An evaluation of in vitro protein-protein interaction techniques: assessing contaminating background proteins

Determination of protein-protein interactions is an important component in assigning function and discerning the biological relevance of proteins within a broader cellular context. In vitro protein-protein interaction methodologies, including affinity chromatography, coimmunoprecipitation, and newer approaches such as protein chip arrays, hold much promise in the detection of protein interactions, particularly in well-characterized organisms with sequenced genomes. However, each of these approaches attracts certain background proteins that can thwart detection and identification of true interactors. In addition, recombinant proteins expressed in Escherichia coli are also extensively used to assess protein-protein interactions, and background proteins in these isolates can thus contaminate interaction studies. Rigorous validation of a true interaction thus requires not only that an interaction be found by alternate techniques, but more importantly that researchers be aware of and control for matrix/support dependence. Here, we evaluate these methods for proteins interacting with DmsD (an E. coli redox enzyme maturation protein chaperone), in vitro, using E. coli subcellular fractions as prey sources. We compare and contrast the various in vitro interaction methods to identify some of the background proteins and protein profiles that are inherent to each of the methods in an E. coli system.     

Uncovering new aspects of protein interactions through analysis of specificity landscapes in peptide recognition domains

Protein interactions underlie all biological processes. An important class of protein interactions, often observed in signaling pathways, consists of peptide recognition domains binding short protein segments on the surface of their target proteins. Recent developments in experimental techniques have uncovered many such interactions and shed new lights on their specificity. To analyze these data, novel computational methods have been introduced that can accurately describe the specificity landscape of peptide recognition domains and predict new interactions. Combining large-scale analysis of binding specificity data with structure-based modeling can further reveal new biological insights into the molecular recognition events underlying signaling pathways.     

Proteomic interrogation of human chromatin

Chromatin proteins provide a scaffold for DNA packaging and a basis for epigenetic regulation and genomic maintenance. Despite understanding its functional roles, mapping the chromatin proteome (i.e. the "Chromatome") is still a continuing process. Here, we assess the biological specificity and proteomic extent of three distinct chromatin preparations by identifying proteins in selected chromatin-enriched fractions using mass spectrometry-based proteomics. These experiments allowed us to produce a chromatin catalog, including several proteins ranging from highly abundant histone proteins to less abundant members of different chromatin machinery complexes. Using a Normalized Spectral Abundance Factor approach, we quantified relative abundances of the proteins across the chromatin enriched fractions giving a glimpse into their chromosomal abundance. The large-scale data sets also allowed for the discovery of a variety of novel post-translational modifications on the identified chromatin proteins. With these comparisons, we find one of the probed methods to be qualitatively superior in specificity for chromatin proteins, but inferior in proteomic extent, evidencing a compromise that must be made between biological specificity and broadness of characterization. Additionally, we attempt to identify proteins in eu- and heterochromatin, verifying the enrichments by characterizing the post-translational modifications detected on histone proteins from these chromatin regions. In summary, our results provide insights into the value of different methods to extract chromatin-associated proteins and provide starting points to study the factors that may be involved in directing gene expression and other chromatin-related processes.     

适配体结合蛋白质靶标的亲和力表征方法及其在疾病诊断中的应用

核酸适配体是通过体外指数富集配体系统进化（SELEX）技术筛选获得,并能够和蛋白质靶标高特异性、高亲和力结合的单链寡核苷酸。核酸适配体不但具有抗体的识别特性,而且具有自己独特的优良性能,目前已应用于分析检验、食品安全和生物医药等各个领域。蛋白质具有多种多样的生物功能以及临床诊断价值。因此,核酸适配体针对蛋白质靶标并在蛋白质相关的基础研究领域受到广泛的关注。核酸适配体应用性能的优劣取决于与其靶标蛋白质的亲和力与特异性。本文主要综述核酸适配体对蛋白质靶标的亲和力表征方法,以及在药物研发、肿瘤检测、生物成像以及生物传感器方面的应用。     

蛋白质间相互作用技术的研究近况

蛋白质间相互作用技术的研究近况黄翠芬叶棋浓（军事医学科学院生物工程研究所,北京１００８５０关键词：蛋白质,相互作用,技术ＲｅｃｅｎｔＡｄｖａｎｃｅｓｉｎｔｈｅＴｅｃｈｎｉｑｕｅｓｏｆＰｒｏｔｅｉｎ┐ＰｒｏｔｅｉｎＩｎｔｅｒａｃｔｉｏｎｓＨｕａｎｇＣｕ...     

Refined phylogenetic profiles method for predicting protein-protein interactions

MOTIVATION: The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method. RESULTS: In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.     

Decade of genomics--methods for genome investigation in yeast Saccharomyces cerevisiae

The last ten years, since yeast Saccharomyces cerevisiae genome was sequenced, brought a big impact in genome-wide techniques. The tenth anniversary of genomic era provokes the following resume: a lot of new methods were invented. The yeast strains libraries carrying transposon insertions, gene deletions or tagged proteins have been created. Using them, the phenotypes of gene deletions, as well as the biological activity, cellular localization and possible modifications of their protein products were elucidated. SAGE analysis and DNA microarray experiments showed gene expression profiles and allowed to build interaction networks of gene regulation. The two dimensional gels, mass spectrometry, protein arrays and two-hybrid system carry information about protein interactions, modifications, and biochemical activities. All these methods permit to increase the number of genes with known cellular functions. Moreover, testing these techniques on yeast S. cerevisiae--a model eukaryotic organism--opened the door for their usage in all other species.     

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.     

Proteomic insights into ubiquitin and ubiquitin-like proteins

The dynamic and specific modification of cellular proteins by members of the ubiquitin protein family is a vital regulatory mechanism that lies at the heart of almost all biological processes. Because of both their pervasive and complex nature, these regulatory pathways have been the target of many recent proteomic studies. Such works have provided numerous insights. Through the use of various mass spectrometry techniques, affinity purification methods, and/or chemical probes, large lists have begun to be compiled for the multitude of substrates, interacting partners, and enzymatic components of these regulatory circuits. Furthermore, similar tools have provided many insights into functional aspects such as their mechanisms of substrate specificity and enzymatic activity. This review provides a summary of these recent proteomic works, along with comments on future directions of the field.     

“Fuzzy oil drop” model applied to individual small proteins built of 70 amino acids

The proteins composed of short polypeptides (about 70 amino acid residues) representing the following functional groups (according to PDB notation): growth hormones, serine protease inhibitors, antifreeze proteins, chaperones and proteins of unknown function, were selected for structural and functional analysis. Classification based on the distribution of hydrophobicity in terms of deficiency/excess as the measure of structural and functional specificity is presented. The experimentally observed distribution of hydrophobicity in the protein body is compared to the idealized one expressed by a three-dimensional Gauss function. The differences between these two distributions reveal the specificity of structural/functional characteristics of the protein. The residues of hydrophobicity deficiency versus the idealized distribution are assumed to indicate cavities with the potential to bind ligands, while the residues of hydrophobicity excess are interpreted as potentially participating in protein-protein complexation. The distribution of hydrophobicity irregularity seems to be specific for particular structures and functions of proteins. A comparative analysis of such profiles is carried out to identify the potential biological activity of proteins of unknown function.     

Engineered Allosteric Regulation of Protein Function

Allosteric regulation of proteins has been utilized to study various aspects of cell signaling, from unicellular events to organism-wide phenotypes. However, traditional methods of allosteric regulation, such as constitutively active mutants and inhibitors, lack tight spatiotemporal control. This often leads to unintended signaling consequences that interfere with data interpretation. To overcome these obstacles, researchers employed protein engineering approaches that enable tight control of protein function through allosteric mechanisms. These methods provide high specificity as well as spatial and temporal precision in regulation of protein activity in vitro and in vivo. In this review, we focus on the recent advancements in engineered allosteric regulation and discuss the various bioengineered allosteric techniques available now, from chimeric GPCRs to chemogenetic and optogenetic switches. We highlight the benefits and pitfalls of each of these techniques as well as areas in which future improvements can be made. Additionally, we provide a brief discussion on implementation of engineered allosteric regulation approaches, demonstrating that these tools can shed light on elusive biological events and have the potential to be utilized in precision medicine.     

Assessment of lectin and HILIC based enrichment protocols for characterization of serum glycoproteins by mass spectrometry

Protein glycosylation is a common post-translational modification that is involved in many biological processes, including cell adhesion, protein-protein and receptor-ligand interactions. The glycoproteome constitutes a source for identification of disease biomarkers since altered protein glycosylation profiles are associated with certain human ailments. Glycoprotein analysis by mass spectrometry of biological samples, such as blood serum, is hampered by sample complexity and the low concentration of the potentially informative glycopeptides and -proteins. We assessed the utility of lectin-based and HILIC-based affinity enrichment techniques, alone or in combination, for preparation of glycoproteins and glycopeptides for subsequent analysis by MALDI and ESI mass spectrometry. The methods were successfully applied to human serum samples and a total of 86 N-glycosylation sites in 45 proteins were identified using a mixture of three immobilized lectins for consecutive glycoprotein enrichment and glycopeptide enrichment. The combination of lectin affinity enrichment of glycoproteins and subsequent HILIC enrichment of tryptic glycopeptides identified 81 N-glycosylation sites in 44 proteins. A total of 63 glycosylation sites in 38 proteins were identified by both methods, demonstrating distinct differences and complementarity. Serial application of custom-made microcolumns of mixed, immobilized lectins proved efficient for recovery and analysis of glycopeptides from serum samples of breast cancer patients and healthy individuals to assess glycosylation site frequencies.     

Survey of binding properties of fatty acid-binding proteins. Chromatographic methods

Fatty acid-binding proteins (FABPs) are members of a super family of lipid-binding proteins, and occur intracellularly in vertebrates and invertebrates. This review briefly addresses the structural and molecular properties of the fatty acid binding proteins, together with their potential physiological role. Special attention is paid to the methods used to study the binding characteristics of FABPs. An overview of the conventional (Lipidex, the ADIFAB and ITC) and innovative separation-based techniques (chromatographic and electrophoretic methods) for the study of ligand-protein interactions is presented along with a discussion of their strengths, weak points and potential applications. The best conventional approaches with natural fatty acids have generally revealed only limited information about the interactions of fatty acid proteins. In contrast, high-performance affinity chromatography (HPAC) studies of several proteins provide full information on the binding characteristics. The review uses, as an example, the application of immobilized liver basic FABP as a probe for the study of ligand-protein binding by high-performance affinity chromatography. The FABP from chicken liver has been immobilized on aminopropyl silica and the developed stationary phase was used to examine the enantioselective properties of this protein and to study the binding of drugs to FABP. In order to clarify the retention mechanism, competitive displacement studies were also carried out by adding short chain fatty acids to the mobile phase as displacing agents and preliminary quantitative structure-retention relationship (QSRRs) correlations were developed to describe the nature of the interactions between the chemical structures of the analytes and the observed chromatographic results.The results of these studies may shed light on the proposed roles of these proteins in biological systems and may find applications in medicine and medicinal chemistry. This knowledge will yield a deeper insight into the mechanism of fatty acid binding in order to indisputably show the central role played by FABPs in cellular FA transport and utilization for a proper lipid metabolism.     

Methods for the purification of ubiquitinated proteins

Post-translational protein modification by the covalent conjugation of ubiquitin, originally implicated as a signal for proteolytic degradation by 26S proteasome, has now been realised to play important roles in the regulation of almost all biological processes in eukaryotes. In order to understand these processes in greater detail there is a requirement for techniques that can purify mixtures of ubiquitin-conjugated proteins, as a prerequisite to their identification and characterisation. Here we review the methods that have been applied to the bulk purification of ubiquitinated proteins and discuss their applications in proteomic analyses of the 'ubiquitome'.     

Profiling gene expression using onto-express

Gene expression profiles obtained through microarray or data mining analyses often exist as vast data strings. To interpret the biology of these genetic profiles, investigators must analyze this data in the context of other information such as the biological, biochemical, or molecular function of the translated proteins. This is particularly challenging for a human analyst because large quantities of less than relevant data often bury such information. To address this need we implemented an automated routine, called Onto-Express (http://vortex.cs.wayne.edu:8080), to systematically translate genetic fingerprints into functional profiles. Using strings of accession or cluster identification numbers, Onto-Express searches the public databases and returns tables that correlate expression profiles with the cytogenetic locations, biochemical and molecular functions, biological processes, cellular components, and cellular roles of the translated proteins. The profiles created by Onto-Express fundamentally increase the value of gene expression analyses by facilitating the translation of quantitative value sets to records that contain biological implications.     

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures

Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein–chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.     

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.