Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Integrated analysis of multiple data sources reveals modular structure of biological networks

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.     

Computational methods of analysis of protein-protein interactions

Computational methods play an important role at all stages of the process of determining protein-protein interactions. They are used to predict potential interactions, to validate the results of high-throughput interaction screens and to analyze the protein networks inferred from interaction databases.     

From sequence to function: using RNAi to elucidate mechanisms of human disease

RNA interference (RNAi) has emerged as one of the most powerful tools for functionally characterizing large sets of genomic data. Capabilities of RNAi place it at the forefront of high-throughput screens, which are able to span the human genome in search of novel targets. Although RNAi screens have been used to elucidate pathway components and discover potential drug targets in lower organisms, including Caenorhabditis elegans and Drosophila, only recently has the technology been advanced to a state in which large-scale screens can be performed in mammalian cells. In this review, we will evaluate the major advancements in the field of mammalian RNAi, specifically in terms of high-throughput assays. Crucial points of experimental design will be highlighted, as well as suggestions as to how to interpret and follow-up on potential cell death targets. Finally, we assess the prospective applications of high-throughput screens, the data they are capable of generating, and the potential for this technique to further our understanding of human disease.     

Development and application of a DNA microarray-based yeast two-hybrid system

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.     

大规模高通量方法在蛋白质相互作用研究中的应用

随着后基因组时代的到来,阐明蛋白质间相互作用关系成为蛋白质研究的又一热点,促进了相关技术的不断产生、发展和完善.其中涉及到诸多大规模高通量的方法,如双杂交系统、噬菌体展示、质谱、蛋白质芯片以及生物信息学等,这为系统分析蛋白质相互作用提供视点,有望在蛋白质组学研究中发挥重要作用.每种方法各有其优缺点且适用范围不同,在一定程度上各方法的实验结果互为补充.现拟就这些大规模高通量方法的研究进展及其在蛋白质相互作用研究中的应用作一综述.     

High-content siRNA screening

Very recent developments in instrumentation and image analysis have made microscopy applicable to high-throughput screening (HTS). For 'High-Content Screening' modern automated microscopy systems provide a throughput of up to 100,000 (confocal) images, with amazingly high resolution, of cells fluorescently stained using multiple colours that are imaged simultaneously during the screen. Image analysis tools provide multi-parametric pattern extraction and quantification on-the-fly. Big pharmaceutical companies have presented image-based screens of more than 100,000 compounds, while academia has published data on large RNA interference screens for functional genomics. Numerous whole-genome sequencing projects have been completed and published. Gene annotation is still in flux. Nevertheless, about 23,000 human genes have been reliably annotated. Additionally, gene expression array technologies and proteomics have added further data on molecules present in cells and tissues. The major challenge of the present and future is to unravel the detailed function of all these gene products and their interaction. One way to gain insight, is to design oligonucleotides that induce lack-of-function phenotypes by specifically inhibiting protein production.     

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

Background  

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes.     

High-throughput RNAi screening to dissect cellular pathways: A how-to guide

RNA interference (RNAi) has become a powerful tool to dissect cellular pathways and characterize gene functions. The availability of genome-wide RNAi libraries for various model organisms and mammalian cells has enabled high-throughput RNAi screenings. These RNAi screens successfully identified key components that had previously been missed in classical forward genetic screening approaches and allowed the assessment of combined loss-of-function phenotypes. Crucially, the quality of RNAi screening results depends on quantitative assays and the choice of the right biological context. In this review, we provide an overview on the design and application of high-throughput RNAi screens as well as data analysis and candidate validation strategies.     

Large-scale mapping of human protein interactome using structural complexes

Although the identification of protein interactions by high-throughput (HTP) methods progresses at a fast pace, 'interactome' data sets still suffer from high rates of false positives and low coverage. To map the human protein interactome, we describe a new framework that uses experimental evidence on structural complexes, the atomic details of binding interfaces and evolutionary conservation. The structurally inferred interaction network is highly modular and more functionally coherent compared with experimental interaction networks derived from multiple literature citations. Moreover, structurally inferred and high-confidence HTP networks complement each other well, allowing us to construct a merged network to generate testable hypotheses and provide valuable experimental leads.     

Inferring physical protein contacts from large-scale purification data of protein complexes

Recent large-scale data sets of protein complex purifications have provided unprecedented insights into the organization of cellular protein complexes. Several computational methods have been developed to detect co-complexed proteins in these data sets. Their common aim is the identification of biologically relevant protein complexes. However, much less is known about the network of direct physical protein contacts within the detected protein complexes. Therefore, our work investigates whether direct physical contacts can be computationally derived by combining raw data of large-scale protein complex purifications. We assess four established scoring schemes and introduce a new scoring approach that is specifically devised to infer direct physical protein contacts from protein complex purifications. The physical contacts identified by the five methods are comprehensively benchmarked against different reference sets that provide evidence for true physical contacts. Our results show that raw purification data can indeed be exploited to determine high-confidence physical protein contacts within protein complexes. In particular, our new method outperforms competing approaches at discovering physical contacts involving proteins that have been screened multiple times in purification experiments. It also excels in the analysis of recent protein purification screens of molecular chaperones and protein kinases. In contrast to previous findings, we observe that physical contacts inferred from purification experiments of protein complexes can be qualitatively comparable to binary protein interactions measured by experimental high-throughput assays such as yeast two-hybrid. This suggests that computationally derived physical contacts might complement binary protein interaction assays and guide large-scale interactome mapping projects by prioritizing putative physical contacts for further experimental screens.     

Computational detection of protein complexes in AP-MS experiments

Protein complex identification is an important goal of protein-protein interaction analysis. To date, development of computational methods for detecting protein complexes has been largely motivated by genome-scale interaction data sets from high-throughput assays such as yeast two-hybrid or tandem affinity purification coupled with mass spectrometry (TAP-MS). However, due to the popularity of small to intermediate-scale affinity purification-mass spectrometry (AP-MS) experiments, protein complex detection is increasingly discussed in local network analysis. In such data sets, protein complexes cannot be detected using binary interaction data alone because the data contain interactions with tagged proteins only and, as a result, interactions between all other proteins remain unobserved, limiting the scope of existing algorithms. In this article, we provide a pragmatic review of network graph-based computational algorithms for protein complex analysis in global interactome data, without requiring any computational background. We discuss the practical gap in applying these algorithms to recently surging small to intermediate-scale AP-MS data sets, and review alternative clustering algorithms using quantitative proteomics data and their limitations.     

Toward an integrated map of genetic interactions in cancer cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype‐dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene‐background relationships. In addition to known dependencies, we identified new genotype‐specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β‐catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.     

High-throughput RNAi in Caenorhabditis elegans: genome-wide screens and functional genomics

The phenomenon of RNA-mediated interference (RNAi) was first discovered in the nematode Caenorhabditis elegans, in which introduction of double-stranded RNA causes specific inactivation of genes with corresponding sequences. Technical advances in RNAi methodology and the availability of the complete genome sequence have enabled the high-throughput, genome-wide RNAi analysis of this organism. Several groups have used large-scale RNAi to systematically examine every C. elegans gene for knock-down phenotypes, providing basal information to be mined in more detailed studies. Now, in addition to functional genomic RNAi analyses, high-throughput RNAi is also routinely used for rapid, genome-wide screens for genes involved in specific biological processes. The integration of high-throughput RNAi experiments with other large-scale data, such as DNA microarrays and protein-protein interaction maps, enhances the speed and reliability of such screens. The accumulation of RNAi phenotype data dramatically accelerates our understanding of this organism at the genetic level.     

Ion mobility–mass spectrometry for structural proteomics

Ion mobility coupled to mass spectrometry has been an important tool in the fields of chemical physics and analytical chemistry for decades, but its potential for interrogating the structure of proteins and multiprotein complexes has only recently begun to be realized. Today, ion mobility–mass spectrometry is often applied to the structural elucidation of protein assemblies that have failed high-throughput crystallization or NMR spectroscopy screens. Here, we highlight the technology, approaches and data that have led to this dramatic shift in use, including emerging trends such as the integration of ion mobility–mass spectrometry data with more classical (e.g., ‘bottom-up’) proteomics approaches for the rapid structural characterization of protein networks.     

Protein expression clone repositories for functional proteomics

The information generated from the sequence of the human genome has inspired efforts to systematically develop organized collections of human cDNA clones for use in expression screens in mammalian cells. These high-throughput cloning initiatives offer significant advantages over the cDNA libraries that have been used in the past, including greater experimental flexibility, immediate identification of hits, information regarding all tested proteins (even for those giving no response) and eventually more comprehensive coverage. Some of the lessons learned and the considerations that underlie the creation of genome-wide cDNA repositories are discussed here. Although still inchoate, these resources are already impacting the manner in which high-throughput functional screens are performed.     

Increasing specificity in high-throughput yeast two-hybrid experiments

Since its inception, the yeast two-hybrid (Y2H) system has proven to be an efficient system to identify novel protein-protein interactions. However, Y2H screens are sometimes criticized for generating high rates of false-positives. Minimizing false-positive interactions is especially important in proteome wide high-throughput (HT) Y2H. Here, we summarize various approaches that reduce false-positives in HT-Y2H projects. We evaluated the potential of examining putative positives after removing the prey encoding plasmid by negative selection. We found that this method reliably identifies false-positives caused by spontaneous conversion of baits into auto-activators and provides significant time-savings in HT screens. In addition, we present a method to eliminate an important source of false-positives: contaminating prey plasmids. Y2H interactors can be wrongly identified due to the presence of two or more different plasmids in the cells of a single yeast colony. Of these independent plasmids, only one encodes a genuine interactor. Contaminating plasmids are eliminated by extended culture of yeast cells under positive selection for the interaction, allowing the identification of the true interaction partner.     

Questioning the Ubiquity of Neofunctionalization

Gene duplication provides much of the raw material from which functional diversity evolves. Two evolutionary mechanisms have been proposed that generate functional diversity: neofunctionalization, the de novo acquisition of function by one duplicate, and subfunctionalization, the partitioning of ancestral functions between gene duplicates. With protein interactions as a surrogate for protein functions, evidence of prodigious neofunctionalization and subfunctionalization has been identified in analyses of empirical protein interactions and evolutionary models of protein interactions. However, we have identified three phenomena that have contributed to neofunctionalization being erroneously identified as a significant factor in protein interaction network evolution. First, self-interacting proteins are underreported in interaction data due to biological artifacts and design limitations in the two most common high-throughput protein interaction assays. Second, evolutionary inferences have been drawn from paralog analysis without consideration for concurrent and subsequent duplication events. Third, the theoretical model of prodigious neofunctionalization is unable to reproduce empirical network clustering and relies on untenable parameter requirements. In light of these findings, we believe that protein interaction evolution is more persuasively characterized by subfunctionalization and self-interactions.     

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.