Results

We have developed a new method using a machine learning approach to detect if protein binding sites, once identified, interact with each other. The method exploits information relating to sequence and structural complementary across protein interfaces and has been tested on a non-redundant data set consisting of 584 homo-dimers and 198 hetero-dimers extracted from the PDB. Results indicate 87.4% of the interacting binding sites and 68.6% non-interacting binding sites were correctly identified. Furthermore, we built a pipeline that links this method to a modified version of our previously developed method that predicts the location of binding sites.

Conclusion

We have demonstrated that this high-throughput pipeline is capable of identifying binding sites for proteins, their interacting binding sites and, ultimately, their binding partners on a large scale.

Identification of hot regions in protein-protein interactions by sequential pattern mining

BACKGROUND

Identification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.

RESULTS

The experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach.

CONCLUSION

This work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery.

Advanced glycation endproducts: what is their relevance to diabetic complications?

Glycation is a major cause of spontaneous damage to proteins in physiological systems. This is exacerbated in diabetes as a consequence of the increase in glucose and other saccharides derivatives in plasma and at the sites of vascular complications. Protein damage by the formation of early glycation adducts is limited to lysine side chain and N-terminal amino groups whereas later stage adducts, advanced glycation endproducts (AGEs), modify these and also arginine and cysteine residues. Metabolic dysfunction in vascular cells leads to the increased formation of methylglyoxal which adds disproportionately to the glycation damage in hyperglycaemia. AGE-modified proteins undergo cellular proteolysis leading to the formation and urinary excretion of glycation free adducts. AGEs may potentiate the development of diabetic complications by activation of cell responses by AGE-modified proteins interacting with specific cell surface receptors, activation of cell responses by AGE free adducts, impairment of protein-protein and enzyme-substrate interactions by AGE residue formation, and increasing resistance to proteolysis of extracellular matrix proteins. The formation of AGEs is suppressed by intensive glycaemic control, and may in future be suppressed by thiamine and pyridoxamine supplementation, and several other pharmacological agents. Increasing expression of enzymes of the enzymatic defence against glycation provides a novel and potentially effective future therapeutic strategy to suppress protein glycation.

Protein-protein binding-sites prediction by protein surface structure conservation

A new algorithm to predict protein-protein binding sites using conservation of both protein surface structure and physical-chemical properties in structurally similar proteins is developed. Binding-site residues in proteins are known to be more conserved than the rest of the surface, and finding local surface similarities by comparing a protein to its structural neighbors can potentially reveal the location of binding sites on this protein. This approach, which has previously been used to predict binding sites for small ligands, is now extended to predict protein-protein binding sites. Examples of binding-site predictions for a set of proteins, which have previously been studied for sequence conservation in protein-protein interfaces, are given. The predicted binding sites and the actual binding sites are in good agreement. Our algorithm for finding conserved surface structures in a set of similar proteins is a useful tool for the prediction of protein-protein binding sites.

Prediction-based fingerprints of protein-protein interactions

The recognition of protein interaction sites is an important intermediate step toward identification of functionally relevant residues and understanding protein function, facilitating experimental efforts in that regard. Toward that goal, the authors propose a novel representation for the recognition of protein-protein interaction sites that integrates enhanced relative solvent accessibility (RSA) predictions with high resolution structural data. An observation that RSA predictions are biased toward the level of surface exposure consistent with protein complexes led the authors to investigate the difference between the predicted and actual (i.e., observed in an unbound structure) RSA of an amino acid residue as a fingerprint of interaction sites. The authors demonstrate that RSA prediction-based fingerprints of protein interactions significantly improve the discrimination between interacting and noninteracting sites, compared with evolutionary conservation, physicochemical characteristics, structure-derived and other features considered before. On the basis of these observations, the authors developed a new method for the prediction of protein-protein interaction sites, using machine learning approaches to combine the most informative features into the final predictor. For training and validation, the authors used several large sets of protein complexes and derived from them nonredundant representative chains, with interaction sites mapped from multiple complexes. Alternative machine learning techniques are used, including Support Vector Machines and Neural Networks, so as to evaluate the relative effects of the choice of a representation and a specific learning algorithm. The effects of induced fit and uncertainty of the negative (noninteracting) class assignment are also evaluated. Several representative methods from the literature are reimplemented to enable direct comparison of the results. Using rigorous validation protocols, the authors estimated that the new method yields the overall classification accuracy of about 74% and Matthews correlation coefficients of 0.42, as opposed to up to 70% classification accuracy and up to 0.3 Matthews correlation coefficient for methods that do not utilize RSA prediction-based fingerprints. The new method is available at http://sppider.cchmc.org.

Two potentially important elements of the hepatitis B virus large envelope protein are dispensable for the infectivity of hepatitis delta virus

Previous studies have attempted to clarify the roles of the pre-S1 and pre-S2 domains of the large envelope protein of hepatitis B virus (HBV) in attachment and entry into susceptible cells. Difficulties arise in that these domains contain regions involved in the nucleocapsid assembly of HBV and overlapping with the coding regions of the viral polymerase and RNA sequences required for reverse transcription. Such difficulties can be circumvented with hepatitis delta virus (HDV), which needs the HBV large envelope protein only for infectivity. Thus, mutated HBV envelope proteins were examined for their effects on HDV infectivity. Changing the C-terminal region of pre-S1 critical for HBV assembly allowed the envelopment of HDV and had no effect on infectivity in primary human hepatocytes. Similarly, a deletion of the 12 amino acids of a putative translocation motif (TLM) in pre-S2 had no effect. Thus, these two regions are not necessary for HDV infectivity and, by inference, are not needed for HBV attachment and entry into susceptible cells.

PBP5 complementation of a PBP3 deficiency in Enterococcus hirae

The low susceptibility of enterococci to beta-lactams is due to the activity of the low-affinity penicillin-binding protein 5 (PBP5). One important feature of PBP5 is its ability to substitute for most, if not all, penicillin-binding proteins when they are inhibited. That substitution activity was analyzed in Enterococcus hirae SL2, a mutant whose pbp5 gene was interrupted by the nisRK genes and whose PBP3 synthesis was submitted to nisin induction. Noninduced SL2 cells were unable to divide except when plasmid-borne pbp5 genes were present, provided that the PBP5 active site was functional. Potential protein-protein interaction sites of the PBP5 N-terminal module were mutagenized by site-directed mutagenesis. The T167-L184 region (designated site D) appeared to be an essential intramolecular site needed for the stability of the protein. Mutations made in the two globular domains present in the N-terminal module indicated that they were needed for the suppletive activity. The P197-N209 segment (site E) in one of these domains seemed to be particularly important, as single and double mutations reduced or almost completely abolished, respectively, the action of PBP5.

Anti-hemostatic effects of a serpin from the saliva of the tick Ixodes ricinus

Serpins (serine protease inhibitors) are a large family of structurally related proteins found in a wide variety of organisms, including hematophagous arthropods. Protein analyses revealed that Iris, previously described as an immunomodulator secreted in the tick saliva, is related to the leukocyte elastase inhibitor and possesses serpin motifs, including the reactive center loop (RCL), which is involved in the interaction between serpins and serine proteases. Only serine proteases were inhibited by purified recombinant Iris (rIris), whereas mutants L339A and A332P were found devoid of any protease inhibitory activity. The highest Ka was observed with human leukocyte-elastase, suggesting that elastase-like proteases are the natural targets of Iris. In addition, mutation M340R completely changed both Iris substrate specificity and affinity. This likely identified Met-340 as amino acid P1 in the RCL. The effects of rIris and its mutants were also tested on primary hemostasis, blood clotting, and fibrinolysis. rIris increased platelet adhesion, the contact phase-activated pathway of coagulation, and fibrinolysis times in a dose-dependent manner, whereas rIris mutant L339A affected only platelet adhesion. Taken together, these results indicate that Iris disrupts coagulation and fibrinolysis via the anti-proteolytic RCL domain. One or more other domains could be responsible for primary hemostasis inhibition. To our knowledge, this is the first ectoparasite serpin that interferes with both hemostasis and the immune response.

Identifying protein-protein interfacial residues in heterocomplexes using residue conservation scores

Identifying protein-protein interfaces is crucial for structural biology. Because of the constraints in wet experiments, many computational methods have been proposed. Without knowing any information about the partner chains, a new method of predicting protein-protein interaction interface residues purely based on evolutionary information in heterocomplexes is proposed here. Unlike traditional approaches using multiple sequence alignment profiles to represent the conservation level for each residue, we make predictions based on the concept of residue conservation scores so that the dimension of the feature vector for each residue can be drastically reduced, at least 20 times less than conventional methods. Based on the representation approach, a simple linear discriminant function is used to make predictions, so the computational complexity of the whole prediction procedure can also be greatly decreased. By testing our approach on 69 heterocomplex chains, experimental results demonstrate the performance of our approach is indeed superior to current existing methods.

Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale

MOTIVATION

Protein-protein interaction, mediated by protein interaction sites, is intrinsic to many functional processes in the cell. In this paper, we propose a novel method to discover patterns in protein interaction sites. We observed from protein interaction networks that there exist a kind of significant substructures called interacting protein group pairs, which exhibit an all-versus-all interaction between the two protein-sets in such a pair. The full-interaction between the pair indicates a common interaction mechanism shared by the proteins in the pair, which can be referred as an interaction type. Motif pairs at the interaction sites of the protein group pairs can be used to represent such interaction type, with each motif derived from the sequences of a protein group by standard motif discovery algorithms. The systematic discovery of all pairs of interacting protein groups from large protein interaction networks is a computationally challenging problem. By a careful and sophisticated problem transformation, the problem is solved using efficient algorithms for mining frequent patterns, a problem extensively studied in data mining.

RESULTS

We found 5349 pairs of interacting protein groups from a yeast interaction dataset. The expected value of sequence identity within the groups is only 7.48%, indicating non-homology within these protein groups. We derived 5343 motif pairs from these group pairs, represented in the form of blocks. Comparing our motifs with domains in the BLOCKS and PRINTS databases, we found that our blocks could be mapped to an average of 3.08 correlated blocks in these two databases. The mapped blocks occur 4221 out of total 6794 domains (protein groups) in these two databases. Comparing our motif pairs with iPfam consisting of 3045 interacting domain pairs derived from PDB, we found 47 matches occurring in 105 distinct PDB complexes. Comparing with another putative domain interaction database InterDom, we found 203 matches.

AVAILABILITY

http://research.i2r.a-star.edu.sg/BindingMotifPairs/resources.

SUPPLEMENTARY INFORMATION

http://research.i2r.a-star.edu.sg/BindingMotifPairs and Bioinformatics online.