DBD: a transcription factor prediction database.

BackgroundIntrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.MethodsIn this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).ResultsTo evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.ConclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

BackgroundProfile Hidden Markov Model (HMM) is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available.ResultsWe develop a visual editor for profile Hidden Markov Models (HMMEditor). HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path.ConclusionHMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

The N-homologue LRR domain adopts a folding which explains the TMV-Cg-induced HR-like response in sensitive tobacco plants

Leaves are the most conspicuous planar organs in plants, designed for efficient capture of sunlight and its conversion to energy that is channeled into sustaining the entire biosphere. How a few founder cells derived from the shoot apical meristem give rise to diverse leaf forms has interested naturalists and developmental biologists alike. At the heart of leaf morphogenesis lie two simple cellular processes, division and expansion, that are spatially and temporally segregated in a developing leaf. In leaves of dicot model species, cell division occurs predominantly at the base, concomitant with the expansion and differentiation of cells at the tip of the lamina that drives increase in leaf surface area. The timing of the transition from one cell fate (division) to the other (expansion) within a growing leaf lamina is a critical determinant of final leaf shape, size, complexity and flatness. The TCP proteins, unique to plant kingdom, are sequence-specific DNA-binding transcription factors that control several developmental and physiological traits. A sub-group of class II TCPs, called CINCINNATA-like TCPs (CIN-TCPs henceforth), are key regulators of the timing of the transition from division to expansion in dicot leaves. The current review highlights recent advances in our understanding of how the pattern of CIN-TCP activity is translated to the dynamic spatio-temporal control of cell-fate transition through the transactivation of cell-cycle regulators, growth-repressing microRNAs, and interactions with the chromatin remodeling machinery to modulate phytohormone responses. Unravelling how environmental inputs influence CIN-TCP-mediated growth control is a challenge for future studies. © 2018 IUBMB Life, 70(8):718-731, 2018.

Ets proteins are transcription factors that activate or repress the expression of genes that are involved in various biological processes, including cellular proliferation, differentiation, development, transformation and apoptosis. Like other Ets-family members, Elf3 functions as a sequence-specific DNA-binding transcriptional factor. A mouse Elf3 C-terminal fragment (amino-acid residues 269-371) containing the DNA-binding domain has been crystallized in complex with mouse type II TGF-beta receptor promoter (TbetaR-II) DNA. The crystals belonged to space group P2(1)2(1)2(1), with unit-cell parameters a = 42.66, b = 52, c = 99.78 A, and diffracted to a resolution of 2.2 A.

Today, an important problem in molecular biology is the study of gene expression mechanism. The first step in determining differential gene expression is the binding of sequence specific transcription factors to regulatory regions of the genes. An important aspect to understand how a given transcription factor functions is to know the entire gamut of binding sites and potential target genes that the factor may regulate. In this paper, we presented an improved prediction method based on hidden Markov model (HMM) called BSS-HMM(3)s (binding site search based on third-order HMMs) for transcription factor binding sites. The results show that the predicted sensitivity and specificity of BSS-HMM(3)s increased 11.95 and 12.97%, respectively, compared with Match.

Transcription is regulated by sequence-specific transcription factors (TFs) that bind to short genomic DNA elements that can be located in promoters, enhancers and other cis-regulatory modules. Determining which TFs bind where requires techniques that enable the ab initio identification of TF-DNA interactions. These techniques can either be "TF-centered" (protein-to-DNA), where regions of DNA bound by a TF of interest are identified, or "gene-centered" (DNA-to-protein), where TFs that bind a DNA sequence of interest are identified. Here, we describe gene-centered yeast one-hybrid (Y1H) assays. Briefly, in Y1H assays, a DNA fragment is cloned upstream of two different reporters, and these reporter constructs are integrated into the genome of a yeast strain. Next, plasmids expressing TFs as hybrid proteins (hence the name of the assay) fused with the strong transcriptional activation domain (AD) of the yeast TF Gal4 are introduced into the yeast strain. When a TF interacts with the DNA fragment of interest, the AD moiety activates reporter expression in yeast regardless of whether the TF is an activator or repressor in vivo. Sequencing the plasmid in the colonies that exhibit reporter activation reveals the identity of the TFs that can bind the DNA fragment. We have shown Y1H to be a robust method for detecting interactions between a variety of DNA elements and multiple families of TFs.

Development of the malaria parasite, Plasmodium falciparum, is regulated by a limited number of sequence-specific transcription factors (TFs). However, the mechanisms by which these TFs recognize genome-wide binding sites is largely unknown. To address TF specificity, we investigated the binding of two TF subsets that either bind CACACA or GTGCAC DNA sequence motifs and further characterized two additional ApiAP2 TFs, PfAP2-G and PfAP2-EXP, which bind unique DNA motifs (GTAC and TGCATGCA). We also interrogated the impact of DNA sequence and chromatin context on P. falciparum TF binding by integrating high-throughput in vitro and in vivo binding assays, DNA shape predictions, epigenetic post-translational modifications, and chromatin accessibility. We found that DNA sequence context minimally impacts binding site selection for paralogous CACACA-binding TFs, while chromatin accessibility, epigenetic patterns, co-factor recruitment, and dimerization correlate with differential binding. In contrast, GTGCAC-binding TFs prefer different DNA sequence context in addition to chromatin dynamics. Finally, we determined that TFs that preferentially bind divergent DNA motifs may bind overlapping genomic regions due to low-affinity binding to other sequence motifs. Our results demonstrate that TF binding site selection relies on a combination of DNA sequence and chromatin features, thereby contributing to the complexity of P. falciparum gene regulatory mechanisms.

In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting description is then used to build a profile hidden Markov model (PHMM) representation of the shape. PHMMs are a particular type of hidden Markov models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions and missing contour parts. Different from traditional HMM-based shape models, the sparseness of the PHMM structure allows efficient inference and learning algorithms for shape modeling and analysis. The new framework can be applied to a wide range of problems, from shape matching and classification to shape segmentation. Our experimental results show the effectiveness and robustness of this new approach in the three application domains.

In this work we created a sequence model that goes beyond simple linear patterns to model a specific type of higher-order relationship possible in biological sequences. Particularly, we seek models that can account for partially overlaid and interleaved patterns in biological sequences. Our proposed context-switching model (cswHMM) is designed as a variable-order hidden Markov model (HMM) with a specific structure that allows switching control between two or more sub-models.Tests of this approach suggest that a combination of HMMs for protein sequence analysis, such as pattern mining based HMMs or profile HMMs, with the context-switching approach can improve the descriptive ability and performance of the models.

In protein structure prediction problem, DNA-binding protein identification plays a significant role in various processes like transcription, DNA replication, DNA recombination, repair and modification. It has been a very active area of research to find an effective way to solve DNA-binding protein problem. The experimental methods that are employed for protein structure prediction are quite expensive and time consuming. Most of the methods have yielded better results in extracting evolutionary features. Recently, hidden Markov model (HMM) has been employed to extract features with remarkable increase in the efficiency for identification of DNA-binding proteins. In this study, a novel system based on HMM profile has been proposed to capture distant homology and evolutionary information. The experiments using the HMM profile model have been carried out on independent dataset with prediction accuracy 72% prediction accuracy, which is 3% better compared to the previous attempts. In this study, a comprehensive analysis of HMM profile model is conducted and the results are compared with other traditional models based on performances indices.KeywordsHidden Markov modelDNA-binding proteinProfile HMM

Motivation: One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif.Results: We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile–profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions.Availability and implementaion: A webserver that runs SMURFLite is available at: http://smurf.cs.tufts.edu/smurflite/Contact: lenore.cowen@tufts.edu; bab@mit.edu

Proximity labelling that uses promiscuous biotin ligases (BirA) fused to a bait protein is a powerful tool to identify protein interaction partners in vivo under different metabolic or developmental conditions. BirA can also be used to determine protein composition and interaction partners at specific chromatin locations when it is fused with enzymatically-disabled Cas9 (dCas9) and then guided to the location of interest by sgRNAs. We adapted this method (called CasID) for fungal cells using the nitrate assimilation gene cluster of A. nidulans as a model locus and estrogen-inducible expression of the dCas9-BirA fusion to improve condition-specific labelling. For method establishment, we first verified the presence of dCas-BirA and a known transcription factor at the nitrate locus by chromatin immunoprecipitation (ChIP). Results show that both dCas-BirA and the AreA transcription factor are present at the locus of interest under the conditions used for biotinylation. We then optimized the CasID procedure for efficient labelling and background reduction using the CasID-sgRNA strain and two control strains, one lacking the sgRNA and another one lacking the whole CasID system. Here we provide proof-of-concept for the suitability of the method by showing that biotinylated proteins are enriched in the CasID strains in comparison to the controls. After background reduction, 32 proteins remained in two independent experiments exclusively enriched in the Cas-ID-sgRNA strain. Among these proteins was NmrA, an AreA-interacting regulator, and we also found several chromatin-associated proteins. Overall, our results demonstrate that CasID is suitable for locus-specific labelling and identification of chromatin-associated proteins and transcription factors in A. nidulans. However, the high background of proteins that are biotinylated out of chromatin context or unspecifically attach to the affinity purification matrix needs to be addressed by implementing a set of rigorous controls. In summary, we herewith provide a detailed protocol for application of the method that proved to be useful for the identification of novel chromatin-associated proteins and their interaction partners at a specific genomic locus in divers metabolic and developmental conditions. AUTHOR SUMMARY: This study demonstrates that locus-specific proteomics can be carried out by dCas-BirA guided proximity labelling in Aspergillus nidulans. For establishment, we targeted the well-described bidirectional promoter region between niaD, a nitrate reductase, and niiA, a nitrite reductase. At this locus we could test by chromatin immunoprecipitation (ChIP) in combination with qPCR if both, the dCas9-BirA fusion as well as a central transcription factor are at the locus under the conditions of our CasID experiment. After this first control step, we considered that unspecific labelling by dCas-BirA during the time from translation to landing at the targeted chromatin locus may be one of the most relevant drawbacks of the method. Therefore, we developed a number of control strains that would allow us to clearly discriminate between background and sgRNA-dependent specific labelling at the locus. Our protein MS results validated these estimates and only considering the results of these controls enabled us to distinguish the set of locus-specific proteins from a very high general background. Finally, enrichment of biotinylated proteins through affinity purification with streptavidin resin and subsequent LC-MS/MS analysis showed that more than 800 proteins were detected in each sample, emphasizing the high background of the purification method. After background reduction of the control samples, we were able to identify 32 proteins which were exclusively detected in the test strain in two independent measurements, including several chromatin-associated proteins and NmrA, a negative regulator of the nitrate locus transcription factor AreA.

Physiological, biochemical, and transcriptional regulation in a leguminous forage Trifolium pratense L. responding to silver ions

The Pax genes encode sequence-specific DNA binding transcription factors that are expressed in embryonic development of the nervous system. Primary neuronal cell cultures derived from the cerebellar cortex of embryonic day 14, newborn and 7-d old mice, were used to investigate the cell-type specific expression patterns of three members of the murine paired box containing gene family (Pax gene family), in vitro. Cell types which express Pax-2, Pax-3, and Pax-6 RNA in primary cultures correspond to those found in regions of the cerebellum which show RNA signals in sections of the developing mouse brain. To find mechanisms regulating Pax gene expression during cerebellar development, the differential regulation of Pax-2, Pax-3, and Pax-6 by NGF and BDNF, two structurally related neurotrophins, was studied in such primary cultures. Pax-2 and Pax-6 RNA increased slightly by 1 h and remained elevated throughout a 24-h treatment with BDNF and NGF. Pax-3 RNA was not detected in newborn cultures, but underwent a rapid (1 h) and transient (2 h) induction upon treatment with either BDNF or NGF. No response was seen with EGF or FGF. Cycloheximide treatment amplified Pax-3 induction and prolonged the signal. Thus, Pax-3 induction resembles that of the immediate-early gene c-fos, which transduces growth factor signals during the development of particular neuronal/glial cell types. The changes in Pax expression were inductive rather than trophic.