A fast method to predict protein interaction sites from sequences

Increased cellular protein modification by methylglyoxal activates endoplasmic reticulum-based sensors of the unfolded protein response

The unfolded protein response (UPR) detects increased misfolded proteins and activates protein refolding, protein degradation and inflammatory responses. UPR sensors in the endoplasmic reticulum, IRE1α and PERK, bind and are activated by proteins with unexpected surface hydrophobicity, whereas sensor ATF6 is activated by proteolytic cleavage when released from complexation with protein disulfide isomerases (PDIs). Metabolic dysfunction leading to the formation of misfolded proteins with surface hydrophobicity and disruption of ATF6-PDI complexes leading to activation of UPR sensors remains unclear. The cellular concentration of reactive dicarbonyl metabolite, methylglyoxal (MG), is increased in impaired metabolic health, producing increased MG-modified cellular proteins. Herein we assessed the effect of high glucose concentration and related increased cellular MG on activation status of IRE1α, PERK and ATF6. Human aortal endothelial cells and HMEC-1 microvascular endothelial cells were incubated in low and high glucose concentration to model blood glucose control, with increase or decrease of MG by silencing or increasing expression of glyoxalase 1 (Glo1), which metabolizes MG. Increased MG induced by high glucose concentration activated IRE1α, PERK and ATF6 and related downstream signalling leading to increased chaperone, apoptotic and inflammatory gene expression. Correction of increased MG by increasing Glo1 expression prevented UPR activation. MG modification of proteins produces surface hydrophobicity through arginine-derived hydroimidazolone MG-H1 formation, with related protein unfolding and preferentially targets PDIs and chaperone pathways for modification. It thereby poses a major challenge to proteostasis and activates UPR sensors. Pharmacological decrease of MG with Glo1 inducer, trans-resveratrol and hesperetin in combination, offers a novel treatment strategy to counter UPR-related cell dysfunction, particularly in hyperglycemia associated with diabetes.

Using evolutionary data to make sense of macromolecules with a "face-lifted" ConSurf

The ConSurf web-sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per-site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand-binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user-friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model-based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re-implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin-1, the main protein of membrane invaginations called caveolae.

RLBind: a deep learning method to predict RNA-ligand binding sites

Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics compared with regular therapeutics targeting proteins. RNAs can provide many potential drug targets with diverse structures and functions. However, up to now, only a few methods have been proposed. Predicting RNA-small molecule binding sites still remains a big challenge. New computational model is required to better extract the features and predict RNA-small molecule binding sites more accurately. In this paper, a deep learning model, RLBind, was proposed to predict RNA-small molecule binding sites from sequence-dependent and structure-dependent properties by combining global RNA sequence channel and local neighbor nucleotides channel. To our best knowledge, this research was the first to develop a convolutional neural network for RNA-small molecule binding sites prediction. Furthermore, RLBind also can be used as a potential tool when the RNA experimental tertiary structure is not available. The experimental results show that RLBind outperforms other state-of-the-art methods in predicting binding sites. Therefore, our study demonstrates that the combination of global information for full-length sequences and local information for limited local neighbor nucleotides in RNAs can improve the model's predictive performance for binding sites prediction. All datasets and resource codes are available at https://github.com/KailiWang1/RLBind.

An Introduction to the Special Issue "Protein Glycation in Food, Nutrition, Health and Disease"

On 20-24 September 2021, leading researchers in the field of glycation met online at the 14th International Symposium on the Maillard Reaction (IMARS-14), hosted by the authors of this introductory editorial, who are from Doha, Qatar [...].

Prediction of Protein-Protein Interaction Sites by Multifeature Fusion and RF with mRMR and IFS

Prediction of protein-protein interaction (PPI) sites is one of the most perplexing problems in drug discovery and computational biology. Although significant progress has been made by combining different machine learning techniques with a variety of distinct characteristics, the problem still remains unresolved. In this study, a technique for PPI sites is presented using a random forest (RF) algorithm followed by the minimum redundancy maximal relevance (mRMR) approach, and the method of incremental feature selection (IFS). Physicochemical properties of proteins and the features of the residual disorder, sequence conservation, secondary structure, and solvent accessibility are incorporated. Five 3D structural characteristics are also used to predict PPI sites. Analysis of features shows that 3D structural features such as relative solvent-accessible surface area (RASA) and surface curvature (SC) help in the prediction of PPI sites. Results show that the performance of the proposed predictor is superior to several other state-of-the-art predictors, whose average prediction accuracy is 81.44%, sensitivity is 82.17%, and specificity is 80.71%, respectively. The proposed predictor is expected to become a helpful tool for finding PPI sites, and the feature analysis presented in this study will give useful insights into protein interaction mechanisms.

Integrated structure-based protein interface prediction

Background

Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model.

Results

We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces.

Conclusions

The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04852-2.

Methylglyoxal and glyoxalase 1-a metabolic stress pathway-linking hyperglycemia to the unfolded protein response and vascular complications of diabetes

The study of the glyoxalase system by Thornalley and co-workers in clinical diabetes mellitus and correlation with diabetic complications revealed increased exposure of patients with diabetes to the reactive, dicarbonyl metabolite methylglyoxal (MG). Twenty-eight years later, extended and built on by Thornalley and co-workers and others, the glyoxalase system is an important pathway contributing to the development of insulin resistance and vascular complications of diabetes. Other related advances have been: characterization of a new kind of metabolic stress—‘dicarbonyl stress’; identification of the major physiological advanced glycation endproduct (AGE), MG-H1; physiological substrates of the unfolded protein response (UPR); new therapeutic agents—‘glyoxalase 1 (Glo1) inducers’; and a refined mechanism underlying the link of dysglycemia to the development of insulin resistance and vascular complications of diabetes.

Anticoagulant Oligonucleotide-Peptide Conjugates: Identification of Thrombin Aptamer Conjugates with Improved Characteristics

Oligonucleotide–peptide conjugates (OPCs) are a promising class of biologically active compounds with proven potential for improving nucleic acid therapeutics. OPCs are commonly recognized as an efficient instrument to enhance the cellular delivery of therapeutic nucleic acids. In addition to this application field, OPCs have an as yet unexplored potential for the post-SELEX optimization of DNA aptamers. In this paper, we report the preparation of designer thrombin aptamer OPCs with peptide side chains anchored to a particular thymidine residue of the aptamer. The current conjugation strategy utilizes unmodified short peptides and support-bound protected oligonucleotides with activated carboxyl functionality at the T3 thymine nucleobase. The respective modification of the oligonucleotide strand was implemented using N3-derivatized thymidine phosphoramidite. Aptamer OPCs retained the G-quadruplex architecture of the parent DNA structure and showed minor to moderate stabilization. In a series of five OPCs, conjugates bearing T3–Ser–Phe–Asn (SFN) or T3–Tyr–Trp–Asn (YWN) side chains exhibited considerably improved anticoagulant characteristics. Molecular dynamics studies of the aptamer OPC complexes with thrombin revealed the roles of the amino acid nature and sequence in the peptide subunit in modulating the anticoagulant activity.

Using ConSurf to Detect Functionally Important Regions in RNA

The ConSurf web server (https://consurf.tau.ac.il/) for using evolutionary data to detect functional regions is useful for analyzing proteins. The analysis is based on the premise that functional regions, which may for example facilitate ligand binding and catalysis, often evolve slowly. The analysis requires finding enough effective, i.e., non-redundant, sufficiently remote homologs. Indeed, the ConSurf pipeline, which is based on state-of-the-art protein sequence databases and analysis tools, is highly valuable for protein analysis. ConSurf also allows evolutionary analysis of RNA, but the analysis often fails due to insufficient data, particularly the inability of the current pipeline to detect enough effective RNA homologs. This is because the RNA search tools and databases offered are not as good as those used for protein analysis. Fortunately, ConSurf also allows importing external collections of homologs in the form of a multiple sequence alignment (MSA). Leveraging this, here we describe various protocols for constructing MSAs for successful ConSurf analysis of RNA queries. We report the level of success of these protocols on an exemplary set comprising a dozen RNA molecules of diverse structure and function. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Standard ConSurf evolutionary conservation analysis of an RNA query. Basic Protocol 2: ConSurf evolutionary conservation analysis of an RNA query with external MSA. Support Protocol 1: Construction of an MSA for an RNA query using other online servers. Support Protocol 2: Construction of an MSA for an RNA query using nHMMER locally.

The Role of Glyoxalase in Glycation and Carbonyl Stress Induced Metabolic Disorders

Glycation refers to the covalent binding of sugar molecules to macromolecules, such as DNA, proteins, and lipids in a non-enzymatic reaction, resulting in the formation of irreversibly bound products known as advanced glycation end products (AGEs). AGEs are synthesized in high amounts both in pathological conditions, such as diabetes and under physiological conditions resulting in aging. The body's anti-glycation defense mechanisms play a critical role in removing glycated products. However, if this defense system fails, AGEs start accumulating, which results in pathological conditions. Studies have been shown that increased accumulation of AGEs acts as key mediators in multiple diseases, such as diabetes, obesity, arthritis, cancer, atherosclerosis, decreased skin elasticity, male erectile dysfunction, pulmonary fibrosis, aging, and Alzheimer's disease. Furthermore, glycation of nucleotides, proteins, and phospholipids by α-oxoaldehyde metabolites, such as glyoxal (GO) and methylglyoxal (MGO), causes potential damage to the genome, proteome, and lipidome. Glyoxalase-1 (GLO-1) acts as a part of the anti-glycation defense system by carrying out detoxification of GO and MGO. It has been demonstrated that GLO-1 protects dicarbonyl modifications of the proteome and lipidome, thereby impeding the cell signaling and affecting age-related diseases. Its relationship with detoxification and anti-glycation defense is well established. Glycation of proteins by MGO and GO results in protein misfolding, thereby affecting their structure and function. These findings provide evidence for the rationale that the functional modulation of the GLO pathway could be used as a potential therapeutic target. In the present review, we summarized the newly emerged literature on the GLO pathway, including enzymes regulating the process. In addition, we described small bioactive molecules with the potential to modulate the GLO pathway, thereby providing a basis for the development of new treatment strategies against age-related complications.

SeRenDIP-CE: Sequence-based Interface Prediction for Conformational Epitopes

Motivation

Antibodies play an important role in clinical research and biotechnology, with their specificity determined by the interaction with the antigen’s epitope region, as a special type of protein–protein interaction (PPI) interface. The ubiquitous availability of sequence data, allows us to predict epitopes from sequence in order to focus time-consuming wet-lab experiments toward the most promising epitope regions. Here, we extend our previously developed sequence-based predictors for homodimer and heterodimer PPI interfaces to predict epitope residues that have the potential to bind an antibody.

Results

We collected and curated a high quality epitope dataset from the SAbDab database. Our generic PPI heterodimer predictor obtained an AUC-ROC of 0.666 when evaluated on the epitope test set. We then trained a random forest model specifically on the epitope dataset, reaching AUC 0.694. Further training on the combined heterodimer and epitope datasets, improves our final predictor to AUC 0.703 on the epitope test set. This is better than the best state-of-the-art sequence-based epitope predictor BepiPred-2.0. On one solved antibody–antigen structure of the COVID19 virus spike receptor binding domain, our predictor reaches AUC 0.778. We added the SeRenDIP-CE Conformational Epitope predictors to our webserver, which is simple to use and only requires a single antigen sequence as input, which will help make the method immediately applicable in a wide range of biomedical and biomolecular research.

Availability and implementation

Webserver, source code and datasets at www.ibi.vu.nl/programs/serendipwww/.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Dual-Role of Methylglyoxal in Tumor Progression - Novel Therapeutic Approaches

One of the hallmarks of cancer cells is their metabolic reprogramming, which includes the preference for the use of anaerobic glycolysis to produce energy, even in presence of normal oxygen levels. This phenomenon, known as “Warburg effect”, leads to the increased production of reactive intermediates. Among these Methylglyoxal (MGO), a reactive dicarbonyl known as the major precursor of the advanced glycated end products (AGEs), is attracting great attention. It has been well established that endogenous MGO levels are increased in several types of cancer, however the MGO contribution in tumor progression is still debated. Although an anti-cancer role was initially attributed to MGO due to its cytotoxicity, emerging evidence has highlighted its pro-tumorigenic role in several types of cancer. These apparently conflicting results are explained by the hormetic potential of MGO, in which lower doses of MGO are able to establish an adaptive response in cancer cells while higher doses cause cellular apoptosis. Therefore, the extent of MGO accumulation and the tumor context are crucial to establish MGO contribution to cancer progression. Several therapeutic approaches have been proposed and are currently under investigation to inhibit the pro-tumorigenic action of MGO. In this review, we provide an overview of the early and latest evidence regarding the role of MGO in cancer, in order to define its contribution in tumor progression, and the therapeutic strategies aimed to counteract the tumor growth.

Truncated YY1 interacts with BASP1 through a 339KLK341 motif in YY1 and suppresses vascular smooth muscle cell growth and intimal hyperplasia after vascular injury

AIMS

In-stent restenosis and late stent thrombosis are complications associated with the use of metallic and drug-coated stents. Strategies that inhibit vascular smooth muscle cell (SMC) proliferation without affecting endothelial cell (EC) growth would be helpful in reducing complications arising from percutaneous interventions. Our group previously showed that the forced expression of the injury-inducible zinc finger (ZNF) transcription factor, yin yang-1 (YY1) comprising 414 residues inhibits neointima formation in carotid arteries of rabbits and rats. YY1 inhibits SMC proliferation without affecting EC growth. Identifying a shorter version of YY1 retaining cell-selective inhibition would make it more amenable for potential use as a gene therapeutic agent.

METHODS AND RESULTS

We dissected YY1 into a range of shorter fragments (YY1A-D, YY1Δ) and found that the first two ZNFs in YY1 (construct YY1B, spanning 52 residues) repressed SMC proliferation. Receptor Binding Domain analysis predicts a three residue (339KLK341) interaction domain. Mutation of 339KLK341 to 339AAA341 in YY1B (called YY1Bm) abrogated YY1B's ability to inhibit SMC but not EC proliferation and migration. Incubation of recombinant GST-YY1B and GST-YY1Bm with SMC lysates followed by precipitation with glutathione-agarose beads and mass spectrometric analysis identified a novel interaction between YY1B and BASP1. Overexpression of BASP1, like YY1, inhibited SMC but not EC proliferation and migration. BASP1 siRNA partially rescued SMC from growth inhibition by YY1B. In the rat carotid balloon injury model, adenoviral overexpression of YY1B, like full-length YY1, reduced neointima formation, whereas YY1Bm had no such effect. CD31 immunostaining suggested YY1B could increase re-endothelialization in a 339KLK341-dependent manner.

CONCLUSIONS

These studies identify a truncated form of YY1 (YY1B) that can interact with BASP1 and inhibits SMC proliferation, migration and intimal hyperplasia after balloon injury of rat carotid arteries as effectively as full length YY1. We demonstrate the therapeutic potential of YY1B in vascular proliferative disease.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Vulnerabilities of the SARS-CoV-2 Virus to Proteotoxicity-Opportunity for Repurposed Chemotherapy of COVID-19 Infection

The global pandemic of COVID-19 disease caused by infection with the SARS-CoV-2 coronavirus, has produced an urgent requirement and search for improved treatments while effective vaccines are developed. A strategy for improved drug therapy is to increase levels of endogenous reactive metabolites for selective toxicity to SARS-CoV-2 by preferential damage to the viral proteome. Key reactive metabolites producing major quantitative damage to the proteome in physiological systems are: reactive oxygen species (ROS) and the reactive glycating agent methylglyoxal (MG); cysteine residues and arginine residues are their most susceptible targets, respectively. From sequenced-based prediction of the SARS-CoV-2 proteome, we found 0.8-fold enrichment or depletion of cysteine residues in functional domains of the viral proteome; whereas there was a 4.6-fold enrichment of arginine residues, suggesting SARS-CoV-2 is resistant to oxidative agents and sensitive to MG. For arginine residues of the SARS-CoV-2 coronavirus predicted to be in functional domains, we examined which are activated toward modification by MG - residues with predicted or expected low pKa by neighboring group in interactions. We found 25 such arginine residues, including 2 in the spike protein and 10 in the nucleoprotein. These sites were partially conserved in related coronaviridae: SARS-CoV and MERS. Finally, we identified drugs which increase cellular MG concentration to virucidal levels: antitumor drugs with historical antiviral activity, doxorubicin and paclitaxel. Our findings provide evidence of potential vulnerability of SARS-CoV-2 to inactivation by MG and a scientific rationale for repurposing of doxorubicin and paclitaxel for treatment of COVID-19 disease, providing efficacy and adequate therapeutic index may be established.

Glycolytic overload-driven dysfunction of periodontal ligament fibroblasts in high glucose concentration, corrected by glyoxalase 1 inducer

INTRODUCTION

Patients with diabetes have increased risk of periodontal disease, with increased risk of weakening of periodontal ligament and tooth loss. Periodontal ligament is produced and maintained by periodontal ligament fibroblasts (PDLFs). We hypothesized that metabolic dysfunction of PDLFs in hyperglycemia produces an accumulation of the reactive glycating agent, methylglyoxal (MG), leading to increased formation of the major advanced glycation endproduct, MG-H1 and PDLF dysfunction. The aim of this study was to assess if there is dicarbonyl stress and functional impairment of human PDLFs in primary culture in high glucose concentration-a model of hyperglycemia, to characterize the metabolic drivers of it and explore remedial intervention by the glyoxalase 1 inducer dietary supplement, trans-resveratrol and hesperetin combination (tRES-HESP).

RESEARCH DESIGN AND METHODS

Human PDLFs were incubated in low and high glucose concentration in vitro. Metabolic and enzymatic markers of MG and glucose control were quantified and related changes in the cytoplasmic proteome and cell function-binding to collagen-I, assessed. Reversal of PDLF dysfunction by tRES-HESP was explored.

RESULTS

In high glucose concentration cultures, there was a ca. twofold increase in cellular MG, cellular protein MG-H1 content and decreased attachment of PDLFs to collagen-I. This was driven by increased hexokinase-2 linked glucose metabolism and related increased MG formation. Proteomics analysis revealed increased abundance of chaperonins, heat shock proteins (HSPs), Golgi-to-endoplasmic reticulum transport and ubiquitin E3 ligases involved in misfolded protein degradation in high glucose concentration, consistent with activation of the unfolded protein response by increased misfolded MG-modified proteins. PDLF dysfunction was corrected by tRES-HESP.

CONCLUSIONS

Increased hexokinase-2 linked glucose metabolism produces dicarbonyl stress, increased MG-modified protein, activation of the unfolded protein response and functional impairment of PDLFs in high glucose concentration. tRES-HESP resolves this at source by correcting increased glucose metabolism and may be of benefit in prevention of diabetic periodontal disease.

Understanding Diabetic Neuropathy: Focus on Oxidative Stress

Diabetic neuropathy is one of the clinical syndromes characterized by pain and substantial morbidity primarily due to a lesion of the somatosensory nervous system. The burden of diabetic neuropathy is related not only to the complexity of diabetes but also to the poor outcomes and difficult treatment options. There is no specific treatment for diabetic neuropathy other than glycemic control and diligent foot care. Although various metabolic pathways are impaired in diabetic neuropathy, enhanced cellular oxidative stress is proposed as a common initiator. A mechanism-based treatment of diabetic neuropathy is challenging; a better understanding of the pathophysiology of diabetic neuropathy will help to develop strategies for the new and correct diagnostic procedures and personalized interventions. Thus, we review the current knowledge of the pathophysiology in diabetic neuropathy. We focus on discussing how the defects in metabolic and vascular pathways converge to enhance oxidative stress and how they produce the onset and progression of nerve injury present in diabetic neuropathy. We discuss if the mechanisms underlying neuropathy are similarly operated in type I and type II diabetes and the progression of antioxidants in treating diabetic neuropathy.

Protein–Protein Interaction Methods and Protein Phase Separation

In the last decade, newly developed experimental methods have made it possible to highlight that macromolecules in the cell milieu physically interact to support physiology. This has shifted the problem of protein–protein interaction from a microscopic, electron-density scale to a mesoscopic one. Further, nowadays there is increasing evidence that proteins in the nucleus and in the cytoplasm can aggregate in membraneless organelles for different physiological reasons. In this scenario, it is urgent to face the problem of biomolecule functional annotation with efficient computational methods, suited to extract knowledge from reliable data and transfer information across different domains of investigation. Here, we revise the present state of the art of our knowledge of protein–protein interaction and the computational methods that differently implement it. Furthermore, we explore experimental and computational features of a set of proteins involved in phase separation.

ConvsPPIS: Identifying Protein-protein Interaction Sites by an Ensemble Convolutional Neural Network with Feature Graph

Background/Objective:

Protein-protein interactions are essentials for most cellular processes and thus, unveiling how proteins interact with is a crucial question that can be better understood by recognizing which residues participate in the interaction. Although many computational approaches have been proposed to predict interface residues, their feature perspective and model learning ability are not enough to achieve ideal results. So, our objective is to improve the predictive performance under considering feature perspective and new learning algorithm.

Method:

In this study, we proposed an ensemble deep convolutional neural network, which explores the context and positional context of consecutive residues within a protein sub-sequence. Specifically, unlike the feature view of previous methods, ConvsPPIS uses evolutionary, physicochemical, and structural protein characteristics to construct their own feature graph respectively. After that, three independent deep convolutional neural networks are trained on each type of feature graph for learning the underlying pattern in sub-sequence. Lastly, we integrated those three deep networks into an ensemble predictor with leveraging complementary information of those features to predict potential interface residues.

Results:

Some comparative experiments have conducted through 10-fold cross-validation. The results indicated that ConvsPPIS achieved superior performance on DBv5-Sel dataset with an accuracy of 88%. Additional experiments on CAPRI-Alone dataset demonstrated ConvsPPIS has also better prediction performance.

Conclusion:

The ConvsPPIS method provided a new perspective to capture protein feature expression for identifying protein-protein interaction sites. The results proved the superiority of this method.

Protein Glycation in Plants-An Under-Researched Field with Much Still to Discover

Recent research has identified glycation as a non-enzymatic post-translational modification of proteins in plants with a potential contributory role to the functional impairment of the plant proteome. Reducing sugars with a free aldehyde or ketone group such as glucose, fructose and galactose react with the N-terminal and lysine side chain amino groups of proteins. A common early-stage glycation adduct formed from glucose is N_ε-fructosyl-lysine (FL). Saccharide-derived reactive dicarbonyls are arginine residue-directed glycating agents, forming advanced glycation endproducts (AGEs). A dominant dicarbonyl is methylglyoxal—formed mainly by the trace-level degradation of triosephosphates, including through the Calvin cycle of photosynthesis. Methylglyoxal forms the major quantitative AGE, hydroimidazolone MG-H1. Glucose and methylglyoxal concentrations in plants change with the developmental stage, senescence, light and dark cycles and also likely biotic and abiotic stresses. Proteomics analysis indicates that there is an enrichment of the amino acid residue targets of glycation, arginine and lysine residues, in predicted functional sites of the plant proteome, suggesting the susceptibility of proteins to functional inactivation by glycation. In this review, we give a brief introduction to glycation, glycating agents and glycation adducts in plants. We consider dicarbonyl stress, the functional vulnerability of the plant proteome to arginine-directed glycation and the likely role of methylglyoxal-mediated glycation in the activation of the unfolded protein response in plants. The latter is linked to the recent suggestion of protein glycation in sugar signaling in plant metabolism. The overexpression of glyoxalase 1, which suppresses glycation by methylglyoxal and glyoxal, produced plants resistant to high salinity, drought, extreme temperature and other stresses. Further research to decrease protein glycation in plants may lead to improved plant growth and assist the breeding of plant varieties resistant to environmental stress and senescence—including plants of commercial ornamental and crop cultivation value.

Bio-semantic relation extraction with attention-based external knowledge reinforcement

Background

Semantic resources such as knowledge bases contains high-quality-structured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge.

Results

The paper proposes a novel method that uses a deep neural network model adopting the prior knowledge to improve performance in the automated extraction of biological semantic relations from the scientific literature. The model is based on a recurrent neural network combining the attention mechanism with the semantic resources, i.e., UniProt and BioModels. Our method is evaluated on the BioNLP and BioCreative corpus, a set of manually annotated biological text. The experiments demonstrate that the method outperforms the current state-of-the-art models, and the structured semantic information could improve the result of bio-text-mining.

Conclusion

The experiment results show that our approach can effectively make use of the external prior knowledge information and improve the performance in the protein-protein interaction extraction task. The method should be able to be generalized for other types of data, although it is validated on biomedical texts.

Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets

Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

Methylglyoxal, a Highly Reactive Dicarbonyl Compound, in Diabetes, Its Vascular Complications, and Other Age-Related Diseases

The formation and accumulation of methylglyoxal (MGO), a highly reactive dicarbonyl compound, has been implicated in the pathogenesis of type 2 diabetes, vascular complications of diabetes, and several other age-related chronic inflammatory diseases such as cardiovascular disease, cancer, and disorders of the central nervous system. MGO is mainly formed as a byproduct of glycolysis and, under physiological circumstances, detoxified by the glyoxalase system. MGO is the major precursor of nonenzymatic glycation of proteins and DNA, subsequently leading to the formation of advanced glycation end products (AGEs). MGO and MGO-derived AGEs can impact on organs and tissues affecting their functions and structure. In this review we summarize the formation of MGO, the detoxification of MGO by the glyoxalase system, and the biochemical pathways through which MGO is linked to the development of diabetes, vascular complications of diabetes, and other age-related diseases. Although interventions to treat MGO-associated complications are not yet available in the clinical setting, several strategies to lower MGO have been developed over the years. We will summarize several new directions to target MGO stress including glyoxalase inducers and MGO scavengers. Targeting MGO burden may provide new therapeutic applications to mitigate diseases in which MGO plays a crucial role.

Methylglyoxal modification reduces the sensitivity of hen egg white lysozyme to stress-induced aggregation: Insight into the anti-amyloidogenic property of α-dicarbonyl compound

The reactive α-oxoaldehyde, methylglyoxal reacts with different proteins to form Advanced Glycation End Products (AGEs) through Maillard reaction. Its level increases significantly in diabetic condition. Here, we have investigated the effect of different concentrations of methylglyoxal (200-400 µM) on the monomeric protein, hen egg white lysozyme (HEWL) following incubation for 3 weeks. Reaction of methylglyoxal with HEWL induced considerable changes in tertiary structure of the protein, but no significant alteration in secondary structure, as evident from different spectroscopic and biophysical studies. Interestingly, methylglyoxal modification was found to enhance the thermal stability of the protein and reduce its sensitivity to stress-induced aggregation. Finally, peptide mass fingerprinting revealed modification of arginine (Arg-45, Arg-14, Arg-68 or Arg-72) and lysine (Lys-116) residues of the protein to AGE adducts, namely, hydroimidazolone, tetrahydropyrimidine, and carboxyethyllysine. Methylglyoxal-derived AGE adducts (MAGE) appear to be responsible for the observed changes in protein. As demonstrated in the present study, the findings may highlight a possible therapeutic potential of the α-oxoaldehyde against protein misfolding and conformational disorder.Communicated by Ramaswamy H. Sarma.

Dicarbonyl Stress at the Crossroads of Healthy and Unhealthy Aging

Dicarbonyl stress occurs when dicarbonyl metabolites (i.e., methylglyoxal, glyoxal and 3-deoxyglucosone) accumulate as a consequence of their increased production and/or decreased detoxification. This toxic condition has been associated with metabolic and age-related diseases, both of which are characterized by a pro-inflammatory and pro-oxidant state. Methylglyoxal (MGO) is the most reactive dicarbonyl and the one with the highest endogenous flux. It is the precursor of the major quantitative advanced glycated products (AGEs) in physiological systems, arginine-derived hydroimidazolones, which accumulate in aging and dysfunctional tissues. The aging process is characterized by a decline in the functional properties of cells, tissues and whole organs, starting from the perturbation of crucial cellular processes, including mitochondrial function, proteostasis and stress-scavenging systems. Increasing studies are corroborating the causal relationship between MGO-derived AGEs and age-related tissue dysfunction, unveiling a previously underestimated role of dicarbonyl stress in determining healthy or unhealthy aging. This review summarizes the latest evidence supporting a causal role of dicarbonyl stress in age-related diseases, including diabetes mellitus, cardiovascular disease and neurodegeneration.

Activation of the unfolded protein response in high glucose treated endothelial cells is mediated by methylglyoxal

Metabolic dysfunction of endothelial cells in hyperglycemia contributes to the development of vascular complications of diabetes where increased reactive glycating agent, methylglyoxal (MG), is involved. We assessed if increased MG glycation induced proteotoxic stress, identifying related metabolic drivers and protein targets. Human aortal endothelial cells (HAECs) were incubated in high glucose concentration (20 mM versus 5 mM control) in vitro for 3–6 days. Flux of glucose metabolism, MG formation and glycation and changes in cytosolic protein abundances, MG modification and proteotoxic responses were assessed. Similar studies were performed with human microvascular endothelial HMEC-1 cells where similar outcomes were observed. HAECs exposed to high glucose concentration showed increased cellular concentration of MG (2.27 ± 0.21 versus 1.28 ± 0.03 pmol/10⁶ cells, P < 0.01) and formation of MG-modified proteins (24.0 ± 3.7 versus 14.1 ± 3.2 pmol/10⁶ cells/day; P < 0.001). In proteomics analysis, high glucose concentration increased proteins of the heat shock response – indicating activation of the unfolded protein response (UPR) with downstream inflammatory and pro-thrombotic responses. Proteins susceptible to MG modification were enriched in protein folding, protein synthesis, serine/threonine kinase signalling, glycolysis and gluconeogenesis. MG was increased in high glucose by increased flux of MG formation linked to increased glucose metabolism mediated by proteolytic stabilisation and increase of hexokinase-2 (HK-2); later potentiated by proteolytic down regulation of glyoxalase 1 (Glo1) - the major enzyme of MG metabolism. Silencing of Glo1, selectively increasing MG, activated the UPR similarly. Silencing of HK-2 prevented increased glucose metabolism and MG formation. trans-Resveratrol and hesperetin combination (tRES-HESP) corrected increased MG and glucose metabolism by increasing expression of Glo1 and decreasing expression of HK-2. Increased MG glycation activates the UPR in endothelial cells and thereby may contribute to endothelial cell dysfunction in diabetic vascular disease where tRES-HESP may provide effective therapy.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Dicarbonyl Stress and Glyoxalase-1 in Skeletal Muscle: Implications for Insulin Resistance and Type 2 Diabetes

Glyoxalase-1 (GLO1) is a ubiquitously expressed cytosolic protein which plays a role in the natural maintenance of cellular health and is abundantly expressed in human skeletal muscle. A consequence of reduced GLO1 protein expression is cellular dicarbonyl stress, which is elevated in obesity, insulin resistance and type 2 diabetes (T2DM). Both in vitro and pre-clinical models suggest dicarbonyl stress per se induces insulin resistance and is prevented by GLO1 overexpression, implicating a potential role for GLO1 therapy in insulin resistance and type 2 diabetes (T2DM). Recent work has identified the therapeutic potential of novel natural agents as a GLO1 inducer, which resulted in improved whole-body metabolism in obese adults. Given skeletal muscle is a major contributor to whole-body glucose, lipid, and protein metabolism, such GLO1 inducers may act, in part, through mechanisms in skeletal muscle. Currently, investigations examining the specificity of dicarbonyl stress and GLO1 biology in human skeletal muscle are lacking. Recent work from our lab indicates that dysregulation of GLO1 in skeletal muscle may underlie human insulin resistance and that exercise training may impart therapeutic benefits. This minireview will summarize the existing human literature examining skeletal muscle GLO1 and highlight the emerging therapeutic concepts for GLO1 gain-of-function in conditions such as insulin resistance and cardiometabolic disease.

Methylglyoxal-induced dicarbonyl stress in aging and disease: first steps towards glyoxalase 1-based treatments

Dicarbonyl stress is the abnormal accumulation of dicarbonyl metabolites leading to increased protein and DNA modification contributing to cell and tissue dysfunction in aging and disease. It is produced by increased formation and/or decreased metabolism of dicarbonyl metabolites. MG (methylglyoxal) is a dicarbonyl metabolite of relatively high flux of formation and precursor of the most quantitatively and functionally important spontaneous modifications of protein and DNA clinically. Major MG-derived adducts are arginine-derived hydroimidazolones of protein and deoxyguanosine-derived imidazopurinones of DNA. These are formed non-oxidatively. The glyoxalase system provides an efficient and essential basal and stress-response-inducible enzymatic defence against dicarbonyl stress by the reduced glutathione-dependent metabolism of methylglyoxal by glyoxalase 1. The GLO1 gene encoding glyoxalase 1 has low prevalence duplication and high prevalence amplification in some tumours. Dicarbonyl stress contributes to aging, disease and activity of cytotoxic chemotherapeutic agents. It is found at a low, moderate and severe level in obesity, diabetes and renal failure respectively, where it contributes to the development of metabolic and vascular complications. Increased glyoxalase 1 expression confers multidrug resistance to cancer chemotherapy and has relatively high prevalence in liver, lung and breast cancers. Studies of dicarbonyl stress are providing improved understanding of aging and disease and the basis for rational design of novel pharmaceuticals: glyoxalase 1 inducers for obesity, diabetes and cardiovascular disease and glyoxalase 1 inhibitors for multidrug-resistant tumours. The first clinical trial of a glyoxalase 1 inducer in overweight and obese subjects showed improved glycaemic control, insulin resistance and vascular function.

Integrating computational methods and experimental data for understanding the recognition mechanism and binding affinity of protein-protein complexes

Protein-protein interactions perform several functions inside the cell. Understanding the recognition mechanism and binding affinity of protein-protein complexes is a challenging problem in experimental and computational biology. In this review, we focus on two aspects (i) understanding the recognition mechanism and (ii) predicting the binding affinity. The first part deals with computational techniques for identifying the binding site residues and the contribution of important interactions for understanding the recognition mechanism of protein-protein complexes in comparison with experimental observations. The second part is devoted to the methods developed for discriminating high and low affinity complexes, and predicting the binding affinity of protein-protein complexes using three-dimensional structural information and just from the amino acid sequence. The overall view enhances our understanding of the integration of experimental data and computational methods, recognition mechanism of protein-protein complexes and the binding affinity.

Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes

Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis.

Mass spectrometric determination of early and advanced glycation in biology

Protein glycation in biological systems occurs predominantly on lysine, arginine and N-terminal residues of proteins. Major quantitative glycation adducts are found at mean extents of modification of 1-5 mol percent of proteins. These are glucose-derived fructosamine on lysine and N-terminal residues of proteins, methylglyoxal-derived hydroimidazolone on arginine residues and N(ε)-carboxymethyl-lysine residues mainly formed by the oxidative degradation of fructosamine. Total glycation adducts of different types are quantified by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring mode. Metabolism of glycated proteins is followed by LC-MS/MS of glycation free adducts as minor components of the amino acid metabolome. Glycated proteins and sites of modification within them - amino acid residues modified by the glycating agent moiety - are identified and quantified by label-free and stable isotope labelling with amino acids in cell culture (SILAC) high resolution mass spectrometry. Sites of glycation by glucose and methylglyoxal in selected proteins are listed. Key issues in applying proteomics techniques to analysis of glycated proteins are: (i) avoiding compromise of analysis by formation, loss and relocation of glycation adducts in pre-analytic processing; (ii) specificity of immunoaffinity enrichment procedures, (iii) maximizing protein sequence coverage in mass spectrometric analysis for detection of glycation sites, and (iv) development of bioinformatics tools for prediction of protein glycation sites. Protein glycation studies have important applications in biology, ageing and translational medicine - particularly on studies of obesity, diabetes, cardiovascular disease, renal failure, neurological disorders and cancer. Mass spectrometric analysis of glycated proteins has yet to find widespread use clinically. Future use in health screening, disease diagnosis and therapeutic monitoring, and drug and functional food development is expected. A protocol for high resolution mass spectrometry proteomics of glycated proteins is given.

ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules

The degree of evolutionary conservation of an amino acid in a protein or a nucleic acid in DNA/RNA reflects a balance between its natural tendency to mutate and the overall need to retain the structural integrity and function of the macromolecule. The ConSurf web server (http://consurf.tau.ac.il), established over 15 years ago, analyses the evolutionary pattern of the amino/nucleic acids of the macromolecule to reveal regions that are important for structure and/or function. Starting from a query sequence or structure, the server automatically collects homologues, infers their multiple sequence alignment and reconstructs a phylogenetic tree that reflects their evolutionary relations. These data are then used, within a probabilistic framework, to estimate the evolutionary rates of each sequence position. Here we introduce several new features into ConSurf, including automatic selection of the best evolutionary model used to infer the rates, the ability to homology-model query proteins, prediction of the secondary structure of query RNA molecules from sequence, the ability to view the biological assembly of a query (in addition to the single chain), mapping of the conservation grades onto 2D RNA models and an advanced view of the phylogenetic tree that enables interactively rerunning ConSurf with the taxa of a sub-tree.

Progress and challenges in predicting protein interfaces

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Computational Analysis of the Binding Specificities of PH Domains

Pleckstrin homology (PH) domains share low sequence identities but extremely conserved structures. They have been found in many proteins for cellular signal-dependent membrane targeting by binding inositol phosphates to perform different physiological functions. In order to understand the sequence-structure relationship and binding specificities of PH domains, quantum mechanical (QM) calculations and sequence-based combined with structure-based binding analysis were employed in our research. In the structural aspect, the binding specificities were shown to correlate with the hydropathy characteristics of PH domains and electrostatic properties of the bound inositol phosphates. By comparing these structure properties with sequence-based profiles of physicochemical properties, PH domains can be classified into four functional subgroups according to their binding specificities and affinities to inositol phosphates. The method not only provides a simple and practical paradigm to predict binding specificities for functional genomic research but also gives new insight into the understanding of the basis of diseases with respect to PH domain structures.

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein-protein binding sites (PPBSs) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique. Formed by many individual "Random Forest" classifiers, the operation engine to run prediction is a two-layer ensemble classifier, with the 1st-layer voting out the best training data-set from many bootstrap systems and the 2nd-layer voting out the most relevant one from seven physicochemical properties. Cross-validation tests indicate that the new predictor is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach, quite consistent with the facts that many important biological functions of proteins can be elucidated with their low-frequency internal motions. The web server of iPPBS-PseAAC is accessible at http://www.jci-bioinfo.cn/iPPBS-PseAAC , by which users can easily acquire their desired results without the need to follow the complicated mathematical equations involved.

PETs: A Stable and Accurate Predictor of Protein-Protein Interacting Sites Based on Extremely-Randomized Trees

Protein-protein interaction (PPI) plays crucial roles in the performance of various biological processes. A variety of methods are dedicated to identify whether proteins have interaction residues, but it is often more crucial to recognize each amino acid. In practical applications, the stability of a prediction model is as important as its accuracy. However, random sampling, which is widely used in previous prediction models, often brings large difference between each training model. In this paper, a Predictor of protein-protein interaction sites based on Extremely-randomized Trees (PETs) is proposed to improve the prediction accuracy while maintaining the prediction stability. In PETs, a cluster-based sampling strategy is proposed to ensure the model stability: first, the training dataset is divided into subsets using specific features; second, the subsets are clustered using K-means; and finally the samples are selected from each cluster. Using the proposed sampling strategy, samples which have different types of significant features could be selected independently from different clusters. The evaluation shows that PETs is able to achieve better accuracy while maintaining a good stability. The source code and toolkit are available at https://github.com/BinXia/PETs.

Sequence specificity between interacting and non-interacting homologs identifies interface residues--a homodimer and monomer use case

Background

Protein families participating in protein-protein interactions may contain sub-families that have different binding characteristics, ranging from right binding to showing no interaction at all. Composition differences at the sequence level in these sub-families are often decisive to their differential functional interaction. Methods to predict interface sites from protein sequences typically exploit conservation as a signal. Here, instead, we provide proof of concept that the sequence specificity between interacting versus non-interacting groups can be exploited to recognise interaction sites.

Results

We collected homodimeric and monomeric proteins and formed homologous groups, each having an interacting (homodimer) subgroup and a non-interacting (monomer) subgroup. We then compiled multiple sequence alignments of the proteins in the homologous groups and identified compositional differences between the homodimeric and monomeric subgroups for each of the alignment positions. Our results show that this specificity signal distinguishes interface and other surface residues with 40.9 % recall and up to 25.1 % precision.

Conclusions

To our best knowledge, this is the first large scale study that exploits sequence specificity between interacting and non-interacting homologs to predict interaction sites from sequence information only. The performance obtained indicates that this signal contains valuable information to identify protein-protein interaction sites.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0758-y) contains supplementary material, which is available to authorized users.

Site specific modification of the human plasma proteome by methylglyoxal

Increasing evidence identifies dicarbonyl stress from reactive glucose metabolites, such as methylglyoxal (MG), as a major pathogenic link between hyperglycemia and complications of diabetes. MG covalently modifies arginine residues, yet the site specificity of this modification has not been thoroughly investigated. Sites of MG adduction in the plasma proteome were identified using LC-MS/MS analysis in vitro following incubation of plasma proteins with MG. Treatment of plasma proteins with MG yielded 14 putative MG hotspots from five plasma proteins (albumin [nine hotspots], serotransferrin, haptoglobin [2 hotspots], hemopexin, and Ig lambda-2 chain C regions). The search results revealed two versions of MG-arginine modification, dihydroxyimidazolidine (R+72) and hydroimidazolone (R+54) adducts. One of the sites identified was R257 in human serum albumin, which is a critical residue located in drug binding site I. This site was validated as a target for MG modification by a fluorescent probe displacement assay, which revealed significant drug dissociation at 300 μM MG from a prodan-HSA complex (75 μM). Moreover, twelve human plasma samples (six male, six female, with two type 2 diabetic subjects from both genders) were analyzed using multiple reaction monitoring (MRM) tandem mass spectrometry and revealed the presence of the MG-modified albumin R257 peptide. These data provide insights into the nature of the site-specificity of MG modification of arginine, which may be useful for therapeutic treatments that aim to prevent MG-mediated adverse responses in patients.

Dicarbonyl proteome and genome damage in metabolic and vascular disease

Methylglyoxal is a potent protein-glycating agent. It is an arginine-directed glycating agent and often modifies functionally important sites in proteins. Glycation forms mainly MG-H1 [Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)ornithine] residues. MG-H1 content of proteins is quantified by stable isotopic dilution analysis-MS/MS and also by immunoblotting with specific monoclonal antibodies. Methylglyoxal-modified proteins undergo cellular proteolysis and release MG-H1 free adduct for excretion. MG-H1 residues have been found in proteins of animals, plants, bacteria, fungi and protoctista. MG-H1 is often the major advanced glycation end-product in proteins of tissues and body fluids, increasing in diabetes and associated vascular complications, renal failure, cirrhosis, Alzheimer's disease, arthritis, Parkinson's disease and aging. Proteins susceptible to methylglyoxal modification with related functional impairment are called the DCP (dicarbonyl proteome). The DCP includes albumin, haemoglobin, transcription factors, mitochondrial proteins, extracellular matrix proteins, lens crystallins and others. DCP component proteins are linked to mitochondrial dysfunction in diabetes and aging, oxidative stress, dyslipidaemia, cell detachment and anoikis and apoptosis. Methylglyoxal also modifies DNA where deoxyguanosine residues are modified to imidazopurinone MGdG {3-(2'-deoxyribosyl)-6,7-dihydro-6,7-dihydroxy-6/7-methylimidazo-[2,3-b]purine-9(8)one} isomers. MGdG was the major quantitative adduct detected in vivo. It was linked to frequency of DNA strand breaks and increased markedly during apoptosis induced by a cell-permeant glyoxalase I inhibitor. Glyoxalase I metabolizes >99% methylglyoxal and thereby protects the proteome and genome. Gene deletion of GLO1 is embryonically lethal and GLO1 silencing increases methylglyoxal concentration, MG-H1 and MGdG, premature aging and disease. Studies of methylglyoxal glycation have importance for human health, longevity and treatment of disease.

The glyoxalase pathway: the first hundred years... and beyond

The discovery of the enzymatic formation of lactic acid from methylglyoxal dates back to 1913 and was believed to be associated with one enzyme termed ketonaldehydemutase or glyoxalase, the latter designation prevailed. However, in 1951 it was shown that two enzymes were needed and that glutathione was the required catalytic co-factor. The concept of a metabolic pathway defined by two enzymes emerged at this time. Its association to detoxification and anti-glycation defence are its presently accepted roles, since methylglyoxal exerts irreversible effects on protein structure and function, associated with misfolding. This functional defence role has been the rationale behind the possible use of the glyoxalase pathway as a therapeutic target, since its inhibition might lead to an increased methylglyoxal concentration and cellular damage. However, metabolic pathway analysis showed that glyoxalase effects on methylglyoxal concentration are likely to be negligible and several organisms, from mammals to yeast and protozoan parasites, show no phenotype in the absence of one or both glyoxalase enzymes. The aim of the present review is to show the evolution of thought regarding the glyoxalase pathway since its discovery 100 years ago, the current knowledge on the glyoxalase enzymes and their recognized role in the control of glycation processes.