Protein-protein interactions: principles, techniques, and their potential role in new drug development

A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.     

Protein microarrays, biosensors, and cell-based methods for secretome-wide extracellular protein-protein interaction mapping

Approximately one quarter of all human genes encode proteins that function in the extracellular space or serve to bridge the extracellular and intracellular environments. Physical associations between these secretome proteins serve to regulate a wide range of biological activities and consequently represent important therapeutic targets. Moreover, some extracellular proteins are targeted by pathogens to allow host access or immune evasion. Despite the importance of extracellular protein-protein interactions, our knowledge in this area has remained sparse. Weak affinities and low abundance have often hindered efforts to identify these interactions using traditional methods such as biochemical purification and cDNA library expression cloning. Moreover, current large-scale protein-protein interaction mapping techniques largely under represent extracellular protein-protein interactions. This review highlights emerging biosensor and protein microarray technology, along with more traditional cell-based techniques, that are compatible with secretome-wide screens for extracellular protein-protein interaction discovery. A combination of these approaches will serve to rapidly expand our knowledge of the extracellular protein-protein interactome.     

From protein interaction networks to novel therapeutic strategies

Cellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.     

Protein interactions study through proximity-labeling

Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms.

Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome.

Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care     

靶向宿主致病菌sRNA的发现及其靶标确定的实验技术进展

人类时常暴露于充满各种致病菌的环境中,这些致病菌与人体细胞或组织之间存在多种相互作用。在相互作用的过程中,细菌通过调节自身毒性、侵袭性等致病性,以适应宿主环境并生存下来,同样,宿主细胞也会通过调动自身的免疫系统来抵抗致病菌的入侵。然而,大多数研究者主要聚焦于致病菌sRNA (small RNA, sRNA)自身生理功能的研究,致病菌与宿主相互作用的认识仍然处于起步阶段。因此,如何使用高灵敏性、高分辨率的方法研究致病菌与宿主之间的相互作用成为当前研究面临的一大难题。本文综合国内外相关研究,概述了目前研究致病菌与宿主相互作用常用的技术方法及实验流程,提高对其机制原理的理解,为致病菌sRNA-宿主靶标的相关研究提供技术参考。     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

White adipose tissue reference network: a knowledge resource for exploring health-relevant relations

Optimal health is maintained by interaction of multiple intrinsic and environmental factors at different levels of complexity—from molecular, to physiological, to social. Understanding and quantification of these interactions will aid design of successful health interventions. We introduce the reference network concept as a platform for multi-level exploration of biological relations relevant for metabolic health, by integration and mining of biological interactions derived from public resources and context-specific experimental data. A White Adipose Tissue Health Reference Network (WATRefNet) was constructed as a resource for discovery and prioritization of mechanism-based biomarkers for white adipose tissue (WAT) health status and the effect of food and drug compounds on WAT health status. The WATRefNet (6,797 nodes and 32,171 edges) is based on (1) experimental data obtained from 10 studies addressing different adiposity states, (2) seven public knowledge bases of molecular interactions, (3) expert’s definitions of five physiologically relevant processes key to WAT health, namely WAT expandability, Oxidative capacity, Metabolic state, Oxidative stress and Tissue inflammation, and (4) a collection of relevant biomarkers of these processes identified by BIOCLAIMS (http://bioclaims.uib.es). The WATRefNet comprehends multiple layers of biological complexity as it contains various types of nodes and edges that represent different biological levels and interactions. We have validated the reference network by showing overrepresentation with anti-obesity drug targets, pathology-associated genes and differentially expressed genes from an external disease model dataset. The resulting network has been used to extract subnetworks specific to the above-mentioned expert-defined physiological processes. Each of these process-specific signatures represents a mechanistically supported composite biomarker for assessing and quantifying the effect of interventions on a physiological aspect that determines WAT health status. Following this principle, five anti-diabetic drug interventions and one diet intervention were scored for the match of their expression signature to the five biomarker signatures derived from the WATRefNet. This confirmed previous observations of successful intervention by dietary lifestyle and revealed WAT-specific effects of drug interventions. The WATRefNet represents a sustainable knowledge resource for extraction of relevant relationships such as mechanisms of action, nutrient intervention targets and biomarkers and for assessment of health effects for support of health claims made on food products.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-014-0439-x) contains supplementary material, which is available to authorized users.     

Elucidating the dynamic remodelling of Escherichia coli interactome in different growth conditions using multiplex co-fractionation MS (mCF-MS)

Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques, including CF-MS, have been developed to capture, identify, and quantify protein-protein interactions (PPIs) on a large scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, 00, e2200404) combined multiplex CF-MS and a new algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. Although the results demonstrated that most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition specific. Further bioinformatics analyses also revealed the biophysical properties and structural patterns that govern such a response.     

The application of gene silencing in proteomics: from laboratory to clinic

Introduction: Since the completion of genome sequencing, gene silencing technologies have emerged as powerful tools to study gene functions in various biological processes, both in vivo and in vitro. Moreover, they have also been proposed as therapeutic agents to inhibit selected genes in a variety of pathological conditions, such as cancer, neurodegenerative, and cardiovascular diseases.

Area covered: This review summarizes the mechanisms of action and applications of genome editing tools, from RNA interference to clustered regularly interspaced short palindromic repeats-based systems, in research and in clinics. We describe their essential role in high-throughput genetic screens and, in particular, in functional proteomics studies, to identify diagnostic markers and therapeutic targets. Indeed, gene silencing and proteomics have been extensively integrated to study global proteome changes, posttranslational modifications, and protein–protein interactions.

Expert commentary: Functional proteomics approaches that leverage gene silencing tools have been successfully applied to examine the role of several genes in various contexts, leading to a deeper knowledge of biological pathways and disease mechanisms. Recent developments of gene silencing tools have improved their performance, also in terms of off-targets effects reduction, paving the way for a wider therapeutic application of these systems.     

Computational identification of protein-protein interactions in rice based on the predicted rice interactome network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

Visualizing protein-protein interactions in living animals

A variety of techniques have been developed to analyze protein-protein interactions in vitro and in cultured cells. However, these methods do not determine how protein interactions affect and are regulated by physiologic and pathophysiologic conditions in living animals. This article describes methodology for detecting and quantifying protein interactions in living mice, using an inducible two-hybrid system developed for positron emission tomography (PET) imaging. We discuss the methods to establish stably transfected cells with components of the imaging system, create tumor xenografts, synthesize PET radiopharmaceuticals used to visualize the imaging reporter, perform microPET imaging, and analyze data from imaging studies. Development and application of technologies for molecular imaging of protein-protein interactions in vivo should enable researchers to investigate intrinsic binding specificities of proteins during normal development and disease progression as well as aid drug development through direct interrogation of molecular targets within intact animals.     

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.     

Solid-state NMR structures of integral membrane proteins

Abstract

Solid-state NMR is unique for its ability to obtain three-dimensional structures and to measure atomic-resolution structural and dynamic information for membrane proteins in native lipid bilayers. An increasing number and complexity of integral membrane protein structures have been determined by solid-state NMR using two main methods. Oriented sample solid-state NMR uses macroscopically aligned lipid bilayers to obtain orientational restraints that define secondary structure and global fold of embedded peptides and proteins and their orientation and topology in lipid bilayers. Magic angle spinning (MAS) solid-state NMR uses unoriented rapidly spinning samples to obtain distance and torsion angle restraints that define tertiary structure and helix packing arrangements. Details of all current protein structures are described, highlighting developments in experimental strategy and other technological advancements. Some structures originate from combining solid- and solution-state NMR information and some have used solid-state NMR to refine X-ray crystal structures. Solid-state NMR has also validated the structures of proteins determined in different membrane mimetics by solution-state NMR and X-ray crystallography and is therefore complementary to other structural biology techniques. By continuing efforts in identifying membrane protein targets and developing expression, isotope labelling and sample preparation strategies, probe technology, NMR experiments, calculation and modelling methods and combination with other techniques, it should be feasible to determine the structures of many more membrane proteins of biological and biomedical importance using solid-state NMR. This will provide three-dimensional structures and atomic-resolution structural information for characterising ligand and drug interactions, dynamics and molecular mechanisms of membrane proteins under physiological lipid bilayer conditions.     

Network Neighbors of Drug Targets Contribute to Drug Side-Effect Similarity

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.     

Disruption of protein-protein interactions: towards new targets for chemotherapy

Protein-protein interactions play a key role in various mechanisms of cellular growth and differentiation, and in the replication of pathogen organisms in host cells. Thus, inhibition of these interactions is a promising novel approach for rational drug design against a wide number of cellular and microbial targets. In the past few years, attempts to inhibit protein-protein interactions using antibodies, peptides, and synthetic or natural small molecules have met with varying degrees of success, and these will be the focus of this review.     

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer,COPD, and asthma

Introduction: Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges.

Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma.

Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.     

Antibody-Array Interaction Mapping, a New Method to Detect Protein Complexes Applied to the Discovery and Study of Serum Amyloid P Interactions with Kininogen in Human Plasma

Protein-protein interactions are fundamentally important in biological processes, but the existing analytical tools have limited ability to sensitively and precisely measure the dynamic composition of protein complexes in biological samples. We report here the development of antibody-array interaction mapping (AAIM) to address that need. We used AAIM to probe interactions among a set of 48 proteins in serum and found several known interactions as well potentially novel interactions, including multiprotein clusters of interactions. A novel interaction initially identified between the innate immune system protein C-reactive protein and the inflammatory protein kininogen (KNG) was confirmed in subsequent experiments to involve serum amyloid P instead of its highly related family member, C-reactive protein. AAIM was used in a variety of formats to further study this interaction. In vitro studies confirmed the ability of the purified proteins to interact and revealed a zinc dependence of the interaction. Studies using plasma samples collected longitudinally following a controlled myocardial infarction revealed no consistent changes in the serum amyloid P-KNG interaction levels but consistent changes in KNG activation and interactions with plasma prekallikrein. These results demonstrate a versatile platform for measuring the dynamic composition of protein complexes in biological samples that should have value for studies of normal and disease-related signaling networks, multiprotein clusters, or enzymatic cascades.Protein-protein interactions form the basis of many types of biological processes, such as signal transduction, immune recognition, and metabolism. Individual proteins may have myriad interaction partners, existing in static conditions in multiprotein units or dynamically in the context of interconnected networks of interactions. Major efforts are underway to identify protein-protein interactions (1, 2), to model networks of interactions (3–5), and to understand the biochemical basis for interactions (6, 7). Increased information about protein-protein interactions ultimately will enable a better determination of individual protein functions and a better understanding of biological systems.Although information regarding interaction partners among proteins has been expanding rapidly, data describing the dynamic variation in the composition of protein complexes in biological samples are still limited. Such information is important for determining the involvement of interactions in particular biological processes, for understanding how interactions are regulated, or for identifying abnormal levels that may be involved in pathologies. The relative lack of knowledge in this area relates to the capabilities of conventional approaches for examining protein-protein interactions. A standard approach is to isolated a tagged or antibody-targeted protein and probe the interacting proteins using Western blots or mass spectrometry (8). Such approaches are valuable for identifying interaction partners directly from biological samples, but these methods do not have the reproducibility required to accurately measure changes in interaction levels between samples. Other methods include yeast two-hybrid screens, which are widely used for identifying or confirming direct, binary interactions between proteins generated from cDNA (9), and protein arrays, which have found broad use for examining interactions between purified proteins and other types of ligands (5, 10, 11). Although effective for gathering certain types of information, these methods still do not enable an analysis of the composition of protein complexes in biological samples. Therefore, the development of additional methods for identifying and measuring protein-protein interactions is an important goal.Antibody-based detection allows the sensitive and reproducible detection of specific targets in complex backgrounds. Interactions between individual proteins can be detected in an ELISA-type format using “mismatched” pairs of capture and detection antibodies in which each antibody targets one member of the complex as demonstrated in a study of C-reactive protein (CRP)1-complement interactions (12). Building on that principle, we developed antibody-array interaction mapping (AAIM) for the detection and measurement of interactions among a larger, defined set of proteins. By combining antibody-based interaction detection with the capabilities of low volume, high throughput microarrays (13), multiple potential interactions, or selected interactions using multiple antibodies, can be examined. AAIM complements the existing repertoire of methods for measuring protein-protein interactions by enabling the sensitive, reproducible, and high throughput detection of multiprotein complexes in their native, biological states.We applied this technology to the study of protein-protein interactions in the circulation. For most blood proteins, little is known about whether they circulate alone, with a few other proteins, or as parts of large, multiprotein clusters. Even less is known regarding changes related to various physiological states. Circulating protein-protein interactions are particularly important in the inflammation and coagulation systems in which the maintenance of health depends upon the regulation and accurate processing of multiple signals. Antibody-array interaction mapping offers a unique opportunity for a more comprehensive evaluation of multiple protein-protein interactions in the blood. Furthermore, antibody-based methods can provide high specificity detection even in the presence of high concentration background proteins, which can be problematic when using mass spectrometry detection. Therefore, we used AAIM both as a discovery tool and as a method to study particular blood-based interactions under a variety of conditions. We show that AAIM can be used to measure dynamic changes in interactions using both in vitro experiments and clinical samples. Furthermore, we present the discovery and initial characterization of an interaction that provides new links between innate immunity and inflammation or the contact system of coagulation.     

Evaluation of Physical and Functional Protein-Protein Interaction Prediction Methods for Detecting Biological Pathways

Background

Cellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway.

Methods

Towards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity.

Conclusions

Our results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also shown that the effective use of a particular prediction method depends on the pathway under investigation. In case one is not focused on specific pathway, gene expression similarity method is the best option.     

Recent advances in platelet proteomics

Platelets are the fundamental players in primary hemostasis, but are also involved in several pathological conditions. The remarkable advances in proteomic methodologies have allowed a better understanding of the basic physiological pathways underlying platelet biology. In addition, recent platelet proteomics focused on disease conditions, helping to elucidate the molecular mechanisms of complex and/or unknown human disorders and to find novel biomarkers for early diagnosis and drug targets. The most common and innovative proteomic techniques, both gel-based and gel-free, used in platelet proteomics will be reviewed here. A particular focus will be given to studies that used a subproteomic strategy to analyze specific platelet conditions (resting or activated), compartments (membrane, granules and microparticles) or fractions (phosphoproteome or glycoproteome). The thousands of platelet proteins and interactions discovered so far by these different powerful proteomic approaches represent a precious source of information for both basic science and clinical applications in the field of platelet biology.     

Isocyanide-based multicomponent reactions in drug discovery

This review describes recent advances in the application of isocyanide-based multicomponent reactions (IMCRs) in drug discovery and summarizes the various chemotypes used to probe biological targets. In the past couple of years, IMCR-derived ligands have been used to develop agents against infectious diseases and to interfere with protein-protein interactions. Additionally, they were active against a variety of targets such as enzymes, GPCRs and ion channels. The rational for the chemical biologist to apply such diversity generating chemistries is also discussed.