蛋白质相互作用的生物信息学研究进展

生命过程的分子基础在于生物分子之间的相互作用,其中蛋白质分子之间的相互作用占有极其重要的地位。研究蛋白质相互作用对于理解生命的真谛、探讨致病微生物的致病机理,以及研究新药提高人们的健康水平具有重要的作用。用生物信息学的方法研究蛋白质的相互作用已经取得了许多重要的成果,但也有很多问题还需解决。本文从蛋白质相互作用的数据库、预测方法、可预测蛋白质相互作用的网上服务、蛋白质相互作用网络等几方面,对蛋白质相互作用的生物信息学研究成果及其存在的问题做了概述。     

Noninvasive imaging of protein-protein interactions in living organisms

Genomic research is expected to generate new types of complex observational data, changing the types of experiments as well as our understanding of biological processes. The investigation and definition of relationships among proteins is essential for understanding the function of each gene and the mechanisms of biological processes that specific genes are involved in. Recently, a study by Paulmurugan et al. demonstrated a tool for in vivo noninvasive imaging of protein-protein interactions and intracellular networks.     

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-Protein Interactions: Principles,Techniques, and their Potential Role in New Drug Development

Abstract

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.     

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.     

Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data

MOTIVATION: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data. RESULTS: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein-protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein-protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein-protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.     

Methods for mapping of interaction networks involving membrane proteins

Nearly one-third of all genes in various organisms encode membrane-associated proteins that participate in numerous protein-protein interactions important to the processes of life. However, membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is limited with respect to the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.     

Co-evolutionary analysis reveals insights into protein-protein interactions

Protein-protein interactions play crucial roles in biological processes. Experimental methods have been developed to survey the proteome for interacting partners and some computational approaches have been developed to extend the impact of these experimental methods. Computational methods are routinely applied to newly discovered genes to infer protein function and plausible protein-protein interactions. Here, we develop and extend a quantitative method that identifies interacting proteins based upon the correlated behavior of the evolutionary histories of protein ligands and their receptors. We have studied six families of ligand-receptor pairs including: the syntaxin/Unc-18 family, the GPCR/G-alpha's, the TGF-beta/TGF-beta receptor system, the immunity/colicin domain collection from bacteria, the chemokine/chemokine receptors, and the VEGF/VEGF receptor family. For correlation scores above a defined threshold, we were able to find an average of 79% of all known binding partners. We then applied this method to find plausible binding partners for proteins with uncharacterized binding specificities in the syntaxin/Unc-18 protein and TGF-beta/TGF-beta receptor families. Analysis of the results shows that co-evolutionary analysis of interacting protein families can reduce the search space for identifying binding partners by not only finding binding partners for uncharacterized proteins but also recognizing potentially new binding partners for previously characterized proteins. We believe that correlated evolutionary histories provide a route to exploit the wealth of whole genome sequences and recent systematic proteomic results to extend the impact of these studies and focus experimental efforts to categorize physiologically or pathologically relevant protein-protein interactions.     

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.     

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。     

DIP: the database of interacting proteins

The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. This database is intended to provide the scientific community with a comprehensive and integrated tool for browsing and efficiently extracting information about protein interactions and interaction networks in biological processes. Beyond cataloging details of protein-protein interactions, the DIP is useful for understanding protein function and protein-protein relationships, studying the properties of networks of interacting proteins, benchmarking predictions of protein-protein interactions, and studying the evolution of protein-protein interactions.     

Analysing protein-protein interactions with the yeast two-hybrid system

Plant research is moving into the post-genomic era. Proteomic-based strategies are now being developed to study functional aspects of the genes predicted from the various genome-sequencing initiatives. All biological processes depend on interactions formed between proteins and the mapping of such interactions on a global scale is providing interesting functional insights. One of the techniques that has proved itself invaluable in the mapping of protein-protein interactions is the yeast two-hybrid system. This system is a sensitive molecular genetic approach for studying protein-protein interactions in vivo. In this review we will introduce the yeast two-hybrid system, discuss modifications of the system that may be of interest to the plant science community and suggest potential applications of the technology.     

Protein coadaptation and the design of novel approaches to identify protein-protein interactions

Proteins rarely function in isolation but they form part of complex networks of interactions with other proteins within or among cells. The importance of a particular protein for cell viability is directly dependent upon the number of interactions where it participates and the function it performs: the larger the number of interactions of a protein the greater its functional importance is for the cell. With the advent of genome sequencing and "omics" technologies it became feasible conducting large-scale searches for protein interacting partners. Unfortunately, the accuracy of such analyses has been underwhelming owing to methodological limitations and to the inherent complexity of protein interactions. In addition to these experimental approaches, many computational methods have been developed to identify protein-protein interactions by assuming that interacting proteins coevolve resulting from the coadaptation dynamics between the amino acids of their interacting faces. We review the main technological advances made in the field of interactomics and discuss the feasibility of computational methods to identify protein-protein interactions based on the estimation of coevolution. As proof-of-concept, we present a classical case study: the interactions of cell surface proteins (receptors) and their ligands. Finally, we take this discussion one step forward to include interactions between organisms and species to understand the generation of biological complexity. Development of technologies for accurate detection of protein-protein interactions may shed light on processes that go from the fine-tuning of pathways and metabolic networks to the emergence of biological complexity.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.     

Protein complementation as tool for studying protein-protein interactions in living cells

The association and dissociation of protein-protein complexes play an important role in various processes in living cells. The disruption of protein-protein interactions is observed in various pathologies. The study of the nature of these interactions will contribute to a better understanding of the molecular basis of the pathogenesis of the disease and the development of new approaches to therapy. Now there is a set of methods that allow one to reveal and analyze the interaction of proteins in vitro. However, more accurate data can be obtained by studying protein-protein interactions in vivo. One of a few prospective methods is based on the effect of the complementation of fragments of reporter proteins. These reporter systems are based on the change in the fluorescent properties or enzymatic activity of the proteins that can be measured using colorimetric, fluorescent, or other substrates. The principle of the complementation is widely used to analyze protein interactions, to determine of order of interaction of protein partners in different signaling pathways, as well as in high-performance screening studies for detecting and mapping previously unknown protein-protein interactions. The possibilities of existing complementation reporter systems allow one to solve problems that are far beyond the simple registration of the interactions of two or more proteins.     

Augmented transitive relationships with high impact protein distillation in protein interaction prediction

Predicting new protein-protein interactions is important for discovering novel functions of various biological pathways. Predicting these interactions is a crucial and challenging task. Moreover, discovering new protein-protein interactions through biological experiments is still difficult. Therefore, it is increasingly important to discover new protein interactions. Many studies have predicted protein-protein interactions, using biological features such as Gene Ontology (GO) functional annotations and structural domains of two proteins. In this paper, we propose an augmented transitive relationships predictor (ATRP), a new method of predicting potential protein interactions using transitive relationships and annotations of protein interactions. In addition, a distillation of virtual direct protein-protein interactions is proposed to deal with unbalanced distribution of different types of interactions in the existing protein-protein interaction databases. Our results demonstrate that ATRP can effectively predict protein-protein interactions. ATRP achieves an 81% precision, a 74% recall and a 77% F-measure in average rate in the prediction of direct protein-protein interactions. Using the generated benchmark datasets from KUPS to evaluate of all types of the protein-protein interaction, ATRP achieved a 93% precision, a 49% recall and a 64% F-measure in average rate. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Cellular Localization and Characterization of Cytosolic Binding Partners for Gla Domain-containing Proteins PRRG4 and PRRG2

The genes encoding a family of proteins termed proline-rich γ-carboxyglutamic acid (PRRG) proteins were identified and characterized more than a decade ago, but their functions remain unknown. These novel membrane proteins have an extracellular γ-carboxyglutamic acid (Gla) protein domain and cytosolic WW binding motifs. We screened WW domain arrays for cytosolic binding partners for PRRG4 and identified novel protein-protein interactions for the protein. We also uncovered a new WW binding motif in PRRG4 that is essential for these newly found protein-protein interactions. Several of the PRRG-interacting proteins we identified are essential for a variety of physiologic processes. Our findings indicate possible novel and previously unidentified functions for PRRG proteins.