Protein Function Assignment Research Articles

RNA-dependent interactome allows network-based assignment of RNA-binding protein function.

RNA-binding proteins (RBPs) form highly diverse and dynamic ribonucleoprotein complexes, whose functions determine the molecular fate of the bound RNA. In the model organism Sacchromyces cerevisiae, the number of proteins identified as RBPs has greatly increased over the last decade. However, the cellular function of most of these novel RBPs remains largely unexplored. We used mass spectrometry-based quantitative proteomics to systematically identify protein-protein interactions (PPIs) and RNA-dependent interactions (RDIs) to create a novel dataset for 40 RBPs that are associated with the mRNA life cycle. Domain, functional and pathway enrichment analyses revealed an over-representation of RNA functionalities among the enriched interactors. Using our extensive PPI and RDI networks, we revealed putative new members of RNA-associated pathways, and highlighted potential new roles for several RBPs. Our RBP interactome resource is available through an online interactive platform as a community tool to guide further in-depth functional studies and RBP network analysis (https://www.butterlab.org/RINE).

Discovery of Thermostable, Fluorescently Responsive Glucose Biosensors by Structure-Assisted Function Extrapolation.

Accurate assignment of protein function from sequence remains a fascinating and difficult challenge. The periplasmic-binding protein (PBP) superfamily present an interesting case of function prediction because they are both ubiquitous in prokaryotes and tend to diversify through gene duplication "explosions" that can lead to large numbers of paralogs in a genome. An engineered version of the moderately thermostable glucose-binding PBP from Escherichia coli has been used successfully as a reagentless fluorescent biosensor both in vitro and in vivo. To develop more robust sensors that meet the challenges of real-world applications, we report the discovery of thermostable homologues that retain a glucose-mediated conformationally coupled fluorescence response. Accurately identifying a glucose-binding PBP homologue among closely related paralogs is challenging. We demonstrate that a structure-based method that filters sequences by residues that bind glucose in an archetype structure is highly effective. Using fully sequenced bacterial genomes, we found that this filter reduced high paralog numbers to single hits in a genome, consistent with the accurate separation of glucose binding from other functions. We expressed engineered proteins for eight homologues, chosen to represent different degrees of sequence identity, and tested their glucose-mediated fluorescence responses. We accurately predicted the presence of glucose binding down to 31% sequence identity. We have also successfully identified suitable candidates for next-generation robust, fluorescent glucose sensors.

Open Issues for Protein Function Assignment in Haloferax volcanii and Other Halophilic Archaea.

Background: Annotation ambiguities and annotation errors are a general challenge in genomics. While a reliable protein function assignment can be obtained by experimental characterization, this is expensive and time-consuming, and the number of such Gold Standard Proteins (GSP) with experimental support remains very low compared to proteins annotated by sequence homology, usually through automated pipelines. Even a GSP may give a misleading assignment when used as a reference: the homolog may be close enough to support isofunctionality, but the substrate of the GSP is absent from the species being annotated. In such cases, the enzymes cannot be isofunctional. Here, we examined a variety of such issues in halophilic archaea (class Halobacteria), with a strong focus on the model haloarchaeon Haloferax volcanii. Results: Annotated proteins of Hfx. volcanii were identified for which public databases tend to assign a function that is probably incorrect. In some cases, an alternative, probably correct, function can be predicted or inferred from the available evidence, but this has not been adopted by public databases because experimental validation is lacking. In other cases, a probably invalid specific function is predicted by homology, and while there is evidence that this assigned function is unlikely, the true function remains elusive. We listed 50 of those cases, each with detailed background information, so that a conclusion about the most likely biological function can be drawn. For reasons of brevity and comprehension, only the key aspects are listed in the main text, with detailed information being provided in a corresponding section of the Supplementary Materials. Conclusions: Compiling, describing and summarizing these open annotation issues and functional predictions will benefit the scientific community in the general effort to improve the evaluation of protein function assignments and more thoroughly detail them. By highlighting the gaps and likely annotation errors currently in the databases, we hope this study will provide a framework for experimentalists to systematically confirm (or disprove) our function predictions or to uncover yet more unexpected functions.

A Randomized Clustering Forest Approach for Efficient Prediction of Protein Functions

With the advances in genetic sequencing technology, the automated assignment of protein function has become a key challenge in bioinformatics and computational biology. In nature, many kinds of proteins consist of a variety of structural domains, and each domain almost holds its own function independently or implements a new function in cooperation with neighbors. Thus, a multi-domain protein function prediction problem can be converted into multi-instance multi-label (MIML) learning tasks. In this paper, we propose a novel ensemble MIML algorithm called multi-instance multi-label randomized clustering forest (MIMLRC-Forest) for protein function prediction. In MIMLRC-Forest, we develop a set of hierarchical clustering trees and conduct a label transfer mechanism to identify the relevant function labels in learning process. The clustering tree with a hierarchical structure can handle the multi-label problem by exploiting more discriminable label concepts at higher-level nodes and by transferring less discriminable labels into the lower-level nodes. Then, the label dependency can be computed by aggregating tree labels for protein function prediction. Extensive experiments on five real-world protein data sets show the effectiveness of the proposed algorithm compared with several state-of-the-art baselines, including MIMLSVM, MIMLNN, MIML-kNN, EnMIMLNN, and M3MIML.

AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms.

The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms. We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects. http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually predicting functional properties of proteins has become increasingly cumbersome. Such large genomics data sets can only be annotated computationally. However, automated assignment of functions to unknown protein is challenging due to its inherent difficulty and complexity. Previous studies have revealed that solving problems involving complicated objects with multiple semantic meanings using the multi-instance multi-label (MIML) framework is effective. For the protein function prediction problems, each protein object in nature may associate with distinct structural units (instances) and multiple functional properties (class labels) where each unit is described by an instance and each functional property is considered as a class label. Thus, it is convenient and natural to tackle the protein function prediction problem by using the MIML framework. In this paper, we propose a sparse Markov chain-based semi-supervised MIML method, called Sparse-Markov. A sparse transductive probability graph is constructed to encode the affinity information of the data based on ensemble of Hausdorff distance metrics. Our goal is to exploit the affinity between protein objects in the sparse transductive probability graph to seek a sparse steady state probability of the Markov chain model to do protein function prediction, such that two proteins are given similar functional labels if they are close to each other in terms of an ensemble Hausdorff distance in the graph. Experimental results on seven real-world organism data sets covering three biological domains show that our proposed Sparse-Markov method is able to achieve better performance than four state-of-the-art MIML learning algorithms.

Helicobacter pyloriprotein function assignment through the 3D structure

<i>Helicobacter pylori</i>protein function assignment through the 3D structure

Localisation-based imaging of malarial antigens during erythrocyte entry reaffirms a role for AMA1 but not MTRAP in invasion.

ABSTRACTMicroscopy-based localisation of proteins during malaria parasite (Plasmodium) invasion of the erythrocyte is widely used for tentative assignment of protein function. To date, however, imaging has been limited by the rarity of invasion events and the poor resolution available, given the micron size of the parasite, which leads to a lack of quantitative measures for definitive localisation. Here, using computational image analysis we have attempted to assign relative protein localisation during invasion using wide-field deconvolution microscopy. By incorporating three-dimensional information we present a detailed assessment of known parasite effectors predicted to function during entry but as yet untested or for which data are equivocal. Our method, termed longitudinal intensity profiling, resolves confusion surrounding the localisation of apical membrane antigen 1 (AMA1) at the merozoite–erythrocyte junction and predicts that the merozoite thrombospondin-related anonymous protein (MTRAP) is unlikely to play a direct role in the mechanics of entry, an observation supported with additional biochemical evidence. This approach sets a benchmark for imaging of complex micron-scale events and cautions against simplistic interpretations of small numbers of representative images for the assignment of protein function or prioritisation of candidates as therapeutic targets.

Role of undergraduate biochemistry education in protein function assignment (618.26)

There are over 3000 unannotated structures in the PDB. Students in our labs have proposed functions for &gt;90 of these structures using template‐based alignments with the ProMOL plugin for PyMOL, BLAST, Dali and Pfam. Subsequently the students confirmed the function of four of these structures in work that they completed with minimal supervision. First they reviewed the putative enzyme function and searched the literature for an assay for that function. If the reagents were available and relatively inexpensive, they obtained the plasmid containing the gene encoding that protein, which they expressed and purified, using metal ion chromatography of the his‐tagged protein. The purified enzyme was then characterized in vitro. The process typically requires 10 ‐ 20 weeks for an individual undergraduate student working full time. We believe that this approach can be easily implemented in undergraduate biochemistry teaching labs, where it would benefit the students and the community. The students will learn about protein expression, purification and characterization using both traditional methods (electrophoresis, UV‐Visible spectroscopy) and modern approaches (plasmid expression, single step chromatographic methods). At the same time, a small army of undergraduates could make a significant dent in the number of unannotated structures in the PDB. Supported by NIGMS 2R15GM078077‐02 and 3R15GM078077‐02S1.Grant Funding Source: Supported by NIGMS 2R15GM078077‐02 and 3R15GM078077‐02S1

A novel neural response algorithm for protein function prediction

BackgroundLarge amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction.ResultsWe designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%.ConclusionsThe proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.

Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily

The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion.

Systematic Planning of Genome-Scale Experiments in Poorly Studied Species

Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms.

Bee, wasp and ant venomics pave the way for a component-resolved diagnosis of sting allergy

With the complete sequencing of its genome, the honey bee is now a preferred model organism for Hymenoptera species, also with respect to venomic studies. Major pitfalls in proteomic profiling are: i) highly abundant proteins masking low-copy number proteins; ii) high heterogeneity in proteomes due to isoforms, protease activity and PTMs; iii) the inability for protein function assignment. If genomic information is not available, proteins still might be identified through cross-species protein identifications or MS/MS data-based de novo sequencing techniques. Venomic approaches discovered several new proteins and peptides from honey bees, bumble bees, ants and different wasp species, and some of these constituents were proven to be of immunological significance. Further digging in the proteome/peptidome will yield more so-called "venom trace elements" with only a local function in the venom duct or reservoir or released by leakage of the gland tissue. An impressive list of recombinants venom proteins has become available from a diverse range of Hymenopterans. Protein microarray allows the determination and monitoring of allergic patients' IgE reactivity profiles to disease-causing allergens using single measurements and minute amounts of serum. The information the physician will get from such a single run will largely exceed the output from current IgE capturing tools using whole venom preparations.

Predicting protein function from domain content

Computational assignment of protein function may be the single most vital application of bioinformatics in the post-genome era. These assignments are made based on various protein features, where one is the presence of identifiable domains. The relationship between protein domain content and function is important to investigate, to understand how domain combinations encode complex functions. Two different models are presented on how protein domain combinations yield specific functions: one rule-based and one probabilistic. We demonstrate how these are useful for Gene Ontology annotation transfer. The first is an intuitive generalization of the Pfam2GO mapping, and detects cases of strict functional implications of sets of domains. The second uses a probabilistic model to represent the relationship between domain content and annotation terms, and was found to be better suited for incomplete training sets. We implemented these models as predictors of Gene Ontology functional annotation terms. Both predictors were more accurate than conventional best BLAST-hit annotation transfer and more sensitive than a single-domain model on a large-scale dataset. We present a number of cases where combinations of Pfam-A protein domains predict functional terms that do not follow from the individual domains. Scripts and documentation are available for download at http://sonnhammer.sbc.su.se/multipfam2go_source_docs.tar

WebPIPSA: a web server for the comparison of protein interaction properties

Protein molecular interaction fields are key determinants of protein functionality. PIPSA (Protein Interaction Property Similarity Analysis) is a procedure to compare and analyze protein molecular interaction fields, such as the electrostatic potential. PIPSA may assist in protein functional assignment, classification of proteins, the comparison of binding properties and the estimation of enzyme kinetic parameters. webPIPSA is a web server that enables the use of PIPSA to compare and analyze protein electrostatic potentials. While PIPSA can be run with downloadable software (see http://projects.eml.org/mcm/software/pipsa), webPIPSA extends and simplifies a PIPSA run. This allows non-expert users to perform PIPSA for their protein datasets. With input protein coordinates, the superposition of protein structures, as well as the computation and analysis of electrostatic potentials, is automated. The results are provided as electrostatic similarity matrices from an all-pairwise comparison of the proteins which can be subjected to clustering and visualized as epograms (tree-like diagrams showing electrostatic potential differences) or heat maps. webPIPSA is freely available at: http://pipsa.eml.org.

Protein Function Assignment through Mining Cross-Species Protein-Protein Interactions

BackgroundAs we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data.Methodology/Principal FindingsThe proposed method is assessed both biologically and statistically over the genome of H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete.Conclusions/SignificanceIt can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable to a genome-wide application.

Assignment of protein function and discovery of novel nucleolar proteins based on automatic analysis of MEDLINE

Attribution of the most probable functions to proteins identified by proteomics is a significant challenge that requires extensive literature analysis. We have developed a system for automated prediction of implicit and explicit biologically meaningful functions for a proteomics study of the nucleolus. This approach uses a set of vocabulary terms to map and integrate the information from the entire MEDLINE database. Based on a combination of cross-species sequence homology searches and the corresponding literature, our approach facilitated the direct association between sequence data and information from biological texts describing function. Comparison of our automated functional assignment to manual annotation demonstrated our method to be highly effective. To establish the sensitivity, we defined the functional subtleties within a family containing a highly conserved sequence. Clustering of the DEAD-box protein family of RNA helicases confirmed that these proteins shared similar morphology although functional subfamilies were accurately identified by our approach. We visualized the nucleolar proteome in terms of protein functions using multi-dimensional scaling, showing functional associations between nucleolar proteins that were not previously realized. Finally, by clustering the functional properties of the established nucleolar proteins, we predicted novel nucleolar proteins. Subsequently, nonproteomics studies confirmed the predictions of previously unidentified nucleolar proteins.

A Chemical Proteomics Approach for the Identification of Accessible Antigens Expressed in Human Kidney Cancer

A promising avenue toward the development of more selective anticancer drugs consists in the targeted delivery of bioactive molecules to the tumor environment by means of binding molecules specific to tumor-associated markers. We have used a chemical proteomics approach based on the ex vivo perfusion and biotinylation of accessible structures within surgically resected human kidneys with tumor to gain information about accessible and abundant antigens that are overexpressed in human cancer. This procedure led to the selective labeling with biotin of vascular structures. Biotinylated proteins were purified on streptavidin resin and identified using mass spectrometric methodologies, revealing 637 proteins, 184 of which were only found in tumor specimens and 223 of which were only found in portions of normal kidneys. Immunohistochemical and PCR analysis confirmed that several of the putative cancer antigens identified in this study are indeed preferentially expressed in tumors. In conclusion, we have developed a methodology that allows the identification of accessible biomarkers in human tissues. The tumor-associated antigens identified in this study may be suitable targets for antibody-based anticancer therapies. The experimental approach described here should be applicable to other surgical specimens and to other pathologies as well as to the study of basic physiological and immunological processes.

Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm

Motivation Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein–protein interaction networks, Nat. Biotechnol., 21, 697–700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction. Conclusion On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO’s improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.

Probabilistic annotation of protein sequences based on functional classifications

BackgroundOne of the most evident achievements of bioinformatics is the development of methods that transfer biological knowledge from characterised proteins to uncharacterised sequences. This mode of protein function assignment is mostly based on the detection of sequence similarity and the premise that functional properties are conserved during evolution. Most automatic approaches developed to date rely on the identification of clusters of homologous proteins and the mapping of new proteins onto these clusters, which are expected to share functional characteristics.ResultsHere, we inverse the logic of this process, by considering the mapping of sequences directly to a functional classification instead of mapping functions to a sequence clustering. In this mode, the starting point is a database of labelled proteins according to a functional classification scheme, and the subsequent use of sequence similarity allows defining the membership of new proteins to these functional classes. In this framework, we define the Correspondence Indicators as measures of relationship between sequence and function and further formulate two Bayesian approaches to estimate the probability for a sequence of unknown function to belong to a functional class. This approach allows the parametrisation of different sequence search strategies and provides a direct measure of annotation error rates. We validate this approach with a database of enzymes labelled by their corresponding four-digit EC numbers and analyse specific cases.ConclusionThe performance of this method is significantly higher than the simple strategy consisting in transferring the annotation from the highest scoring BLAST match and is expected to find applications in automated functional annotation pipelines.