Motif Discovery Research Articles

Proteome‐scale amino‐acid resolution footprinting of protein‐binding sites in the intrinsically disordered regions

Protein‐protein interactions are central to cell function. Numerous studies have provided large‐scale information on human protein‐protein interactions. However, many interactions remain to be discovered, and low affinity, conditional and cell type‐specific interactions disproportionately under‐represented. I will present our efforts towards finding the short linear motif (SLiM)‐based interactions of a variety of protein domains. I will present an optimized proteomic peptide‐phage display (ProP‐PD) library that tiles all disordered regions of the human proteome and allows the screening of ~1,000,000 overlapping peptides in a single binding assay, and the tools and guidelines we have defined for processing the data (1). Using the approach we identified >2,000 interaction pairs for 35 known SLiM‐binding domains and confirmed the quality of the produced data by complementary biophysical or cell‐based assays. The amino acid resolution binding site information can be used to pin‐point functionally important disease mutations and phosphorylation events in intrinsically disordered regions of the proteome. Finally, I will describe how we developed the approach for large‐scale discovery of coronavirus‐host factor protein interactions (2). We screened more than 130 protein domains for binding to viral peptides, and translated the high‐resolution information on direct virus‐host interactions to a specific peptide‐based antiviral inhibitor of an interaction between the G3BP1/2 proteins and an ΦxFG peptide motif in the SARS‐CoV‐2 nucleocapsid (N) protein. ProP‐PD may thus be used both to illuminate the motif‐based part of the interactome and to uncover leads for innovative inhibitor design.References Benz et al., (2022) Proteome‐scale mapping of binding sites in the unstructured regions of the human proteome. Mol Syst Biol. 2022 Jan;18(1):e10584. Kruse et al., (2021) Large scale discovery of coronavirus‐host factor protein interaction motifs reveals SARS‐CoV‐2 specific mechanisms and vulnerabilities. Nat Commun. 2021 Nov 19;12(1):6761.

SHAPE-guided RNA structure homology search and motif discovery

The rapidly growing popularity of RNA structure probing methods is leading to increasingly large amounts of available RNA structure information. This demands the development of efficient tools for the identification of RNAs sharing regions of structural similarity by direct comparison of their reactivity profiles, hence enabling the discovery of conserved structural features. We here introduce SHAPEwarp, a largely sequence-agnostic SHAPE-guided algorithm for the identification of structurally-similar regions in RNA molecules. Analysis of Dengue, Zika and coronavirus genomes recapitulates known regulatory RNA structures and identifies novel highly-conserved structural elements. This work represents a preliminary step towards the model-free search and identification of shared and conserved RNA structural features within transcriptomes.

Novel ZNF414 activity characterized by integrative analysis of ChIP-exo, ATAC-seq and RNA-seq data

Transcription factor binding to DNA is a central mechanism regulating gene expression. Thus, thorough characterization of this process is essential for understanding cellular biology in both health and disease. We combined data from three sequencing-based methods to unravel the DNA binding function of the novel ZNF414 protein in cells representing two tumor types. ChIP-exo served to map protein binding sites, ATAC-seq allowed identification of open chromatin, and RNA-seq examined the transcriptome. We show that ZNF414 is a DNA-binding protein that both induces and represses gene expression. This transcriptional response has an impact on cellular processes related to proliferation and other malignancy-associated functions, such as cell migration and DNA repair. Approximately 20% of the differentially expressed genes harbored ZNF414 binding sites in their promoters in accessible chromatin, likely representing direct targets of ZNF414. De novo motif discovery revealed several putative ZNF414 binding sequences, one of which was validated using EMSA. In conclusion, this study illustrates a highly efficient integrative approach for the characterization of the DNA binding and transcriptional activity of transcription factors.

ATAC-Seq Reveals the Landscape of Open Chromatin andcis-Regulatory Elements in the Phytophthora sojae Genome.

Nucleosome-free open chromatin often harbors transcription factor (TF)-binding sites that are associated with active cis-regulatory elements. However, analysis of open chromatin regions has rarely been applied to oomycete or fungal plant pathogens. In this study, we performed the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) to identify open chromatin and cis-regulatory elements in Phytophthora sojae at the mycelial stage. We identified 10,389 peaks representing nucleosome-free regions (NFRs). The peaks were enriched in gene-promoter regions and associated with 40% of P. sojae genes; transcription levels were higher for genes with multiple peaks than genes with a single peak and were higher for genes with a single peak than genes without peak. Chromatin accessibility was positively correlated with gene transcription level. Through motif discovery based on NFR peaks in core promoter regions, 25 candidate cis-regulatory motifs with evidence of TF-binding footprints were identified. These motifs exhibited various preferences for location in the promoter region and associations with the transcription level of their target genes, which included some putative pathogenicity-related genes. As the first study revealing the landscape of open chromatin and the correlation between chromatin accessibility and gene transcription level in oomycetes, the results provide a technical reference and data resources for future studies on the regulatory mechanisms of gene transcription.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis

BackgroundTranscription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters.ResultsA new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach.ConclusionThis work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq.

Stabilization of antithetic control via molecular buffering.

A key goal in synthetic biology is the construction of molecular circuits that robustly adapt to perturbations. Although many natural systems display perfect adaptation, whereby stationary molecular concentrations are insensitive to perturbations, its de novo engineering has proven elusive. The discovery of the antithetic control motif was a significant step towards a universal mechanism for engineering perfect adaptation. Antithetic control provides perfect adaptation in a wide range of systems, but it can lead to oscillatory dynamics due to loss of stability; moreover, it can lose perfect adaptation in fast growing cultures. Here, we introduce an extended antithetic control motif that resolves these limitations. We show that molecular buffering, a widely conserved mechanism for homeostatic control in Nature, stabilizes oscillations and allows for near-perfect adaptation during rapid growth. We study multiple buffering topologies and compare their performance in terms of their stability and adaptation properties. We illustrate the benefits of our proposed strategy in exemplar models for biofuel production and growth rate control in bacterial cultures. Our results provide an improved circuit for robust control of biomolecular systems.

MODIT: MOtif DIscovery in Temporal Networks.

Temporal networks are graphs where each edge is linked with a timestamp, denoting when an interaction between two nodes happens. According to the most recently proposed definitions of the problem, motif search in temporal networks consists in finding and counting all connected temporal graphs Q (called motifs) occurring in a larger temporal network T, such that matched target edges follow the same chronological order imposed by edges in Q. In the last few years, several algorithms have been proposed to solve motif search, but most of them are limited to very small or specific motifs due to the computational complexity of the problem. In this paper, we present MODIT (MOtif DIscovery in Temporal Networks), an algorithm for counting motifs of any size in temporal networks, inspired by a very recent algorithm for subgraph isomorphism in temporal networks, called TemporalRI. Experiments show that for big motifs (more than 3 nodes and 3 edges) MODIT can efficiently retrieve them in reasonable time (up to few hours) in many networks of medium and large size and outperforms state-of-the art algorithms.

Clues to reaction specificity in PLP-dependent fold type I aminotransferases of monosaccharide biosynthesis.

Novel functions can emerge in an enzyme family while conserving catalytic mechanism, motif or fold. Pyridoxal 5'-phosphate-dependent enzymes have evolved into seven fold-types and catalyze diverse reactions using the same mechanism for the formation of external aldimine. Nucleotide sugar aminotransferases (which will be henceforth referred to as aminotransferases) belong to fold type I and mediate the biosynthesis of several monosaccharides. They use diverse substrates but are highly selective to the C3 or C4 carbon to which amine group is transferred. Profile hidden Markov models (HMMs) were able to identify aminotransferases but could not capture reaction specificity. A search for discriminating features led to the discovery of sequence motifs that are located near the pyranose binding site suggesting their role in imparting reaction specificity. Using a position weight matrix for this motif, we were able to assign reaction specificity to a large number of aminotransferases. Inferences from this analysis set way for future experiments that can shed light on mechanisms of functional diversification in nucleotide sugar aminotransferases of fold type I.

A Review on Planted (l, d) Motif Discovery Algorithms for Medical Diagnose.

Personalized diagnosis of chronic disease requires capturing the continual pattern across the biological sequence. This repeating pattern in medical science is called “Motif”. Motifs are the short, recurring patterns of biological sequences that are supposed signify some health disorder. They identify the binding sites for transcription factors that modulate and synchronize the gene expression. These motifs are important for the analysis and interpretation of various health issues like human disease, gene function, drug design, patient’s conditions, etc. Searching for these patterns is an important step in unraveling the mechanisms of gene expression properly diagnose and treat chronic disease. Thus, motif identification has a vital role in healthcare studies and attracts many researchers. Numerous approaches have been characterized for the motif discovery process. This article attempts to review and analyze fifty-four of the most frequently found motif discovery processes/algorithms from different approaches and summarizes the discussion with their strengths and weaknesses.

LOCATE reveals electrostatic “islands” and “hotspots” are important for a disordered clock protein's interactions to regulate clock robustness

Many important biological processes are regulated by the circadian molecular clock, a negative feedback loop comprised of disordered proteins. The lack of a fixed conformation means that identifying interaction regions on core clock proteins is untenable using typical biophysical methods. Since disordered proteins often interact through short linear binding motifs (SLiMs), we decided to take a divide-and-conquer approach leveraging synthetic peptide technology. Our method termed Linear mOtif disCovery using rATional dEsign (LOCATE) takes a disordered protein sequence and divides it into a set of short overlapping peptides that can be synthesized and printed in a microarray format.

Resolving Heparan Sulfate Oligosaccharide Positional Isomers Using Hydrophilic Interaction Liquid Chromatography-Cyclic Ion Mobility Mass Spectrometry.

Heparan sulfate (HS) is a linear polysaccharide covalently attached to proteoglycans on cell surfaces and within extracellular matrices in all animal tissues. Many biological processes are triggered by the interactions among HS binding proteins and short structural motifs in HS chains. The determination of HS oligosaccharide structures using liquid chromatography-mass spectrometry (LC-MS) is made challenging by the existence of positional sulfation and acetylation isomers. The determination of uronic acid epimer positions is even more challenging. While hydrophilic interaction liquid chromatography (HILIC) separates HS saccharides based on their composition, there is a very limited resolution of positional isomers. This lack of resolution places a burden on the tandem mass spectrometry step for assigning saccharide isomers. In this work, we explored the use of the ion mobility dimension to separate HS saccharide isomers based on molecular shape in the gas phase. We showed that the combination of HILIC and cyclic ion mobility mass spectrometry (cIM-MS) was extremely useful for resolving HS positional isomers including uronic acid epimers and sulfate positions. Furthermore, HILIC-cIM-MS differentiated multicomponent HS isomeric saccharide mixtures. In summary, HILIC-cIM-MS provided high-quality data for analysis of HS oligosaccharide isomeric mixtures that may prove useful in the discovery of new structural motifs for HS binding proteins and for the targeted quality control analysis of commercial HS products.

High-Throughput Activity Profiling of RNA-Cleaving DNA Catalysts by Deoxyribozyme Sequencing (DZ-seq).

RNA-cleaving deoxyribozymes have found broad application as useful tools for RNA biochemistry. However, tedious in vitro selection procedures combined with laborious characterization of individual candidate catalysts hinder the discovery of novel catalytic motifs. Here, we present a new high-throughput sequencing method, DZ-seq, which directly measures activity and localizes cleavage sites of thousands of deoxyribozymes. DZ-seq exploits A-tailing followed by reverse transcription with an oligo-dT primer to capture the cleavage status and sequences of both deoxyribozyme and RNA substrate. We validated DZ-seq by conventional analytical methods and demonstrated its utility by discovery of novel deoxyribozymes that allow for cleaving challenging RNA targets or the analysis of RNA modification states.

A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks

Due to the incomplete coverage and failure of traffic data collectors during the collection, traffic data usually suffers from information missing. Achieving accurate imputation is critical to the operation of transportation networks. Existing approaches usually focus on the characteristic analysis of temporal variation and adjacent spatial representation, and the consideration of higher-order spatial correlations and continuous data missing attracts more attentions from the academia and industry. In this paper, by leveraging motif-based graph aggregation, we propose a spatiotemporal imputation approach to address the issue of traffic data missing. First, through motif discovery, the higher-order graph aggregation model was presented in traffic networks. It utilized graph convolution network (GCN) to polymerize the correlated segment attributes of the missing data segments. Then, the multitime dimension imputation model based on bidirectional long short-term memory (Bi-LSTM) incorporated the recent, daily-periodic, and weekly-periodic dependencies of the historical data. Finally, the spatial aggregated values and the temporal fusion values were integrated to obtain the results. We conducted comprehensive experiments based on the real-world dataset and discussed the case of random and continuous data missing by different time intervals, and the results showed that the proposed approach was feasible and accurate.

Casting plate defect detection using motif discovery with minimal model training and small data sets

Casting plate defect detection using motif discovery with minimal model training and small data sets

Decoding gene regulation in the fly brain.

The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1-6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation.

Novel cis-regulatory elements as synthetic promoters to drive recombinant protein expression from the Chlamydomonas reinhardtii nuclear genome

Eukaryotic green microalgae represent a sustainable, photosynthetic biotechnology platform for generating high-value products. The model green alga Chlamydomonas reinhardtii has already been used to generate high value bioproducts such as recombinant proteins and terpenoids. However, low, unstable, and variable nuclear transgene expression has limited the ease and speed of metabolic engineering and recombinant protein expression in this system. Here, novel genetic devices for transgene expression in C. reinhardtii have been developed by identifying cis-regulatory DNA elements capable of driving high transgene expression in C. reinhardtii promoters using de novo motif discovery informatics approaches. Thirteen putative motifs were synthesized as concatemers, linked to a common minimal basal promoter, and assayed for their activity to drive expression of a yellow fluorescent protein reporter gene. Following transformation of the vectors into C. reinhardtii by electroporation, in vivo measurements of yellow fluorescent protein expression by flow cytometry revealed that five of the DNA motifs analyzed displayed significantly higher reporter expression compared to the basal promoter control. Two of the concatemerized motifs, despite being much smaller minimal cis-regulatory elements, drove reporter expression at levels approaching that of the conventionally-used AR1 promoter. This analysis provides insight into C. reinhardtii promoter structure and gene regulation, and provides a new toolbox of cis-regulatory elements that can be used to drive transgene expression at a variety of expression levels.

Large-Scale Analysis of RNA-Protein Interactions for Functional RNA Motif Discovery Using FOREST.

RNA transcripts can form a variety of higher-order structures. We developed a large-scale affinity analysis system, FOREST (Folded RNA Element Profiling with Structure Library), to investigate the function of these RNA structures on transcriptome-wide scale. Here we describe a protocol to analyze RNA-protein interactions using FOREST . Users of the protocol prepare an RNA structure library comprised of diverse species of transcripts and perform high-throughput characterization of the RNA-protein interactions to obtain quantitative and comprehensive information on the binding affinities and specificities. Moreover, we demonstrate how FOREST can be used to analyze a non-canonical structure, the RNA G-quadruplex, without sequencing bias, because the quantification is performed directly on a microarray without sequence amplification. FOREST will contribute to the discovery of RNA structure motifs that determine RNA-protein interactions.

Molecular tweezers - a new class of potent broad-spectrum antivirals against enveloped viruses.

A new supramolecular approach to broad spectrum antivirals utilizes host guest chemistry between molecular tweezers and lysine/arginine as well as choline. Basic amino acids in amyloid-forming SEVI peptides (semen-derived enhancers of viral infection) are included inside the tweezer cavity leading to disaggregation and neutralization of the fibrils, which lose their ability to enhance HIV-1/HIV-2 infection. Lipid head groups contain the trimethylammonium cation of choline; this is likewise bound by molecular tweezers, which dock onto viral membranes and thus greatly enhance their surface tension. Disruption of the envelope in turn leads to total loss of infectiosity (ZIKA, Ebola, Influenza). This complexation event also seems to be the structural basis for an effective inihibition of cell-to-cell spread in Herpes viruses. The article describes the discovery of novel molecular recognition motifs and the development of powerful antiviral agents based on these host guest systems. It explains the general underlying mechanisms of antiviral action and points to future optimization and application as therapeutic agents.

Physicochemical properties and homology studies of the floral meristem identity gene LFY in nonflowering and flowering plants.

In flowering plants, the LEAFY (LFY) gene controls floral meristem activity. In early land plants such as mosses and ferns, it, however, has a minimum role in cell division and development of diploid sporophyte. Homology modeling, an accurate and efficient protein structure prediction method, was used to construct a 3D model of the LEAFY protein in nonflowering and flowering plants. The present study examines the following species: Charophyte green algae, Physcomitrella, Ceratopteris, Picea, and Arabidopsis, as they are the popularly used model organisms for developmental studies. LEAFY protein sequences from the model organisms were aligned by multiple sequence alignment. 3D models of the LEAFY protein from all the model organisms was constructed using the PHYRE2 program with 100% confidence, and the constructed models were evaluated using the MolProbity tool. On the basis of the conserved regions, Charophyte green algae shared 38-46% sequence similarity with Physcomitrella sp., 37-46% similarity with Ceratopteris sp., 33-41% similarity with Picea sp., and 32-38% similarity with Arabidopsis sp. The Motif Finder server identified the protein family domain FLO_LFY and LFY_SAM, whose function is floral meristem development. Secondary structure prediction analysis indicated that the LEAFY protein belongs to the alpha (α) protein class, which is stable against mutation and thus limits structural changes in the LEAFY protein. The study findings reveal two distinct clusters of the LFY gene from the common ancestor green algae. One cluster is present in nonflowering plants that include mosses, pteridophytes, and gymnosperms, and the other cluster is present in flowering plants that include orchids, monocots, dicots, and angiosperms.

A Method for Predicting DNA Motif Length Based On Deep Learning.

A DNA motif is a sequence pattern shared by the DNA sequence segments that bind to a specific protein. Discovering motifs in a given DNA sequence dataset plays a vital role in studying gene expression regulation. As an important attribute of the DNA motif, the motif length directly affects the quality of the discovered motifs. How to determine the motif length more accurately remains a difficult challenge to be solved. We propose a new motif length prediction scheme named MotifLen by using supervised machine learning. First, a method of constructing sample data for predicting the motif length is proposed. Secondly, a deep learning model for motif length prediction is constructed based on the convolutional neural network. Then, the methods of applying the proposed prediction model based on a motif found by an existing motif discovery algorithm are given. The experimental results show that i) the prediction accuracy of MotifLen is more than 90% on the validation set and is significantly higher than that of the compared methods on real datasets, ii) MotifLen can successfully optimize the motifs found by the existing motif discovery algorithms, and iii) it can effectively improve the time performance of some existing motif discovery algorithms.