Mikael Bodén

MEME Suite: tools for motif discovery and searching

The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.

Molecular basis for specificity of nuclear import and prediction of nuclear localization

Although proteins are translated on cytoplasmic ribosomes, many of these proteins play essential roles in the nucleus, mediating key cellular processes including but not limited to DNA replication and repair as well as transcription and RNA processing. Thus, understanding how these critical nuclear proteins are accurately targeted to the nucleus is of paramount importance in biology. Interaction and structural studies in the recent years have jointly revealed some general rules on the specificity determinants of the recognition of nuclear targeting signals by their specific receptors, at least for two nuclear import pathways: (i) the classical pathway, which involves the classical nuclear localization sequences (cNLSs) and the receptors importin-α/karyopherin-α and importin-β/karyopherin-β1; and (ii) the karyopherin-β2 pathway, which employs the proline-tyrosine (PY)-NLSs and the receptor transportin-1/karyopherin-β2. The understanding of specificity rules allows the prediction of protein nuclear localization. We review the current understanding of the molecular determinants of the specificity of nuclear import, focusing on the importin-α•cargo recognition, as well as the currently available databases and predictive tools relevant to nuclear localization. This article is part of a Special Issue entitled: Regulation of Signaling and Cellular Fate through Modulation of Nuclear Protein Import.

Prediction of subcellular localization using sequence-biased recurrent networks

MOTIVATION Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. RESULTS We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively. AVAILABILITY The Protein Prowler incorporating the recurrent network predictor described in this paper is available online at http://pprowler.imb.uq.edu.au/

The value of position-specific priors in motif discovery using MEME

BackgroundPosition-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types–including sequence conservation, nucleosome positioning, and negative examples–can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM).ResultsWe extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEMEs ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a Gibbs sampling algorithm using the same prior.ConclusionsWe conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.

Assigning roles to DNA regulatory motifs using comparative genomics

Motivation: Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network. Results: Here, we present an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs. We incorporate our approach into the Gomo algorithm, a computational tool for detecting associations between a user-specified DNA regulatory motif [expressed as a position weight matrix (PWM)] and Gene Ontology (GO) terms. Incorporating multiple species into the analysis significantly improves Gomos ability to identify GO terms associated with the regulatory targets of TFs. Including three comparative species in the process of predicting TF roles in Saccharomyces cerevisiae and Homo sapiens increases the number of significant predictions by 75 and 200%, respectively. The predicted GO terms are also more specific, yielding deeper biological insight into the role of the TF. Adjusting motif (binding) affinity scores for individual sequence composition proves to be essential for avoiding false positive associations. We describe a novel DNA sequence-scoring algorithm that compensates a thermodynamic measure of DNA-binding affinity for individual sequence base composition. Gomos prediction accuracy proves to be relatively insensitive to how promoters are defined. Because Gomo uses a threshold-free form of gene set analysis, there are no free parameters to tune. Biologists can investigate the potential roles of DNA regulatory motifs of interest using Gomo via the web (http://meme.nbcr.net). Contact: t.bailey@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Inferring short tandem repeat variation from paired-end short reads

The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in sequence fragment sizes, allowing the analysis of repeats at lengths of relevance to a range of phenotypes. We demonstrate the method’s ability to detect and quantify changes in repeat lengths from short read genomic sequence data across genotypes. We use the method to estimate repeat variation among 12 strains of Arabidopsis thaliana and demonstrate experimentally that our method compares favourably against existing methods. Using this method, we have identified all repeats across the genome, which are likely to be polymorphic. In addition, our predicted polymorphic repeats also included the only known repeat expansion in A. thaliana, suggesting an ability to discover potential unstable repeats.

Distinctive Conformation of Minor Site‐Specific Nuclear Localization Signals Bound to Importin‐α

Nuclear localization signals (NLSs) contain one or two clusters of basic residues and are recognized by the import receptor importin‐α. There are two NLS‐binding sites (major and minor) on importin‐α and the major NLS‐binding site is considered to be the primary binding site. Here, we used crystallographic and biochemical methods to investigate the binding between importin‐α and predicted ‘minor site‐specific’ NLSs: four peptide library‐derived peptides, and the NLS from mouse RNA helicase II/Guα. The crystal structures reveal that these atypical NLSs indeed preferentially bind to the minor NLS‐binding site. Unlike previously characterized NLSs, the C‐terminal residues of these NLSs form an α‐helical turn, stabilized by internal H‐bond and cation‐π interactions between the aromatic residues from the NLSs and the positively charged residues from importin‐α. This helical turn sterically hinders binding at the major NLS‐binding site, explaining the minor‐site preference. Our data suggest the sequence RXXKR[K/X][F/Y/W]XXAF as the optimal minor NLS‐binding site‐specific motif, which may help identify novel proteins with atypical NLSs.

The Applicability of Recurrent Neural Networks for Biological Sequence Analysis

Selection of machine learning techniques requires a certain sensitivity to the requirements of the problem. In particular, the problem can be made more tractable by deliberately using algorithms that are biased toward solutions of the requisite kind. In this paper, we argue that recurrent neural networks have a natural bias toward a problem domain of which biological sequence analysis tasks are a subset. We use experiments with synthetic data to illustrate this bias. We then demonstrate that this bias can be exploitable using a data set of protein sequences containing several classes of subcellular localization targeting peptides. The results show that, compared with feed forward, recurrent neural networks will generally perform better on sequence analysis tasks. Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical.

Crystal Structure of Rice Importin-α and Structural Basis of Its Interaction with Plant-Specific Nuclear Localization Signals

A combination of crystallography, interaction analysis, and nuclear import assays demonstrates a distinct mode of autoinhibition in rice importin- α1a and the binding of plant-specific nuclear localization signals (NLSs) to its minor NLS binding site. In the classical nucleocytoplasmic import pathway, nuclear localization signals (NLSs) in cargo proteins are recognized by the import receptor importin-α. Importin-α has two separate NLS binding sites (the major and the minor site), both of which recognize positively charged amino acid clusters in NLSs. Little is known about the molecular basis of the unique features of the classical nuclear import pathway in plants. We determined the crystal structure of rice (Oryza sativa) importin-α1a at 2-Å resolution. The structure reveals that the autoinhibitory mechanism mediated by the importin-β binding domain of importin-α operates in plants, with NLS-mimicking sequences binding to both minor and major NLS binding sites. Consistent with yeast and mammalian proteins, rice importin-α binds the prototypical NLS from simian virus 40 large T-antigen preferentially at the major NLS binding site. We show that two NLSs, previously described as plant specific, bind to and are functional with plant, mammalian, and yeast importin-α proteins but interact with rice importin-α more strongly. The crystal structures of their complexes with rice importin-α show that they bind to the minor NLS binding site. By contrast, the crystal structures of their complexes with mouse (Mus musculus) importin-α show preferential binding to the major NLS binding site. Our results reveal the molecular basis of a number of features of the classical nuclear transport pathway specific to plants.

Associating transcription factor-binding site motifs with target GO terms and target genes

The roles and target genes of many transcription factors (TFs) are still unknown. To predict the roles of TFs, we present a computational method for associating Gene Ontology (GO) terms with TF-binding motifs. The method works by ranking all genes as potential targets of the TF, and reporting GO terms that are significantly associated with highly ranked genes. We also present an approach, whereby these predicted GO terms can be used to improve predictions of TF target genes. This uses a novel gene-scoring function that reflects the insight that genes annotated with GO terms predicted to be associated with the TF are more likely to be its targets. We construct validation sets of GO terms highly associated with known targets of various yeast and human TF. On the yeast reference sets, our prediction method identifies at least one correct GO term for 73% of the TF, 49% of the correct GO terms are predicted and almost one-third of the predicted GO terms are correct. Results on human reference sets are similarly encouraging. Validation of our target gene prediction method shows that its accuracy exceeds that of simple motif scanning.