Motif Detection Research Articles

Identifying tandem Ankyrin repeats in protein structures.

BackgroundTandem repetition of structural motifs in proteins is frequently observed across all forms of life. Topology of repeating unit and its frequency of occurrence are associated to a wide range of structural and functional roles in diverse proteins, and defects in repeat proteins have been associated with a number of diseases. It is thus desirable to accurately identify specific repeat type and its copy number. Weak evolutionary constraints on repeat units and insertions/deletions between them make their identification difficult at the sequence level and structure based approaches are desired. The proposed graph spectral approach is based on protein structure represented as a graph for detecting one of the most frequently observed structural repeats, Ankyrin repeat.ResultsIt has been shown in a large number of studies that 3-dimensional topology of a protein structure is well captured by a graph, making it possible to analyze a complex protein structure as a mathematical entity. In this study we show that eigen spectra profile of a protein structure graph exhibits a unique repetitive profile for contiguous repeating units enabling the detection of the repeat region and the repeat type. The proposed approach uses a non-redundant set of 58 Ankyrin proteins to define rules for the detection of Ankyrin repeat motifs. It is evaluated on a set of 370 proteins comprising 125 known Ankyrin proteins and remaining non-solenoid proteins and the prediction compared with UniProt annotation, sequence-based approach, RADAR, and structure-based approach, ConSole. To show the efficacy of the approach, we analyzed the complete PDB structural database and identified 641 previously unrecognized Ankyrin repeat proteins. We observe a unique eigen spectra profile for different repeat types and show that the method can be easily extended to detect other repeat types. It is implemented as a web server, AnkPred. It is freely available at ‘bioinf.iiit.ac.in/AnkPred’.ConclusionsAnkPred provides an elegant and computationally efficient graph-based approach for detecting Ankyrin structural repeats in proteins. By analyzing the eigen spectra of the protein structure graph and secondary structure information, characteristic features of a known repeat family are identified. This method is especially useful in correctly identifying new members of a repeat family.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0440-9) contains supplementary material, which is available to authorized users.

Can bioinformatics help in the identification of moonlighting proteins?

Protein multitasking or moonlighting is the capability of certain proteins to execute two or more unique biological functions. This ability to perform moonlighting functions helps us to understand one of the ways used by cells to perform many complex functions with a limited number of genes. Usually, moonlighting proteins are revealed experimentally by serendipity, and the proteins described probably represent just the tip of the iceberg. It would be helpful if bioinformatics could predict protein multifunctionality, especially because of the large amounts of sequences coming from genome projects. In the present article, we describe several approaches that use sequences, structures, interactomics and current bioinformatics algorithms and programs to try to overcome this problem. The sequence analysis has been performed: (i) by remote homology searches using PSI-BLAST, (ii) by the detection of functional motifs, and (iii) by the co-evolutionary relationship between amino acids. Programs designed to identify functional motifs/domains are basically oriented to detect the main function, but usually fail in the detection of secondary ones. Remote homology searches such as PSI-BLAST seem to be more versatile in this task, and it is a good complement for the information obtained from protein-protein interaction (PPI) databases. Structural information and mutation correlation analysis can help us to map the functional sites. Mutation correlation analysis can be used only in very restricted situations, but can suggest how the evolutionary process of the acquisition of the second function took place.

Evolutionary optimization of transcription factor binding motif detection.

All the cell types are under strict control of how their genes are transcribed into expressed transcripts by the temporally dynamic orchestration of the transcription factor binding activities. Given a set of known binding sites (BSs) of a given transcription factor (TF), computational TFBS screening technique represents a cost efficient and large scale strategy to complement the experimental ones. There are two major classes of computational TFBS prediction algorithms based on the tertiary and primary structures, respectively. A tertiary structure based algorithm tries to calculate the binding affinity between a query DNA fragment and the tertiary structure of the given TF. Due to the limited number of available TF tertiary structures, primary structure based TFBS prediction algorithm is a necessary complementary technique for large scale TFBS screening. This study proposes a novel evolutionary algorithm to randomly mutate the weights of different positions in the binding motif of a TF, so that the overall TFBS prediction accuracy is optimized. The comparison with the most widely used algorithm, Position Weight Matrix (PWM), suggests that our algorithm performs better or the same level in all the performance measurements, including sensitivity, specificity, accuracy and Matthews correlation coefficient. Our data also suggests that it is necessary to remove the widely used assumption of independence between motif positions.The supplementary material may be found at: http://www.healthinformaticslab.org/supp/ .

In-situ annotation of carbohydrate diversity, abundance, and degradability in highly complex mixtures using NMR spectroscopy.

Many functions of carbohydrates depend on the detection of short structural motifs, approximately up to hexasaccharide length, by receptors or catalysts. This study investigates the usefulness of state-of-the-art (1)H-(13)C nuclear-magnetic-resonance (NMR) spectroscopy for characterizing the diversity, abundance, and degradability of such short structural motifs in plant-derived carbohydrates. Assignments of carbohydrate signals for (1)H-(13)C NMR spectra of beer, wine, and fruit juice yield up to >130 assignments in situ, i.e. in individual samples without separation or derivatization. More than 500 structural motifs can be resolved over a concentration range of ~10(3)in experiments of a few hours duration. The diversity of carbohydrate units increases according to power laws at lower concentrations for both cereal and fruit-derived samples. Simple graphs resolve the smaller overall contribution of more diverse but less abundant motifs to carbohydrate biomass. Susceptibility to enzymatic degradation is probed simultaneously for hundreds of structural motifs to reduce uncertainties in the identification of motifs and to probe enzyme specificity with assigned motifs. Several properties of fundamental biotechnological importance can thus be assessed in situ by heteronuclear NMR methods.

Acc-Motif: Accelerated Network Motif Detection.

Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo et al. [1], which provided motifs as a way to uncover the basic building blocks of most networks. Motifs have been mainly applied in Bioinformatics, regarding gene regulation networks. Motif detection is based on induced subgraph counting. This paper proposes an algorithm to count subgraphs of size k + 2 based on the set of induced subgraphs of size k. The general technique was applied to detect 3, 4 and 5-sized motifs in directed graphs. Such algorithms have time complexity O(a(G)m), O(m(2)) and O(nm(2)), respectively, where a(G) is the arboricity of G(V, E). The computational experiments in public data sets show that the proposed technique was one order of magnitude faster than Kavosh and FANMOD. When compared to NetMODE, acc-Motif had a slightly improved performance.

Real-time twitter recommendation

We describe a production Twitter system for generating relevant, personalized, and timely recommendations based on observing the temporally-correlated actions of each user's followings. The system currently serves millions of recommendations daily to tens of millions of mobile users. The approach can be viewed as a specific instance of the novel problem of online motif detection in large dynamic graphs. Our current solution partitions the graph across a number of machines, and with the construction of appropriate data structures, motif detection can be translated into the lookup and intersection of adjacency lists in each partition. We conclude by discussing a generalization of the problem that perhaps represents a new class of data management systems.

Context-aware semi-supervised motif detection approach.

Motif detection has raised as an important task in bioinformatics. Recently, the discovery of motifs that are localized relative to a certain biological area has become an important task in many applications. For example, it is used to discover regulatory sequences beside the transcription start site and the neighborhood of known transcription factor binding sites [1]. Therefore, the idea of context aware motif detection approach is needed. Moreover, there is an interest to use both labeled and unlabeled sets to enhance the motif detection approaches. In this paper, three novel context aware semi-supervised motif detection approaches are proposed, which are self-learning, context aware and co-training context aware systems. In self-learning motif Hidden Markov Model (HMM) is enhanced independently using unlabeled sets. While in co-training, three different models are trained based on three different views which are pre-motif sequences, motif sequences and post-motif sequences. Moreover, our co-training context aware system is suitable for parallelization to enhance its execution time. The approaches were evaluated using human motif sequences and the results show that co-training context aware system has achieved the best results. The results also show that our approach outperforms other related works in [1], [2] and [3].

Current innovations and future challenges of network motif detection

Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs. This work analyzes 11 network motif detection tools and algorithms. Detailed comparisons and insightful directions for using these tools and algorithms are discussed. Key aspects of network motif detection are investigated. Network motif types and common network motifs as well as their biological functions are discussed. Applications of network motifs are also presented. Finally, the challenges, future improvements and future research directions for network motif detection are also discussed.

Chem-path-tracker: An Automated Tool to Analyze Chemical Motifs in Molecular Structures

In this article, we propose a method for locating functionally relevant chemical motifs in protein structures. The chemical motifs can be a small group of residues or structure protein fragments with highly conserved properties that have important biological functions. However, the detection of chemical motifs is rather difficult because they often consist of a set of amino acid residues separated by long, variable regions, and they only come together to form a functional group when the protein is folded into its three-dimensional structure. Furthermore, the assemblage of these residues is often dependent on non-covalent interactions among the constituent amino acids that are difficult to detect or visualize. To simplify the analysis of these chemical motifs and give access to a generalized use for all users, we developed chem-path-tracker. This software is a VMD plug-in that allows the user to highlight and reveal potential chemical motifs requiring only a few selections. The analysis is based on atoms/residues pair distances applying a modified version of Dijkstra's algorithm, and it makes possible to monitor the distances of a large pathway, even during a molecular dynamics simulation. This tool turned out to be very useful, fast, and user-friendly in the performed tests. The chem-path-tracker package is distributed as an independent platform and can be found at http://www.fc.up.pt/PortoBioComp/database/doku.php?id=chem-path-tracker.

Integrating bioinformatic resources to predict transcription factors interacting with cis-sequences conserved in co-regulated genes

BackgroundUsing motif detection programs it is fairly straightforward to identify conserved cis-sequences in promoters of co-regulated genes. In contrast, the identification of the transcription factors (TFs) interacting with these cis-sequences is much more elaborate. To facilitate this, we explore the possibility of using several bioinformatic and experimental approaches for TF identification. This starts with the selection of co-regulated gene sets and leads first to the prediction and then to the experimental validation of TFs interacting with cis-sequences conserved in the promoters of these co-regulated genes.ResultsUsing the PathoPlant database, 32 up-regulated gene groups were identified with microarray data for drought-responsive gene expression from Arabidopsis thaliana. Application of the binding site estimation suite of tools (BEST) discovered 179 conserved sequence motifs within the corresponding promoters. Using the STAMP web-server, 49 sequence motifs were classified into 7 motif families for which similarities with known cis-regulatory sequences were identified. All motifs were subjected to a footprintDB analysis to predict interacting DNA binding domains from plant TF families. Predictions were confirmed by using a yeast-one-hybrid approach to select interacting TFs belonging to the predicted TF families. TF-DNA interactions were further experimentally validated in yeast and with a Physcomitrella patens transient expression system, leading to the discovery of several novel TF-DNA interactions.ConclusionsThe present work demonstrates the successful integration of several bioinformatic resources with experimental approaches to predict and validate TFs interacting with conserved sequence motifs in co-regulated genes.

ConSole: using modularity of contact maps to locate solenoid domains in protein structures.

BackgroundPeriodic proteins, characterized by the presence of multiple repeats of short motifs, form an interesting and seldom-studied group. Due to often extreme divergence in sequence, detection and analysis of such motifs is performed more reliably on the structural level. Yet, few algorithms have been developed for the detection and analysis of structures of periodic proteins.ResultsConSole recognizes modularity in protein contact maps, allowing for precise identification of repeats in solenoid protein structures, an important subgroup of periodic proteins. Tests on benchmarks show that ConSole has higher recognition accuracy as compared to Raphael, the only other publicly available solenoid structure detection tool. As a next step of ConSole analysis, we show how detection of solenoid repeats in structures can be used to improve sequence recognition of these motifs and to detect subtle irregularities of repeat lengths in three solenoid protein families.ConclusionsThe ConSole algorithm provides a fast and accurate tool to recognize solenoid protein structures as a whole and to identify individual solenoid repeat units from a structure. ConSole is available as a web-based, interactive server and is available for download at http://console.sanfordburnham.org.

Application of the Codon-based Scoring Method in Motif Detection

A RNA or DNA sequence motif is a short sequence found within a particular nucleic acid sequence families. Most amino acid and nucleic acid sequences have some level of functional or structural similarities. These similarities are mostly represented by short, contiguous sequences called motif. Motif discovery is an important aspect of molecular biology. This is because the knowledge of these sequences helps determine their structural properties, signal sites and/or ligand-binding sites. In most cases, depending on the function of the motif, these contiguous regions can be highly conserved with an homology of nearly 100%. Several algorithms have been proposed for the discovery of motifs. In this paper, the codon-based scoring method is employed to detect motifs and with their invariants. The result obtained shows the reliability and robustness of the method as motifs are discovered irrespective of their length and position in a sequence.

Estimation of protein function using template-based alignment of enzyme active sites

BackgroundThe accumulation of protein structural data occurs more rapidly than it can be characterized by traditional laboratory means. This has motivated widespread efforts to predict enzyme function computationally. The most useful/accurate strategies employed to date are based on the detection of motifs in novel structures that correspond to a specific function. Functional residues are critical components of predictively useful motifs. We have implemented a novel method, to complement current approaches, which detects motifs solely on the basis of distance restraints between catalytic residues.ResultsProMOL is a plugin for the PyMOL molecular graphics environment that can be used to create active site motifs for enzymes. A library of 181 active site motifs has been created with ProMOL, based on definitions published in the Catalytic Site Atlas (CSA). Searches with ProMOL produce better than 50% useful Enzyme Commission (EC) class suggestions for level 1 searches in EC classes 1, 4 and 5, and produce some useful results for other classes. 261 additional motifs automatically translated from Jonathan Barker’s JESS motif set [Bioinformatics 19:1644–1649, 2003] and a set of NMR motifs is under development. Alignments are evaluated by visual superposition, Levenshtein distance and root-mean-square deviation (RMSD) and are reasonably consistent with related search methods.ConclusionThe ProMOL plugin for PyMOL provides ready access to template-based local alignments. Recent improvements to ProMOL, including the expanded motif library, RMSD calculations and output selection formatting, have greatly increased the program’s usability and speed, and have improved the way that the results are presented.

RepARK—de novo creation of repeat libraries from whole-genome NGS reads

Generation of repeat libraries is a critical step for analysis of complex genomes. In the era of next-generation sequencing (NGS), such libraries are usually produced using a whole-genome shotgun (WGS) derived reference sequence whose completeness greatly influences the quality of derived repeat libraries. We describe here a de novo repeat assembly method—RepARK (Repetitive motif detection by Assembly of Repetitive K-mers)—which avoids potential biases by using abundant k-mers of NGS WGS reads without requiring a reference genome. For validation, repeat consensuses derived from simulated and real Drosophila melanogaster NGS WGS reads were compared to repeat libraries generated by four established methods. RepARK is orders of magnitude faster than the other methods and generates libraries that are: (i) composed almost entirely of repetitive motifs, (ii) more comprehensive and (iii) almost completely annotated by TEclass. Additionally, we show that the RepARK method is applicable to complex genomes like human and can even serve as a diagnostic tool to identify repetitive sequences contaminating NGS datasets.

Motif Analyzer for protein 3D structures

The topology of the protein structure of all-β- or α/β-class is a special arrangement of β-strands within β-sheets (and α-helices surrounding β-sheets) and the order of them along the polypeptide chain. Structural motifs are a subset of strands and/or helices with widely spread topology. Structural motifs are used for classification of protein structure. Because of an increasing variety of known structures, an automatic tool for motif detection is needed. MotAn is an algorithmic detector of structural motifs in a given 3D protein structure. It detects β-hairpins, β-meanders, β-helices, Greek keys, interlocks, jellyrolls, β-α-β-motifs and β-α-β-helices. MotAn was tested on selected SCOP families and shown to be more sensitive detector than the PTGL and PROMOTIF programs. MotAn is available at http://mouse.belozersky.msu.ru/motan.

NMDB: NETWORK MOTIF DATABASE ENVISAGED AND EXPLICATED FROM HUMAN DISEASE SPECIFIC PATHWAYS

The study of network motifs for large number of networks can aid us to resolve the functions of complex biological networks. In biology, network motifs that reappear within a network more often than expected in random networks include negative autoregulation, positive autoregulation, single-input modules, feedforward loops, dense overlapping regulons and feedback loops. These network motifs have their different dynamical functions. In this study, our main objective is to examine the enrichment of network motifs in different biological networks of human disease specific pathways. We characterize biological network motifs as biologically significant sub-graphs. We used computational and statistical criteria for efficient detection of biological network motifs, and introduced several estimation measures. Pathways of cardiovascular, cancer, infectious, repair, endocrine and metabolic diseases, were used for identifying and interlinking the relation between nodes. 3–8 sub-graph size network motifs were generated. Network Motif Database was then developed using PHP and MySQL. Results showed that there is an abundance of autoregulation, feedforward loops, single-input modules, dense overlapping regulons and other putative regulatory motifs in all the diseases included in this study. It is believed that the database will assist molecular and system biologists, biotechnologists, and other scientific community to encounter biologically meaningful information. Network Motif Database is freely available for academic and research purpose at: http://www.bioinfoindia.org/nmdb .

Promiscuous scaffolds in proteins - non-native, non-additive and non-trivial

Promiscuity, the ability of an enzyme to catalyze diverse activities using the same active site, sets up the stage for the evolution of complex organisms through gene duplication and specialization. The detection of promiscuous motifs is crucial to understand the physiological relevance of a protein, or for any endeavor that intends to rationally modify these latent capabilities to design new proteins under laboratory conditions. We have established a methodology for identifying catalytic residues based on spatial and electrostatic congruence with known active site configurations. Here, we discuss insights gained in several initiatives using our method on different enzymes.

Molecular detection, phylogenetic analysis, and identification of transcription motifs in feline leukemia virus from naturally infected cats in malaysia.

A nested PCR assay was used to determine the viral RNA and proviral DNA status of naturally infected cats. Selected samples that were FeLV-positive by PCR were subjected to sequencing, phylogenetic analysis, and motifs search. Of the 39 samples that were positive for FeLV p27 antigen, 87.2% (34/39) were confirmed positive with nested PCR. FeLV proviral DNA was detected in 38 (97.3%) of p27-antigen negative samples. Malaysian FeLV isolates are found to be highly similar with a homology of 91% to 100%. Phylogenetic analysis revealed that Malaysian FeLV isolates divided into two clusters, with a majority (86.2%) sharing similarity with FeLV-K01803 and fewer isolates (13.8%) with FeLV-GM1 strain. Different enhancer motifs including NF-GMa, Krox-20/WT1I-del2, BAF1, AP-2, TBP, TFIIF-beta, TRF, and TFIID are found to occur either in single, duplicate, triplicate, or sets of 5 in different positions within the U3-LTR-gag region. The present result confirms the occurrence of FeLV viral RNA and provirus DNA in naturally infected cats. Malaysian FeLV isolates are highly similar, and a majority of them are closely related to a UK isolate. This study provides the first molecular based information on FeLV in Malaysia. Additionally, different enhancer motifs likely associated with FeLV related pathogenesis have been identified.

A survey of motif finding Web tools for detecting binding site motifs in ChIP-Seq data

ChIP-Seq (chromatin immunoprecipitation sequencing) has provided the advantage for finding motifs as ChIP-Seq experiments narrow down the motif finding to binding site locations. Recent motif finding tools facilitate the motif detection by providing user-friendly Web interface. In this work, we reviewed nine motif finding Web tools that are capable for detecting binding site motifs in ChIP-Seq data. We showed each motif finding Web tool has its own advantages for detecting motifs that other tools may not discover. We recommended the users to use multiple motif finding Web tools that implement different algorithms for obtaining significant motifs, overlapping resemble motifs, and non-overlapping motifs. Finally, we provided our suggestions for future development of motif finding Web tool that better assists researchers for finding motifs in ChIP-Seq data.ReviewersThis article was reviewed by Prof. Sandor Pongor, Dr. Yuriy Gusev, and Dr. Shyam Prabhakar (nominated by Prof. Limsoon Wong).

Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs

This paper is concerned with the creation of 'macros' in workflow visualization as a support tool to increase the efficiency of data curation tasks. We propose computation of candidate macros based on their usage in large collections of workflows in data repositories. We describe an efficient algorithm for extracting macro motifs from workflow graphs. We discovered that the state transition information, used to identify macro candidates, characterizes the structural pattern of the macro and can be harnessed as part of the visual design of the corresponding macro glyph. This facilitates partial automation and consistency in glyph design applicable to a large set of macro glyphs. We tested this approach against a repository of biological data holding some 9,670 workflows and found that the algorithmically generated candidate macros are in keeping with domain expert expectations.