Searching for protein signatures using a multilevel alphabet

Short motifs are known to play diverse roles in proteins, such as in mediating the interactions with other molecules, binding to membranes, or conducting a specific biological function. Standard approaches currently employed to detect short motifs in proteins search for enrichment of amino acid motifs considering mostly the sequence information. Here, we presented a new approach to search for common motifs (protein signatures) which share both physicochemical and structural properties, looking simultaneously at different features. Our method takes as an input an amino acid sequence and translates it to a new alphabet that reflects its intrinsic structural and chemical properties. Using the MEME search algorithm, we identified the proteins signatures within subsets of protein which encompass common sequence and structural information. We demonstrated that we can detect enriched structural motifs, such as the amphipathic helix, from large datasets of linear sequences, as well as predicting common structural properties (such as disorder, surface accessibility, or secondary structures) of known functional‐motifs. Finally, we applied the method to the yeast protein interactome and identified novel putative interacting motifs. We propose that our approach can be applied for de novo protein function prediction given either sequence or structural information. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Discriminative motif finding for predicting protein subcellular localization

Many methods have been described to predict the subcellular location of proteins from sequence information. However, most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify potential annotation errors in public databases for the location of some of the proteins. A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/.     

Using RNA secondary structures to guide sequence motif finding towards single-stranded regions

RNA binding proteins recognize RNA targets in a sequence specific manner. Apart from the sequence, the secondary structure context of the binding site also affects the binding affinity. Binding sites are often located in single-stranded RNA regions and it was shown that the sequestration of a binding motif in a double-strand abolishes protein binding. Thus, it is desirable to include knowledge about RNA secondary structures when searching for the binding motif of a protein. We present the approach MEMERIS for searching sequence motifs in a set of RNA sequences and simultaneously integrating information about secondary structures. To abstract from specific structural elements, we precompute position-specific values measuring the single-strandedness of all substrings of an RNA sequence. These values are used as prior knowledge about the motif starts to guide the motif search. Extensive tests with artificial and biological data demonstrate that MEMERIS is able to identify motifs in single-stranded regions even if a stronger motif located in double-strand parts exists. The discovered motif occurrences in biological datasets mostly coincide with known protein-binding sites. This algorithm can be used for finding the binding motif of single-stranded RNA-binding proteins in SELEX or other biological sequence data.     

Identification of the subcellular localization of mycobacterial proteins using localization motifs

Mycobacterium, the most common disease-causing genus, infects billions of people and is notoriously difficult to treat. Understanding the subcellular localization of mycobacterial proteins can provide essential clues for protein function and drug discovery. In this article, we present a novel approach that focuses on local sequence information to identify localization motifs that are generated by a merging algorithm and are selected based on a binomially distributed model. These localization motifs are employed as features for identifying the subcellular localization of mycobacterial proteins. Our approach provides more accurate results than previous methods and was tested on an independent dataset recently obtained from an experimental study to provide a first and reasonably accurate prediction of subcellular localization. Our approach can also be used for large-scale prediction of new protein entries in the UniportKB database and of protein sequences obtained experimentally. In addition, our approach identified many local motifs involved with the subcellular localization that also interact with the environment. Thus, our method may have widespread applications both in the study of the functions of mycobacterial proteins and in the search for a potential vaccine target for designing drugs.     

Occurrence of six-amino-acid motifs in three eukaryotic proteomes

Currently 18 hereditary neurological diseases are known to be associated with such mutations as multiple insertions of a single amino acid into the protein sequence. Therefore, investigation of the functional purpose of simple amino acid motifs becomes an important biological task. In this work, we studied the frequencies of motifs consisting of six identical amino acids and of simple six-amino-acid motifs consisting of two randomly located amino acids. The investigation was conducted on three eukaryotic proteomes of the well-studied model organisms, Homo sapiens, Drosophila melanogaster, and Caenorhabditis elegans. We showed that many simple motifs occurred very frequently; the data on the frequency were presented at These results suggest such motifs to be responsible for common functions of non-homologous and unrelated proteins in different organisms.     

Learning cellular sorting pathways using protein interactions and sequence motifs

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/.     

Beyond KDEL: the role of positions 5 and 6 in determining ER localization

The C-terminal amino acid sequence of a protein plays an important role in determining the endoplasmic reticulum (ER) localization of many soluble proteins that enter the secretory pathway. While it is known that the four amino acids found at the extreme C-terminus of the protein (e.g., KDEL) play a critical role in the interaction with the receptors that mediate retrograde transport back to the ER, other factors within the protein are less well known. Here we show that positions − 5 and − 6 play an important role in determining the ER localization of soluble proteins, with the amino acids at these positions playing an essential role in ER localization of the human protein disulfide isomerase family member, ERp18. Three other naturally occurring C-terminal motifs were also found that work efficiently in ER localization as six-amino-acid variants, but inefficiently as the four-amino-acid variant. Using bimolecular fluorescence complementation, we demonstrate that positions − 5 and − 6 from the C-terminus of the protein play an important role in the recognition of KDEL-like ER retrieval motifs, with the three different human KDEL receptors showing different specificities for changes at these positions for both inefficient and efficient ER localization four-amino-acid motifs.     

A correlated motif approach for finding short linear motifs from protein interaction networks

Background  

An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.     

Scaffold/matrix attachment regions and intrinsic DNA curvature

Recent approaches have failed to detect nucleotide sequence motifs in Scaffold/Matrix Attachment Regions (S/MARs). The lack of any known motifs, together with the confirmation that some S/MARs are not associated to any peculiar sequence, indicates that some structural elements, such as DNA curvature, have a role in chromatin organization and on their efficiency in protein binding. Similar to DNA curvature, S/MARs are located close to promoters, replication origins, and multiple nuclear processes like recombination and breakpoint sites. The chromatin structure in these regulatory regions is important to chromosome organization for accurate regulation of nuclear processes. In this article we review the biological importance of the co-localization between bent DNA sites and S/MARs. Published in Russian in Biokhimiya, 2006, Vol. 71, No. 5, pp. 598–606.     

Comparative genomics of elastin: Sequence analysis of a highly repetitive protein.

Due to the low complexity associated with their sequences, uncovering the evolutionary and functional relationships in highly repetitive proteins such as elastin, spider silks, resilin and abductin represents a significant challenge. Using the polymeric extracellular protein elastin as a model system, we present a novel computational approach to the study of sequence, function and evolutionary relationships in repetitive proteins. To address the absence of accurate sequence annotation for repetitive proteins such as elastin, we have constructed a new database repository, ElastoDB (http://theileria.ccb.sickkids.ca/elastin), dedicated to the storage and retrieval of elastin sequence- and meta-data. To analyse their sequence relationships we have devised an innovative new method, based on the identification of overrepresented 'fuzzy' motifs. Applying this method to elastin sequences derived from mammals, chicken, Xenopus and zebrafish resulted in the identification of both highly conserved, and taxon and species specific motifs that likely represent important functional and/or structural elements. The relative spacing and organization of these elements suggest that exon duplication events have played an important role in the evolution of elastin. Clustering of similarity profiles generated for sets of exons and introns, revealed a pattern of putative duplication events involving exons 15-30 in mammalian and chicken elastins, exons 20-31 in both zebrafish elastins, exons 15-20 in fugu elastin and exons 35-50 in Xenopus elastin 1. The success of this approach for elastin offers a promising route to the elucidation of sequence, structure, function and evolutionary relationships for many other proteins with sequences of low complexity.     

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.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

Systematic discovery of new recognition peptides mediating protein interaction networks

Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or “linear motif” (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a K_D of 22 μM and a VxxxRxYS motif that binds Translin with a K_D of 43 μM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.     

N‐terminal interaction domain implicates PAK4 in translational regulation and reveals novel cellular localization signals

The serine/threonine kinase PAK4 is a Rho GTPases effector protein implicated in many critical biological processes, including regulation of cell morphology and motility, embryonic development, cell survival, response to infection, and oncogenic transformation. Consistently with its pro‐oncogenic features, PAK4 was found to be overexpressed in many cancer cell lines and tissues, and to be necessary to promote activation of survival pathways. PAK4, like other Paks, is now considered a promising target for specific therapy. Little is known on its modes of regulation, molecular partners, and substrates. Because the N‐terminal regulatory moiety plays important roles in PAK4 activity and functions, even independently of GTPase interactions, in this study we employed an affinity chromatography approach to identify N‐terminal domain binding partners. Within this protein region we identified a novel interaction domain involved in association with ribonucleoprotein (RNP) complexes, suggesting PAK4 implications in translational regulation. Indeed, we found that active PAK4 can affect (cap‐independent) translation from specific IRES sequences in vivo, and that the N‐terminal domain is critical for this regulation. Further, we could establish that within the RNP interacting sequence PAK4 regulatory domain contains targeting elements that drive cytoplasmic localization and act as nuclear export signal. Functional implication of endogenous PAK4 protein, which was found in both cytoplasmic and nuclear fractions, in IRES‐mediated translation further underlines the significance of the reported findings. Our data reveal novel means for PAK4 regulation of gene expression, and provide new elements to understand the molecular mechanisms that determine PAK4 cellular localization and functions. J. Cell. Physiol. 224: 722–733, 2010. © 2010 Wiley‐Liss, Inc.     

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.     

Efficient motif search in ranked lists and applications to variable gap motifs

Sequence elements, at all levels-DNA, RNA and protein, play a central role in mediating molecular recognition and thereby molecular regulation and signaling. Studies that focus on -measuring and investigating sequence-based recognition make use of statistical and computational tools, including approaches to searching sequence motifs. State-of-the-art motif searching tools are limited in their coverage and ability to address large motif spaces. We develop and present statistical and algorithmic approaches that take as input ranked lists of sequences and return significant motifs. The efficiency of our approach, based on suffix trees, allows searches over motif spaces that are not covered by existing tools. This includes searching variable gap motifs-two half sites with a flexible length gap in between-and searching long motifs over large alphabets. We used our approach to analyze several high-throughput measurement data sets and report some validation results as well as novel suggested motifs and motif refinements. We suggest a refinement of the known estrogen receptor 1 motif in humans, where we observe gaps other than three nucleotides that also serve as significant recognition sites, as well as a variable length motif related to potential tyrosine phosphorylation.