Applicability of Chromatographic Co‐Elution for Antibiotic Target Identification

Identification of the molecular target is a crucial step in evaluating novel antibiotics. To support target identification, a label‐free method based on chromatographic co‐elution has previously been developed. Target identification by chromatographic coelution (TICC) exploits the alteration of the elution profile of target‐bound drug versus free drug in ion exchange (IEX) chromatography to identify potential target proteins from elution fractions. The applicability of TICC for antibiotic research is investigated by evaluating which proteins, that is, putative targets, can be monitored in Bacillus subtilis. Coelution of components of known protein complexes provides a read‐out for how well the native state of proteins is conserved during chromatography. Rifampicin, which targets RNA polymerase, is used in a proof‐of‐concept study.     

Protein Array Patterning by Diffusive Gel Stamping

Proteins and small molecules are the effectors of physiological action in biological systems and comprehensive methods are needed to analyze their modifications, expression levels and interactions. Systems-scale characterization of the proteome requires thousands of components in high-complexity samples to be isolated and simultaneously probed. While protein microarrays offer a promising approach to probe systems-scale changes in a high-throughput format, they are limited by the need to individually synthesize tens of thousands of proteins. We present an alternative technique, which we call diffusive gel (DiG) stamping, for patterning a microarray using a cellular lysate enabling rapid visualization of dynamic changes in the proteome as well protein interactions. A major advantage of the method described is that it requires no specialized equipment or in-vitro protein synthesis, making it widely accessible to researchers. The method can be integrated with mass spectrometry, allowing for the discovery of novel protein interactions. Here, we describe and characterize the sensitivity and physical features of DiG-Stamping. We demonstrate the biologic utility of DiG-Stamping by (1) identifying the binding partners of a target protein within a cellular lysate and by (2) visualizing the dynamics of proteins with multiple post-translational modifications.     

Semantically assessing the reliability of protein interactions

Protein–protein interactions (PPIs) play very important roles in many cellular processes, and provide rich information for discovering biological facts and knowledge. Although various experimental approaches have been developed to generate large amounts of PPI data for different organisms, high-throughput experimental data usually suffers from high error rates, and as a consequence, the biological knowledge discovered from this data is distorted or incorrect. Therefore, it is vital to assess the quality of protein interaction data and extract reliable protein interactions from the high-throughput experimental data. In this paper, we propose a new Semantic Reliability (SR) method to assess the reliability of each protein interaction and identify potential false-positive protein interactions in a dataset. For each pair of target interacting proteins, the SR method takes into account the semantic influence between proteins that interact with the target proteins, and the semantic influence between the target proteins themselves when assessing the interaction reliability. Evaluations on real protein interaction datasets demonstrated that our method outperformed other existing methods in terms of extracting more reliable interactions from original protein interaction datasets.     

Determining protein–protein functional associations by functional rules based on gene ontology and KEGG pathway

Protein–protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.     

How do biomolecular systems speed up and regulate rates?

The viability of a biological system depends upon careful regulation of the rates of various processes. These rates have limits imposed by intrinsic chemical or physical steps (e.g., diffusion). These limits can be expanded by interactions and dynamics of the biomolecules. For example, (a) a chemical reaction is catalyzed when its transition state is preferentially bound to an enzyme; (b) the folding of a protein molecule is speeded up by specific interactions within the transition-state ensemble and may be assisted by molecular chaperones; (c) the rate of specific binding of a protein molecule to a cellular target can be enhanced by mechanisms such as long-range electrostatic interactions, nonspecific binding and folding upon binding; (d) directional movement of motor proteins is generated by capturing favorable Brownian motion through intermolecular binding energy; and (e) conduction and selectivity of ions through membrane channels are controlled by interactions and the dynamics of channel proteins. Simple physical models are presented here to illustrate these processes and provide a unifying framework for understanding speed attainment and regulation in biomolecular systems.     

Rapid characterization of cellular retinoid-binding proteins by high-performance size-exclusion chromatography

A high-performance size-exclusion chromatographic method was developed for the analysis of cellular retinol and retinoic acid-binding proteins. The chromatographic analysis of the retinol-retinol-binding protein complex or the retinoic acid-retinoic acid-binding protein complex requires 15 min. The use of high-specific-radioactivity retinoid ligand (30-40 Ci/mmol) allows routine detection of 25 fmol of retinoid-binding protein/mg of cytosolic protein. Thus, this method provides a rapid alternative to sucrose density gradient sedimentation analysis of the cellular retinoid-binding proteins. High-performance size-exclusion chromatography is well suited to screening novel tissues, tumors, and cell lines for the presence of retinoid-binding proteins and to the further characterization of these cellular proteins. This method was applied to the characterization of cellular retinol- and retinoic acid-binding proteins in fetal rabbit tissues. Both retinol- and retinoic acid-binding proteins were detected in fetal rabbit brain, intestine, kidney, and lung at a gestational age of 28 days. Neither retinoid-binding protein was detected in 28-day-old fetal rabbit placenta. Specific retinoic acid binding in fetal intestinal cytosol decreased as a function of increasing gestational age.     

Study of the mechanism of interaction of antibody (IgG) on two mixed mode sorbents

Purification of target proteins from a crude biological mixture containing proteins, peptides and other biomolecules is the chromatographic challenge. Mixed mode chromatography offers additional selectivities to improve the overall productivity of commercial bioprocesses with novel chromatographic sorbents being introduced to overcome the problem. HEA HyperCel? (n-hexyl amine) and PPA HyperCel? (phenyl propyl amine) are industry scalable mixed mode chromatography sorbents where both hydrophobic and electrostatic interactions are predominant. Our study focuses on understanding the underlying mechanism of interaction of protein with the sorbent. Parameters like buffer conditions, pH and temperature were tuned to study the adsorption and desorption conditions of the protein. Dynamic binding capacity of HEA HyperCel? and PPA HyperCel? sorbents was studied with human IgG as a model protein. Our study shows that, in HEA the interaction of IgG to the sorbent is predominantly hydrophobic as the binding is enhanced (50–60 mg/ml of sorbent) by presence of salt in buffer and increase in temperature. Binding capacity of PPA is 50–60 mg/ml of sorbent irrespective of temperature effect and/or the presence of salt. The chromatographic experiments show that the interaction could be hydrophobic or ionic or some charge transfer mechanism depending upon the buffer conditions.     

植物中验证蛋白相互作用的Pull-down和Co-IP技术

蛋白互作在细胞生命活动中发挥关键作用,在不同时空层面上参与多种细胞学过程,因此研究蛋白互作对理解分子调控网络至关重要。通常情况下,利用酵母双杂交系统筛选植物蛋白互作必须通过体外和体内系统进行验证。Pull-down和Co-IP是验证植物蛋白互作的常用技术。Pull-down被广泛用于体外验证蛋白间的直接互作;而在植物活体内,利用本氏烟草(Nicotiana benthamiana)叶片瞬时表达蛋白,继而通过Co-IP进行鉴定是目前验证蛋白互作最简单且最有效的方法之一。该文对GST Pull-down和烟草瞬时表达系统中Co-IP技术原理及实验方案进行详细描述,以期为验证植物蛋白互作提供参考。     

SET7/9-dependent methylation of ARTD1 at K508 stimulates poly-ADP-ribose formation after oxidative stress

ADP-ribosyltransferase diphtheria toxin-like 1 (ARTD1, formerly PARP1) is localized in the nucleus, where it ADP-ribosylates specific target proteins. The post-translational modification (PTM) with a single ADP-ribose unit or with polymeric ADP-ribose (PAR) chains regulates protein function as well as protein–protein interactions and is implicated in many biological processes and diseases. SET7/9 (Setd7, KMT7) is a protein methyltransferase that catalyses lysine monomethylation of histones, but also methylates many non-histone target proteins such as p53 or DNMT1. Here, we identify ARTD1 as a new SET7/9 target protein that is methylated at K508 in vitro and in vivo. ARTD1 auto-modification inhibits its methylation by SET7/9, while auto-poly-ADP-ribosylation is not impaired by prior methylation of ARTD1. Moreover, ARTD1 methylation by SET7/9 enhances the synthesis of PAR upon oxidative stress in vivo. Furthermore, laser irradiation-induced PAR formation and ARTD1 recruitment to sites of DNA damage in a SET7/9-dependent manner. Together, these results reveal a novel mechanism for the regulation of cellular ARTD1 activity by SET7/9 to assure efficient PAR formation upon cellular stress.     

Unveiling protein functions through the dynamics of the interaction network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.     

Predicting drug targets based on protein domains

The identification of interactions between drugs and proteins plays key roles in understanding mechanisms underlying drug actions and can lead to new drug design strategies. Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains. The known target proteins of those drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to identification of potential drug-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed PDTD method.     

Identification of protein-protein interactions and topologies in living cells with chemical cross-linking and mass spectrometry

We present results from a novel strategy that enables concurrent identification of protein-protein interactions and topologies in living cells without specific antibodies or genetic manipulations for immuno-/affinity purifications. The strategy consists of (i) a chemical cross-linking reaction: intact cell labeling with a novel class of chemical cross-linkers, protein interaction reporters (PIRs); (ii) two-stage mass spectrometric analysis: stage 1 identification of PIR-labeled proteins and construction of a restricted database by two-dimensional LC/MSMS and stage 2 analysis of PIR-labeled peptides by multiplexed LC/FTICR-MS; and (iii) data analysis: identification of cross-linked peptides and proteins of origin using accurate mass and other constraints. The primary advantage of the PIR approach and distinction from current technology is that protein interactions together with topologies are detected in native biological systems by stabilizing protein complexes with new covalent bonds while the proteins are present in the original cellular environment. Thus, weak or transient interactions or interactions that require properly folded, localized, or membrane-bound proteins can be labeled and identified through the PIR approach. This strategy was applied to Shewanella oneidensis bacterial cells, and initial studies resulted in identification of a set of protein-protein interactions and their contact/binding regions. Furthermore most identified interactions involved membrane proteins, suggesting that the PIR approach is particularly suited for studies of membrane protein-protein interactions, an area under-represented with current widely used approaches.     

Deciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein–protein, protein–RNA, and protein–DNA interactions regulate and execute most biological functions. In particular in brain, protein–protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell–cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte–neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.     

Metal nanoclusters: Protein corona formation and implications for biological applications

Metal nanoclusters (NCs) are a new type of nanoprobe with great potential in various biological applications. For biocompatible and efficient utilization of NCs, a thorough understanding of their interactions with biological systems is highly important. Herein, we focus on recent studies addressing interactions between metal NCs and proteins as well as implications for their further biological application. These findings show that protein adsorption not only affects the photophysical properties of NCs, but also influences their subsequent biological behavior, i.e., cellular uptake and cytotoxicity. Moreover, specific protein–NC interactions have also been harnessed to develop novel protein discrimination strategies.     

Protein separation by potential barrier chromatography

Potential barrier chromatography (PBC) is a liquid chromatographic method in which the adsorption and desorption of proteins are controlled by modifications of repulsive double-layer and attractive van der Waals interactions between the proteins and the adsorbent through changes in mobile phase composition. In this review PBC is compared and contrasted to the more traditional chromatographic methods, namely, ion exchange, gel permeation, hydrophobic interaction, and affinity chromatography. The physical forces that underlie PBC (as well as the other methods) are discussed in terms of their effects on chromatographic behavior. Experimental results are presented to illustrate the use and simplicity of the method.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Gene ontology improves template selection in comparative protein docking

Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains—biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.     

Evaluation of two novel tag-based labelling technologies for site-specific modification of proteins

Modern drug discovery strongly depends on the availability of target proteins in sufficient amounts and with desired properties. For some applications, proteins have to be produced with specific modifications such as tags for protein purification, fluorescent or radiometric labels for detection, glycosylation and phosphorylation for biological activity, and many more. It is well known that covalent modifications can have adverse effects on the biological activity of some target proteins. It is therefore one of the major challenges in protein chemistry to generate covalent modifications without affecting the biological activity of the target protein. Current procedures for modification mostly rely on non-specific labelling of lysine or cysteine residues on the protein of interest, but alternative approaches dedicated to site-specific protein modification are being developed and might replace most of the commonly used methodologies. In this study, we investigated two novel methods where target proteins can be expressed in E. coli with a fusion partner that allows protein modification in a covalent and highly selective manner. Firstly, we explored a method based on the human DNA repair protein O6-alkylguanine-DNA alkyltransferase (hAGT) as a fusion tag for site-directed attachment of small molecules. The AGT-tag (SNAP-tag) can accept almost any chemical moiety when it is attached to the guanine base through a benzyl group. In our experiments we were able to label a target protein fused to the AGT-tag with various fluorophores coupled to O6-benzylguanine. Secondly, we tested in vivo and in vitro site-directed biotinylation with two different tags, consisting of either 15 (AviTag) or 72 amino acids (BioEase tag), which serve as a substrate for bacterial biotin ligase birA. When birA protein was co-expressed in E. coli biotin was incorporated almost completely into a model protein which carried these recognition tags at its C-terminus. The same findings were also obtained with in vitro biotinylation assays using pure birA independently over-expressed in E. coli and added to the biotinylation reaction in the test tube. For both biotinylation methods, peptide mapping and LC-MS proved the highly site-specific modification of the corresponding tags. Our results indicate that these novel site-specific labelling reactions work in a highly efficient manner, allow almost quantitative labelling of the target proteins, have no deleterious effect on the biological activity and are easy to perform in standard laboratories.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.