Lipids and lipid domains in the peroxisomal membrane of the yeast Yarrowia lipolytica

Biological membranes have unique and highly diverse compositions of their lipid constituents. At present, we have only partial understanding of how membrane lipids and lipid domains regulate the structural integrity and functionality of cellular organelles, maintain the unique molecular composition of each organellar membrane by orchestrating the intracellular trafficking of membrane-bound proteins and lipids, and control the steady-state levels of numerous signaling molecules generated in biological membranes. Similar to other organellar membranes, a single lipid bilayer enclosing the peroxisome, an organelle known for its essential role in lipid metabolism, has a unique lipid composition and organizes some of its lipid and protein components into distinctive assemblies. This review highlights recent advances in our knowledge of how lipids and lipid domains of the peroxisomal membrane regulate the processes of peroxisome assembly and maintenance in the yeast Yarrowia lipolytica. We critically evaluate the molecular mechanisms through which lipid constituents of the peroxisomal membrane control these multistep processes and outline directions for future research in this field.     

An FPT approach for predicting protein localization from yeast genomic data

Accurately predicting the localization of proteins is of paramount importance in the quest to determine their respective functions within the cellular compartment. Because of the continuous and rapid progress in the fields of genomics and proteomics, more data are available now than ever before. Coincidentally, data mining methods been developed and refined in order to handle this experimental windfall, thus allowing the scientific community to quantitatively address long-standing questions such as that of protein localization. Here, we develop a frequent pattern tree (FPT) approach to generate a minimum set of rules (mFPT) for predicting protein localization. We acquire a series of rules according to the features of yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regression under various statistical measures. Our results show that mFPT gave better performance than other approaches in predicting protein localization. Meanwhile, setting 0.65 as the minimum hit-rate, we obtained 138 proteins that mFPT predicted differently than the simple naive bayesian method (SNB). In our analysis of these 138 proteins, we present novel predictions for the location for 17 proteins, which currently do not have any defined localization. These predictions can serve as putative annotations and should provide preliminary clues for experimentalists. We also compared our predictions against the eukaryotic subcellular localization database and related predictions by others on protein localization. Our method is quite generalized and can thus be applied to discover the underlying rules for protein-protein interactions, genomic interactions, and structure-function relationships, as well as those of other fields of research.     

Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species

The function of a protein is intimately tied to its subcellular localization. Although localizations have been measured for many yeast proteins through systematic GFP fusions, similar studies in other branches of life are still forthcoming. In the interim, various machine-learning methods have been proposed to predict localization using physical characteristics of a protein, such as amino acid content, hydrophobicity, side-chain mass and domain composition. However, there has been comparatively little work on predicting localization using protein networks. Here, we predict protein localizations by integrating an extensive set of protein physical characteristics over a protein's extended protein-protein interaction neighborhood, using a classification framework called 'Divide and Conquer k-Nearest Neighbors' (DC-kNN). These predictions achieve significantly higher accuracy than two well-known methods for predicting protein localization in yeast. Using new GFP imaging experiments, we show that the network-based approach can extend and revise previous annotations made from high-throughput studies. Finally, we show that our approach remains highly predictive in higher eukaryotes such as fly and human, in which most localizations are unknown and the protein network coverage is less substantial.     

Novel search method for the discovery of functional relationships

MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.     

The binary protein interactome of Treponema pallidum--the syphilis spirochete

Protein interaction networks shed light on the global organization of proteomes but can also place individual proteins into a functional context. If we know the function of bacterial proteins we will be able to understand how these species have adapted to diverse environments including many extreme habitats. Here we present the protein interaction network for the syphilis spirochete Treponema pallidum which encodes 1,039 proteins, 726 (or 70%) of which interact via 3,649 interactions as revealed by systematic yeast two-hybrid screens. A high-confidence subset of 991 interactions links 576 proteins. To derive further biological insights from our data, we constructed an integrated network of proteins involved in DNA metabolism. Combining our data with additional evidences, we provide improved annotations for at least 18 proteins (including TP0004, TP0050, and TP0183 which are suggested to be involved in DNA metabolism). We estimate that this "minimal" bacterium contains on the order of 3,000 protein interactions. Profiles of functional interconnections indicate that bacterial proteins interact more promiscuously than eukaryotic proteins, reflecting the non-compartmentalized structure of the bacterial cell. Using our high-confidence interactions, we also predict 417,329 homologous interactions ("interologs") for 372 completely sequenced genomes and provide evidence that at least one third of them can be experimentally confirmed.     

Deciphering protein complexes and protein interaction networks by tandem affinity purification and mass spectrometry: analytical perspective

We employed a combination of tandem affinity purification and mass spectrometry for deciphering protein complexes and the protein interaction network in budding yeast. 53 genes were epitope-tagged, and their interaction partners were isolated by two-step immunoaffinity chromatography from whole cell lysates. 38 baits pulled down a total of 220 interaction partners, which are members of 19 functionally distinct protein complexes. We identified four proteins shared between complexes of different functionality thus charting segments of a protein interaction network. Concordance with the results of genome-wide two-hybrid screening was poor (14% of identified interactors overlapped) suggesting that the two approaches may provide complementary views on physical interactions within the proteome.     

A Bayesian system integrating expression data with sequence patterns for localizing proteins: comprehensive application to the yeast genome

We develop a probabilistic system for predicting the subcellular localization of proteins and estimating the relative population of the various compartments in yeast. Our system employs a Bayesian approach, updating a protein's probability of being in a compartment, based on a diverse range of 30 features. These range from specific motifs (e.g. signal sequences or the HDEL motif) to overall properties of a sequence (e.g. surface composition or isoelectric point) to whole-genome data (e.g. absolute mRNA expression levels or their fluctuations). The strength of our approach is the easy integration of many features, particularly the whole-genome expression data. We construct a training and testing set of approximately 1300 yeast proteins with an experimentally known localization from merging, filtering, and standardizing the annotation in the MIPS, Swiss-Prot and YPD databases, and we achieve 75 % accuracy on individual protein predictions using this dataset. Moreover, we are able to estimate the relative protein population of the various compartments without requiring a definite localization for every protein. This approach, which is based on an analogy to formalism in quantum mechanics, gives better accuracy in determining relative compartment populations than that obtained by simply tallying the localization predictions for individual proteins (on the yeast proteins with known localization, 92% versus 74%). Our training and testing also highlights which of the 30 features are informative and which are redundant (19 being particularly useful). After developing our system, we apply it to the 4700 yeast proteins with currently unknown localization and estimate the relative population of the various compartments in the entire yeast genome. An unbiased prior is essential to this extrapolated estimate; for this, we use the MIPS localization catalogue, and adapt recent results on the localization of yeast proteins obtained by Snyder and colleagues using a minitransposon system. Our final localizations for all approximately 6000 proteins in the yeast genome are available over the web at: http://bioinfo.mbb.yale. edu/genome/localize.     

Generation of transgenic Arabidopsis plants expressing mcherry-fused organelle marker proteins

In eukaryotic cells, a major proportion of the cellular proteins localize to various subcellular organelles where they are involved in organelle-specific cellular processes. Thus, the localization of a particular protein in the cell is an important part of understanding the physiological role of the protein in the cell. Various approaches such as subcellular fractionation, immunolocalization and live imaging have been used to define the localization of organellar proteins. Of these various approaches, the most powerful one is the live imaging because it can show in vivo dynamics of protein localization depending on cellular and environmental conditions without disturbing cellular structures. However, the live imaging requires the ability to detect the organelles in live cells. In this study, we report generation of a new set of transgenic Arabidopsis plants using various organelle marker proteins fused to a fluorescence protein, monomeric Cherry (mCherry). All these markers representing different subcellular organelles such as chloroplasts, mitochondria, peroxisomes, endoplasmic reticulum (ER) and lytic vacuole showed clear and specific signals regardless of the cell types and tissues. These marker lines can be used to determine localization of organellar proteins by colocalization and also to study the dynamics of organelles under various developmental and environmental conditions.     

Characterization of the Drosophila lipid droplet subproteome

Lipid storage droplets are universal organelles essential for the cellular and organismal lipometabolism including energy homeostasis. Despite their apparently simple design they are proposed to participate in a growing number of cellular processes, raising the question to what extent the functional multifariousness is reflected by a complex organellar proteome composition. Here we present 248 proteins identified in a subproteome analysis using lipid storage droplets of Drosophila melanogaster fat body tissue. In addition to previously known lipid droplet-associated PAT (Perilipin, ADRP, and TIP47) domain proteins and homologues of several mammalian lipid droplet proteins, this study identified a number of proteins of diverse biological function, including intracellular trafficking supportive of the dynamic and multifaceted character of these organelles. We performed intracellular localization studies on selected newly identified subproteome members both in tissue culture cells and in fat body cells directly. The results suggest that the lipid droplets of fat body cells are of combinatorial protein composition. We propose that subsets of lipid droplets within single cells are characterized by a protein "zip code," which reflects functional differences or specific metabolic states.     

Integrative analysis of the mitochondrial proteome in yeast

In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.     

Functional annotation from predicted protein interaction networks

MOTIVATION: Progress in large-scale experimental determination of protein-protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes. RESULTS: We evaluated the performance of a network-based functional annotation method that makes use of our predicted protein interaction networks. We show that this approach performs equally well on experimentally derived and predicted interaction networks, for both manually and computationally assigned annotations. We applied the method to predicted protein interaction networks for over 50 organisms from all domains of life, providing annotations for many previously unannotated proteins and verifying existing low-confidence annotations. AVAILABILITY: Functional predictions for over 50 organisms are available at http://bioverse.compbio.washington.edu and datasets used for analysis at http://data.compbio.washington.edu/misc/downloads/nannotation_data/. SUPPLEMENTARY INFORMATION: A supplemental appendix gives additional details not in the main text. (http://data.compbio.washington.edu/misc/downloads/nannotation_data/supplement.pdf).     

ADP-ribosylation factor (ARF) interaction is not sufficient for yeast GGA protein function or localization

Golgi-localized gamma-ear homology domain, ADP-ribosylation factor (ARF)-binding proteins (GGAs) facilitate distinct steps of post-Golgi traffic. Human and yeast GGA proteins are only ~25% identical, but all GGA proteins have four similar domains based on function and sequence homology. GGA proteins are most conserved in the region that interacts with ARF proteins. To analyze the role of ARF in GGA protein localization and function, we performed mutational analyses of both human and yeast GGAs. To our surprise, yeast and human GGAs differ in their requirement for ARF interaction. We describe a point mutation in both yeast and mammalian GGA proteins that eliminates binding to ARFs. In mammalian cells, this mutation disrupts the localization of human GGA proteins. Yeast Gga function was studied using an assay for carboxypeptidase Y missorting and synthetic temperature-sensitive lethality between GGAs and VPS27. Based on these assays, we conclude that non-Arf-binding yeast Gga mutants can function normally in membrane trafficking. Using green fluorescent protein-tagged Gga1p, we show that Arf interaction is not required for Gga localization to the Golgi. Truncation analysis of Gga1p and Gga2p suggests that the N-terminal VHS domain and C-terminal hinge and ear domains play significant roles in yeast Gga protein localization and function. Together, our data suggest that yeast Gga proteins function to assemble a protein complex at the late Golgi to initiate proper sorting and transport of specific cargo. Whereas mammalian GGAs must interact with ARF to localize to and function at the Golgi, interaction between yeast Ggas and Arf plays a minor role in Gga localization and function.     

Identification of core-attachment complexes based on maximal frequent patterns in protein-protein interaction networks

In this paper, we present a method for core-attachment complexes identification based on maximal frequent patterns (CCiMFP) in yeast protein-protein interaction (PPI) networks. First, we detect subgraphs with high degree as candidate protein cores by mining maximal frequent patterns. Then using topological and functional similarities, we combine highly similar protein cores and filter insignificant ones. Finally, the core-attachment complexes are formed by adding attachment proteins to each significant core. We experimentally evaluate the performance of our method CCiMFP on yeast PPI networks. Using gold standard sets of protein complexes, Gene Ontology (GO), and localization annotations, we show that our method gains an improvement over the previous algorithms in terms of precision, recall, and biological significance of the predicted complexes. The colocalization scores of our predicted complex sets are higher than those of two known complex sets. Moreover, our method can detect GO-enriched complexes with disconnected cores compared with other methods based on the subgraph connectivity.     

Genome-wide identification of Arabidopsis coiled-coil proteins and establishment of the ARABI-COIL database

Increasing evidence demonstrates the importance of long coiled-coil proteins for the spatial organization of cellular processes. Although several protein classes with long coiled-coil domains have been studied in animals and yeast, our knowledge about plant long coiled-coil proteins is very limited. The repeat nature of the coiled-coil sequence motif often prevents the simple identification of homologs of animal coiled-coil proteins by generic sequence similarity searches. As a consequence, counterparts of many animal proteins with long coiled-coil domains, like lamins, golgins, or microtubule organization center components, have not been identified yet in plants. Here, all Arabidopsis proteins predicted to contain long stretches of coiled-coil domains were identified by applying the algorithm MultiCoil to a genome-wide screen. A searchable protein database, ARABI-COIL (http://www.coiled-coil.org/arabidopsis), was established that integrates information on number, size, and position of predicted coiled-coil domains with subcellular localization signals, transmembrane domains, and available functional annotations. ARABI-COIL serves as a tool to sort and browse Arabidopsis long coiled-coil proteins to facilitate the identification and selection of candidate proteins of potential interest for specific research areas. Using the database, candidate proteins were identified for Arabidopsis membrane-bound, nuclear, and organellar long coiled-coil proteins.     

Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae

Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.     

Comparing protein interaction networks via a graph match-and-split algorithm.

We present a method that compares the protein interaction networks of two species to detect functionally similar (conserved) protein modules between them. The method is based on an algorithm we developed to identify matching subgraphs between two graphs. Unlike previous network comparison methods, our algorithm has provable guarantees on correctness and efficiency. Our algorithm framework also admits quite general criteria that define when two subgraphs match and constitute a conserved module. We apply our method to pairwise comparisons of the yeast protein network with the human, fruit fly and nematode worm protein networks, using a lenient criterion based on connectedness and matching edges, coupled with a clustering heuristic. In evaluations of the detected conserved modules against reference yeast protein complexes, our method performs competitively with and sometimes better than two previous network comparison methods. Further, under some conditions (proper homolog and species selection), our method performs better than a popular single-species clustering method. Beyond these evaluations, we discuss the biology of a couple of conserved modules detected by our method. We demonstrate the utility of network comparison for transferring annotations from yeast proteins to human ones, and validate the predicted annotations. Supplemental text is available at www.cs.berkeley.edu/ approximately nmani/M-and-S/supplement.pdf.     

Immunoisolation and subfractionation of synaptic vesicle proteins

Within recent years, the advances in proteomics techniques have resulted in considerable novel insights into the protein expression patterns of specific tissues, cells, and organelles. The information acquired from large-scale proteomics approaches indicated, however, that the proteomic analysis of whole cells or tissues is often not suited to fully unravel the proteomes of individual organellar constituents or to identify proteins that are present at low copy numbers. In addition, the identification of hydrophobic proteins is still a challenge. Therefore, the development of techniques applicable for the enrichment of low-abundance membrane proteins is essential for a comprehensive proteomic analysis. In addition to the enrichment of particular subcellular structures by subcellular fractionation, the spectrum of techniques applicable for proteomics research can be extended toward the separation of integral and peripheral membrane proteins using organic solvents, detergents, and detergent-based aqueous two-phase systems with water-soluble polymers. Here, we discuss the efficacy of a number of experimental protocols. We demonstrate that the appropriate selection of physicochemical conditions results in the isolation of synaptic vesicles of high purity whose proteome can be subfractionated into integral membrane proteins and soluble proteins by several phase separation techniques.