Discovery Of Protein Complexes Research Articles

Abstract PR07: Unravelling the epigenetic landscape of pancreatic cancer: The role of cancer-associated fibroblasts

Abstract Pancreatic cancer is set to become the 3rd deadliest cancer by 2025. Pancreatic ductal adenocarcinoma (PDAC) represents the majority of cases and is the most aggressive subtype, with a 5-year survival rate of around 10% and patients often diagnosed at a metastatic stage. Current treatments offer dismal survival benefit for the patients, highlighting the need for innovative therapeutic strategies. PDAC is characterized by genetic and epigenetic modifications and an abundant tumor microenvironment (TME) largely composed of cancer-associated fibroblasts (CAFs). Multiple populations of CAFs have been described and implicated in PDAC progression, however, little is known regarding the molecular crosstalk between them and the cancer cells. This project aims to investigate how the epigenetic landscape of PDAC is affected by recently described CAF populations. We have optimized a co-culture model consisting of cancer cells and genetically-locked inflammatory CAFs (iCAFs) and myofibroblastic CAFs (myCAFs) to study the link between the TME and the gene regulatory machinery in PDAC through proteomic and genomic approaches. Firstly, a full proteome analysis revealed the two CAF populations drive expression of different subsets of proteins in PDAC cells, including transcription factors. We then interrogated chromatin accessibility via ATAC-seq, which showed that CAF subsets differentially affect chromatin dynamics in epithelial cells. Transcription factor interactomes were investigated using rapid immunoprecipitation of endogenous proteins (RIME), a method developed in our lab that allows the discovery of protein complexes in a robust manner. We leverage this technique to discover druggable transcription factor interactors. RIME data shows that upon co-culture with each CAF population, distinct interactors are recruited towards FOXA1, a pioneer factor that drives a transcriptional program associated with PDAC metastasis to the liver, and towards FOXA2, which has been shown to play distinct roles in different grades of PDAC. We are now assessing the potential of these interactors as drug candidates in each context. Chromatin immunoprecipitation assays have shown that the CAF populations also affect the binding profile of FOXA1 and FOXA2 to the chromatin. We are currently investigating how CAFs impact tumor progression in vivo using an orthotopic implantation model. Preliminary data suggests that CAF subsets affect the metastatic profile in PDAC, as tumors co-injected with iCAFs metastasize to different organs than those co-injected with myCAFs. Ongoing experiments aim to explore the differences between tumors and metastases at the epigenetic level. Subsequent studies will aim to employ distinct approaches that target tumor progression in each context. PDAC is a highly heterogeneous cancer with several layers of complexity; therefore, a more holistic approach is critical to tackle the disease. Understanding tumor-stroma interactions from an epigenetics standpoint may enable identification of druggable molecules much needed for PDAC patients. Citation Format: Catarina Pelicano, Igor Chernukhin, Lisa Young, Naomi Vranas, Yi Cheng, Joshua Kent, Kamal Kishore, Clive D'Santos, Alasdair Russell, Giulia Biffi, Shalivi V. Rao, Jason S. Carroll. Unravelling the epigenetic landscape of pancreatic cancer: The role of cancer-associated fibroblasts [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr PR07.

Protein Complex Identification Based on Heterogeneous Protein Information Network.

Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein-protein interaction (PPI) network, whereas some methods attempt to integrate biological information to enhance the features of the protein network for complex identification. Existing protein complex identification methods are unable to fully integrate network topology information and biological attribute information. Most of these methods are based on homogeneous networks and cannot distinguish the importance of different attributes and protein nodes. To address these issues, a GO attribute Heterogeneous Attention network Embedding (GHAE) method based on heterogeneous protein information networks is proposed. First, GHAE incorporates Gene Ontology (GO) information into the PPI network, constructing a heterogeneous protein information network. Then, GHAE uses a dual attention mechanism and heterogeneous graph convolutional representation learning method to learn protein features and to identify protein complexes. The experimental results show that building heterogeneous protein information networks can fully integrate valuable biological information. The heterogeneous graph embedding learning method can simultaneously mine the features of protein and GO attributes, thereby improving the performance of protein complex identification.

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy.

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein-protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein-protein interactions and a unique perspective on possible novel protein complexes.

PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.

Investigating Mitotic Inheritance of Histone Modifications Using Tethering Strategies.

The covalent and reversible modification of histones enables cells to establish heritable gene expression patterns without altering their genetic blueprint. Epigenetic mechanisms regulate gene expression in two separate ways: (1) establishment, which depends on sequence-specific DNA- or RNA-binding proteins that recruit histone-modifying enzymes to unique genomic loci, and (2) maintenance, which is sequence-independent and depends on the autonomous propagation of preexisting chromatin states during DNA replication. Only a subset of the vast repertoire of histone modifications in the genome is heritable. Here, we describe a synthetic biology approach to tether histone-modifying enzymes to engineer chromatin states in living cells and evaluate their potential for mitotic inheritance. In S. pombe, fusing the H3K9 methyltransferase, Clr4, to the tetracycline-inducible TetR DNA-binding domain facilitates rapid and reversible control of heterochromatin assembly. We describe a framework to successfully implement an inducible heterochromatin establishment system and evaluate its molecular properties. We anticipate that our innovative genetic strategy will be broadly applicable to the discovery of protein complexes and separation-of-function alleles of heterochromatin-associated factors with unique roles in epigenetic inheritance.

Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases.

MotivationGene expression data are commonly used at the intersection of cancer research and machine learning for better understanding of the molecular status of tumour tissue. Deep learning predictive models have been employed for gene expression data due to their ability to scale and remove the need for manual feature engineering. However, gene expression data are often very high dimensional, noisy and presented with a low number of samples. This poses significant problems for learning algorithms: models often overfit, learn noise and struggle to capture biologically relevant information. In this article, we utilize external biological knowledge embedded within structures of gene interaction graphs such as protein–protein interaction (PPI) networks to guide the construction of predictive models.ResultsWe present Gene Interaction Network Constrained Construction (GINCCo), an unsupervised method for automated construction of computational graph models for gene expression data that are structurally constrained by prior knowledge of gene interaction networks. We employ this methodology in a case study on incorporating a PPI network in cancer phenotype prediction tasks. Our computational graphs are structurally constructed using topological clustering algorithms on the PPI networks which incorporate inductive biases stemming from network biology research on protein complex discovery. Each of the entities in the GINCCo computational graph represents biological entities such as genes, candidate protein complexes and phenotypes instead of arbitrary hidden nodes of a neural network. This provides a biologically relevant mechanism for model regularization yielding strong predictive performance while drastically reducing the number of model parameters and enabling guided post-hoc enrichment analyses of influential gene sets with respect to target phenotypes. Our experiments analysing a variety of cancer phenotypes show that GINCCo often outperforms support vector machine, Fully Connected Multi-layer Perceptrons (MLP) and Randomly Connected MLPs despite greatly reduced model complexity.Availability and implementation https://github.com/paulmorio/gincco contains the source code for our approach. We also release a library with algorithms for protein complex discovery within PPI networks at https://github.com/paulmorio/protclus. This repository contains implementations of the clustering algorithms used in this article.Supplementary information Supplementary data are available at Bioinformatics online.

PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery.

For high-dimensional expression data, most prognostic models perform feature selection based on individual genes, which usually lead to unstable prognosis, and the identified risk genes are inherently insufficient in revealing complex molecular mechanisms. Since most genes carry out cellular functions by forming protein complexes-basic representatives of functional modules, identifying risk protein complexes may greatly improve our understanding of disease biology. Coupled with the fact that protein complexes have been shown to have innate resistance to batch effects and are effective predictors of disease phenotypes, constructing prognostic models and selecting features with protein complexes as the basic unit should improve the robustness and biological interpretability of the model. Here, we propose a protein complex-based, group lasso-Cox model (PCLasso) to predict patient prognosis and identify risk protein complexes. Experiments on three cancer types have proved that PCLasso has better prognostic performance than prognostic models based on individual genes. The resulting risk protein complexes not only contain individual risk genes but also incorporate close partners that synergize with them, which may promote the revealing of molecular mechanisms related to cancer progression from a comprehensive perspective. Furthermore, a pan-cancer prognostic analysis was performed to identify risk protein complexes of 19 cancer types, which may provide novel potential targets for cancer research.

A Label-free Mass Spectrometry Method to Predict Endogenous Protein Complex Composition.

Information on the composition of protein complexes can accelerate mechanistic analyses of cellular systems. Protein complex composition identifies genes that function together and provides clues about regulation within and between cellular pathways. Cytosolic protein complexes control metabolic flux, signal transduction, protein abundance, and the activities of cytoskeletal and endomembrane systems. It has been estimated that one third of all cytosolic proteins in leaves exist in an oligomeric state, yet the composition of nearly all remain unknown. Subunits of stable protein complexes copurify, and combinations of mass-spectrometry-based protein correlation profiling and bioinformatic analyses have been used to predict protein complex subunits. Because of uncertainty regarding the power or availability of bioinformatic data to inform protein complex predictions across diverse species, it would be highly advantageous to predict composition based on elution profile data alone. Here we describe a mass spectrometry-based protein correlation profiling approach to predict the composition of hundreds of protein complexes based on biochemical data. Extracts were obtained from an intact organ and separated in parallel by size and charge under nondenaturing conditions. More than 1000 proteins with reproducible elution profiles across all replicates were subjected to clustering analyses. The resulting dendrograms were used to predict the composition of known and novel protein complexes, including many that are likely to assemble through self-interaction. An array of validation experiments demonstrated that this new method can drive protein complex discovery, guide hypothesis testing, and enable systems-level analyses of protein complex dynamics in any organism with a sequenced genome.

Discovering large conserved functional components in global network alignment by graph matching

BackgroundAligning protein-protein interaction (PPI) networks is very important to discover the functionally conserved sub-structures between different species. In recent years, the global PPI network alignment problem has been extensively studied aiming at finding the one-to-one alignment with the maximum matching score. However, finding large conserved components remains challenging due to its NP-hardness.ResultsWe propose a new graph matching method GMAlign for global PPI network alignment. It first selects some pairs of important proteins as seeds, followed by a gradual expansion to obtain an initial matching, and then it refines the current result to obtain an optimal alignment result iteratively based on the vertex cover. We compare GMAlign with the state-of-the-art methods on the PPI network pairs obtained from the largest BioGRID dataset and validate its performance. The results show that our algorithm can produce larger size of alignment, and can find bigger and denser common connected subgraphs as well for the first time. Meanwhile, GMAlign can achieve high quality biological results, as measured by functional consistency and semantic similarity of the Gene Ontology terms. Moreover, we also show that GMAlign can achieve better results which are structurally and biologically meaningful in the detection of large conserved biological pathways between species.ConclusionsGMAlign is a novel global network alignment tool to discover large conserved functional components between PPI networks. It also has many potential biological applications such as conserved pathway and protein complex discovery across species. The GMAlign software and datasets are avaialbile at https://github.com/yzlwhu/GMAlign.

Discovering overlapped protein complexes from weighted PPI networks by removing inter-module hubs

Detecting known protein complexes and predicting undiscovered protein complexes from protein-protein interaction (PPI) networks help us to understand principles of cell organization and its functions. Nevertheless, the discovery of protein complexes based on experiment still needs to be explored. Therefore, computational methods are useful approaches to overcome the experimental limitations. Nevertheless, extraction of protein complexes from PPI network is often nontrivial. Two major constraints are large amount of noise and ignorance of occurrence time of different interactions in PPI network. In this paper, an efficient algorithm, Inter Module Hub Removal Clustering (IMHRC), is developed based on inter-module hub removal in the weighted PPI network which can detect overlapped complexes. By removing some of the inter-module hubs and module hubs, IMHRC eliminates high amount of noise in dataset and implicitly considers different occurrence time of the PPI in network. The performance of the IMHRC was evaluated on several benchmark datasets and results were compared with some of the state-of-the-art models. The protein complexes discovered with the IMHRC method show significantly better agreement with the real complexes than other current methods. Our algorithm provides an accurate and scalable method for detecting and predicting protein complexes from PPI networks.

Native Proteomics: A New Approach to Protein Complex Discovery and Characterization

Protein complexes play critical roles in an array of biological processes, including cancer, senescence, and cell cycle arrest. In order to perform these functions, protein complexes exhibit great diversity in protein membership, post‐translational modification and noncovalent cofactors. Given the vital functions and varied composition of these macromolecular assemblies, their identification and structural characterization is foundational to our understanding of normal and disease biology. Thus, examination of cellular products at progressively higher levels of complexity, from peptides to proteins to protein complexes, paints an increasingly complete picture of the actuators of biological systems.Current methodologies for analysis of protein complexes fall into two groups: those that provide detailed characterization of a single target and those that provide low‐level information on a large number of interactions. Examples of the former include X‐ray crystallography, fluorescence imaging, cryo‐electron microscopy, and co‐immunoprecipitation, which are lower‐throughput techniques that can require large amounts of purified and optimized samples. In contrast, the latter category includes the use of tandem mass spectrometry (MS) techniques in a peptide‐based “bottom‐up” methodology. While bottom‐up approaches can provide extensive maps of physical interactions, they often fail to determine stoichiometry or the complete modification states of complex subunits.In this contribution, we describe the first large‐scale study utilizing our new Native Proteomics platform that occupies the space between existing approaches to gain complete molecular detail from relatively small amounts of unpurified endogenous protein complexes. Native proteomics aims to directly identify and characterize proteoforms and the complexes generated from them, which we term multi proteoform complexes (MPCs). Beyond sample preparation and separation strategies that retain native protein structure, the key to Native Proteomics is a 3‐tiered tandem mass spectrometric approach. During this experiment, a single charge state of one intact MPC is isolated and activated to eject one or multiple subunits. Next, activation is performed prior to isolation and each of the ejected monomers is isolated and fragmented individually. This approach yields an intact mass for the entire protein complex, intact masses of the ejected subunits, and backbone fragment ion masses from each fragmented subunit. Thus, in a single experiment, an unknown complex may be identified by its constituent subunits, stoichiometry may be determined from the intact mass coupled with the knowledge of subunit masses, and any additional sources of mass, such as cofactors and other PTMs, can be identified.In our initial dataset, we identified and characterized 125 unique protein complexes ranging in size from 5–316 kDa. Within these complexes, 28 exhibited metal binding events, 2 of which were previously unannotated in UniProt. Further, we characterized small molecule binding events, covalent cofactors, numerous unannotated PTMs on cysteine side chains, endogenous cleavages and 2 examples of stoichiometric regulation of complex assembly. The level of molecular detail that this platform achieves on endogenous material is unparalleled and represents a new strategy that, for the first time, enables protein complexes to be the primary analytical targets in proteomics research.Support or Funding InformationFunding for this project was provided by the W. M. Keck foundation (DT061512) and Northwestern University. OSS and PFD are supported by US National Science Foundation graduate research fellowships (2014171659 and 2015210477, respectively). LFS was supported by the Chemistry of Life Processes Predoctoral training program at Northwestern University. Additional support for the maintenance of the SEMPC from the National Resource for Translational and Developmental Proteomics (GM108569) is acknowledged.

Evolutionary Graph Clustering for Protein Complex Identification.

This paper presents a graph clustering algorithm, called EGCPI, to discover protein complexes in protein-protein interaction (PPI) networks. In performing its task, EGCPI takes into consideration both network topologies and attributes of interacting proteins, both of which have been shown to be important for protein complex discovery. EGCPI formulates the problem as an optimization problem and tackles it with evolutionary clustering. Given a PPI network, EGCPI first annotates each protein with corresponding attributes that are provided in Gene Ontology database. It then adopts a similarity measure to evaluate how similar the connected proteins are taking into consideration the network topology. Given this measure, EGCPI then discovers a number of graph clusters within which proteins are densely connected, based on an evolutionary strategy. At last, EGCPI identifies protein complexes in each discovered cluster based on the homogeneity of attributes performed by pairwise proteins. EGCPI has been tested with several real data sets and the experimental results show EGCPI is very effective on protein complex discovery, and the evolutionary clustering is helpful to identify protein complexes in PPI networks. The software of EGCPI can be downloaded via: https://github.com/hetiantian1985/EGCPI.

Protein complex discovery by interaction filtering from protein interaction networks using mutual rank coexpression and sequence similarity.

The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI) network analysis. The complexes introduced by the clustering algorithms include noise proteins. The error rate of the noise proteins in the PPI network researches is about 40–90%. However, only 30–40% of the existing interactions in the PPI databases depend on the specific biological function. It is essential to eliminate the noise proteins and the interactions from the complexes created via clustering methods. We have introduced new methods of weighting interactions in protein clusters and the splicing of noise interactions and proteins-based interactions on their weights. The coexpression and the sequence similarity of each pair of proteins are considered the edge weight of the proteins in the network. The results showed that the edge filtering based on the amount of coexpression acts similar to the node filtering via graph-based characteristics. Regarding the removal of the noise edges, the edge filtering has a significant advantage over the graph-based method. The edge filtering based on the amount of sequence similarity has the ability to remove the noise proteins and the noise interactions.

Identifying protein complexes based on the integration of PPI network and gene expression data.

Identification of protein complexes is crucial to understand principles of cellular organisation and predict protein functions. In this paper, a novel protein complex discovery algorithm IPCIPG is proposed based on the integration of Protein-Protein Interaction network (PPI network) and gene expression data. IPCIPG is a local search algorithm which has two versions: IPCIPG-n for identifying non-overlapping clusters and IPCIPG-o for detecting overlapping clusters. The experimental results on the yeast PPI network show that IPCIPG can identify protein complexes with specific biological meaning more effectively, precisely and comprehensively than six other algorithms: HUNTER, HC-PIN, CMC, SPICi, MOCDE and MCL.

Pepper: cytoscape app for protein complex expansion using protein-protein interaction networks.

We introduce Pepper (Protein complex Expansion using Protein–Protein intERactions), a Cytoscape app designed to identify protein complexes as densely connected subnetworks from seed lists of proteins derived from proteomic studies. Pepper identifies connected subgraph by using multi-objective optimization involving two functions: (i) the coverage, a solution must contain as many proteins from the seed as possible, (ii) the density, the proteins of a solution must be as connected as possible, using only interactions from a proteome-wide interaction network. Comparisons based on gold standard yeast and human datasets showed Pepper’s integrative approach as superior to standard protein complex discovery methods. The visualization and interpretation of the results are facilitated by an automated post-processing pipeline based on topological analysis and data integration about the predicted complex proteins. Pepper is a user-friendly tool that can be used to analyse any list of proteins.Availability: Pepper is available from the Cytoscape plug-in manager or online (http://apps.cytoscape.org/apps/pepper) and released under GNU General Public License version 3.Contact: mohamed.elati@issb.genopole.frSupplementary information: Supplementary data are available at Bioinformatics online.

Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data

BackgroundIdentification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules.ResultsIn this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied.ConclusionsThe proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.

RAMTaB: Robust Alignment of Multi-Tag Bioimages

BackgroundIn recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.Methodology/Principal FindingsWe employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.ConclusionsFor the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.

Discovery of Protein Complexes with Core-Attachment Structures from Tandem Affinity Purification (TAP) Data

Many cellular functions involve protein complexes that are formed by multiple interacting proteins. Tandem Affinity Purification (TAP) is a popular experimental method for detecting such multi-protein interactions. However, current computational methods that predict protein complexes from TAP data require converting the co-complex relationships in TAP data into binary interactions. The resulting pairwise protein-protein interaction (PPI) network is then mined for densely connected regions that are identified as putative protein complexes. Converting the TAP data into PPI data not only introduces errors but also loses useful information about the underlying multi-protein relationships that can be exploited to detect the internal organization (i.e., core-attachment structures) of protein complexes. In this article, we propose a method called CACHET that detects protein complexes with Core-AttaCHment structures directly from bipartitETAP data. CACHET models the TAP data as a bipartite graph in which the two vertex sets are the baits and the preys, respectively. The edges between the two vertex sets represent bait-prey relationships. CACHET first focuses on detecting high-quality protein-complex cores from the bipartite graph. To minimize the effects of false positive interactions, the bait-prey relationships are indexed with reliability scores. Only non-redundant, reliable bicliques computed from the TAP bipartite graph are regarded as protein-complex cores. CACHET constructs protein complexes by including attachment proteins into the cores. We applied CACHET on large-scale TAP datasets and found that CACHET outperformed existing methods in terms of prediction accuracy (i.e., F-measure and functional homogeneity of predicted complexes). In addition, the protein complexes predicted by CACHET are equipped with core-attachment structures that provide useful biological insights into the inherent functional organization of protein complexes. Our supplementary material can be found at http://www1.i2r.a-star.edu.sg/~xlli/CACHET/CACHET.htm ; binary executables can also be found there. Supplementary Material is also available at www.liebertonline.com/cmb.

From pull-down data to protein interaction networks and complexes with biological relevance

Recent improvements in high-throughput Mass Spectrometry (MS) technology have expedited genome-wide discovery of protein-protein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of protein-protein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance. A multi-step but easy-to-follow framework for identifying protein complexes from MS pull-down data is introduced. It assesses interaction affinity between two proteins based on similarity of their co-purification patterns derived from MS data. It constructs a protein interaction network by adopting a knowledge-guided threshold selection method. Based on the network, it identifies protein complexes and infers their core components using a graph-theoretical approach. It deploys a statistical evaluation procedure to assess biological relevance of each found complex. On Saccharomyces cerevisiae pull-down data, the framework outperformed other more complicated schemes by at least 10% in F(1)-measure and identified 610 protein complexes with high-functional homogeneity based on the enrichment in Gene Ontology (GO) annotation. Manual examination of the complexes brought forward the hypotheses on cause of false identifications. Namely, co-purification of different protein complexes as mediated by a common non-protein molecule, such as DNA, might be a source of false positives. Protein identification bias in pull-down technology, such as the hydrophilic bias could result in false negatives.