PRIN: a predicted rice interactome network

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in <it>Oryza sativa</it>.</p> <p>Results</p> <p>To better understand the interactions of proteins in <it>Oryza sativa</it>, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (<it>Saccharomyces cerevisiae</it>), worm (<it>Caenorhabditis elegans</it>), fruit fly (<it>Drosophila melanogaster</it>), human (<it>Homo sapiens</it>), <it>Escherichia coli </it>K12 and <it>Arabidopsis thaliana</it>. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization.</p> <p>Conclusions</p> <p>PRIN is the first well annotated protein interaction database for the important model plant <it>Oryza sativa</it>. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology.</p> <p>PRIN is available online at <url>http://bis.zju.edu.cn/prin/</url>.</p

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

The effect of organelle discovery upon sub-cellular protein localisation.

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein–organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein–organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein–organelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein–organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein–organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein–organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction

A new pairwise kernel for biological network inference with support vector machines

International audienceBACKGROUND: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). RESULTS: Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. CONCLUSION: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data

Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

Protein-protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter-wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets

Statistical Relational Learning for Proteomics: Function, Interactions and Evolution

In recent years, the field of Statistical Relational Learning (SRL) [1, 2] has produced new, powerful learning methods that are explicitly designed to solve complex problems, such as collective classification, multi-task learning and structured output prediction, which natively handle relational data, noise, and partial information. Statistical-relational methods rely on some First- Order Logic as a general, expressive formal language to encode both the data instances and the relations or constraints between them. The latter encode background knowledge on the problem domain, and are use to restrict or bias the model search space according to the instructions of domain experts. The new tools developed within SRL allow to revisit old computational biology problems in a less ad hoc fashion, and to tackle novel, more complex ones. Motivated by these developments, in this thesis we describe and discuss the application of SRL to three important biological problems, highlighting the advantages, discussing the trade-offs, and pointing out the open problems. In particular, in Chapter 3 we show how to jointly improve the outputs of multiple correlated predictors of protein features by means of a very gen- eral probabilistic-logical consistency layer. The logical layer — based on grounding-specific Markov Logic networks [3] — enforces a set of weighted first-order rules encoding biologically motivated constraints between the pre- dictions. The refiner then improves the raw predictions so that they least violate the constraints. Contrary to canonical methods for the prediction of protein features, which typically take predicted correlated features as in- puts to improve the output post facto, our method can jointly refine all predictions together, with potential gains in overall consistency. In order to showcase our method, we integrate three stand-alone predictors of corre- lated features, namely subcellular localization (Loctree[4]), disulfide bonding state (Disulfind[5]), and metal bonding state (MetalDetector[6]), in a way that takes into account the respective strengths and weaknesses. The ex- perimental results show that the refiner can improve the performance of the underlying predictors by removing rule violations. In addition, the proposed method is fully general, and could in principle be applied to an array of heterogeneous predictions without requiring any change to the underlying software. In Chapter 4 we consider the multi-level protein–protein interaction (PPI) prediction problem. In general, PPIs can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detailed knowl- edge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the bind- ing process and more efficient drug/enzyme design. We cast the prediction problem in terms of multi-task learning, with one task per level (proteins, domains and residues), and propose a machine learning method that collec- tively infers the binding state of all object pairs, at all levels, concurrently. Our method is based on Semantic Based Regularization (SBR) [7], a flexible and theoretically sound SRL framework that employs First-Order Logic con- straints to tie the learning tasks together. Contrarily to most current PPI prediction methods, which neither identify which regions of a protein actu- ally instantiate an interaction nor leverage the hierarchy of predictions, our method resolves the prediction problem up to residue level, enforcing con- sistent predictions between the hierarchy levels, and fruitfully exploits the hierarchical nature of the problem. We present numerical results showing that our method substantially outperforms the baseline in several experi- mental settings, indicating that our multi-level formulation can indeed lead to better predictions. Finally, in Chapter 5 we consider the problem of predicting drug-resistant protein mutations through a combination of Inductive Logic Programming [8, 9] and Statistical Relational Learning. In particular, we focus on viral pro- teins: viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants. We propose a simple approach for mutant prediction where the in- put consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance, and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones. Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The ap- proach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations

Translational control by the multi-KH domain protein Scp160

The control of mRNA translation mediated by RNA-binding proteins (RBPs) is a key player in modulating gene expression. In S. cerevisiae, the multi-KH domain protein Scp160 associates with a large number of mRNAs and is present on membrane-bound and, to a lesser extent, cytosolic polysomes. Its binding site on the ribosome is close to the mRNA exit tunnel and in vicinity to Asc1, which constitutes a binding platform for signaling molecules. The present study focused on the closer characterization of the Scp160-ribosome interaction and on the suggested function of Scp160 in modulating the translation of specific target mRNAs. Using affinity purifications, the partial RNA-dependence of the Scp160-ribosome association was confirmed. In contrast to published results, ribosome association was found to be only slightly reduced but not abolished in the absence of Asc1 or the last two KH domains. Furthermore, the putative elongation regulator Stm1 was identified as a co-purifier of Scp160. In subcellular fractionation experiments, RNA-binding mutants of Scp160 were present in the ribosome-free cytosolic fraction and therefore partially deficient in ribosome association and/or mRNP formation. However, no physiological conditions were found that equally induce a shift of wildtype Scp160 towards the cytosolic fraction. Within the scope of a translational profiling approach, microarray analyses of RNA isolated from sucrose density gradient fractions were performed and led to the identification of a set of mRNAs that shift their position within the gradients upon Scp160 depletion, indicating changes in their translation rates. Consistent with the membrane localization of Scp160, transcripts encoding secreted proteins were significantly enriched. Using immunoprecipitation and subsequent quantitative real-time PCR (qRT-PCR), the interaction of Scp160 with a subgroup of the identified targets was confirmed and it was shown that their binding is dependent on the conserved GXXG motifs in the two C-terminal KH domains of Scp160. Furthermore, data were obtained indicating that Scp160 can act as a translational activator on some of its target mRNAs, probably on the level of translation elongation. Finally, first evidence was provided that the translational misregulation of specific target transcripts may be involved in the polyploidization that is a hallmark of Scp160-deprived cells. In summary, these data substantiate the assumption that Scp160 is involved in translational regulation of a specific, functionally related subset of mRNAs. This finding is in good accordance with the emerging view that RBPs co-regulate multiple transcripts in order to allow faster adaptation to environmental changes

Characterisation of lnc-G4, a long noncoding RNA that regulates skeletal muscle differentiation through translational repression of G-quadruplex containing mRNAs

Long non-coding RNAs are crucial regulators of the fine tuning of gene expression. Their role has been widely studied especially in developmental processes such as skeletal muscle differentiation. In particular, a novel cytoplasmic long non-coding RNA, called lnc- G4, has a relevant role in promoting murine C2C12 myoblast differentiation. The analysis of the interactors of this long non- coding RNA showed its ability to base-pair with many mRNAs thanks to a repeated element embedded in its sequence; among the interactors, we focused on MLX mRNA, which encodes for a myogenic transcription factor. We demonstrated that lnc-G4 directly interacts with the three splicing isoforms of MLX mRNA, while it is able to specifically inhibit the translation of only MLX g isoform; this translational regulation could depend on the recruitment of the RNA helicase DHX36. Interestingly, the effect of lnc-G4 on MLX g regulates the subcellular localization of the other isoforms, and this has an impact on the transcriptional activation of MLX targets. Taken together, these evidences suggest that lnc-G4 could be a key factor in post-transcriptional gene regulation during the early phases of myogenesis through the translational regulation of MLX g. Moreover, lnc-G4 interacts with other mRNAs, and the regulation mechanism could be extended to many other targets