Simple Motifs Research Articles

Ranking Users in Social Networks with Motif-based PageRank

PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, which represent first-order relations between two connected nodes. It ignores higher-order relations that may exist between nodes. In this article, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order relations into the conventional PageRank computation. Motifs are subgraphs consisting of a small number of nodes. We use motifs to capture higher-order relations between nodes in a network and introduce two methods, one linear and one non-linear, to combine first-order and higher-order relations in PageRank computation. We conduct extensive experiments on three real-world networks, namely, DBLP, Epinions, and Ciao. We study different types of motifs, including 3-node simple and anchor motifs, 4-node and 5-node motifs. Besides using single motif, we also run MPR with ensemble of multiple motifs. We also design a learning task to evaluate the abilities of authority prediction with motif-based features. All experimental results demonstrate that MPR can significantly improve the performance of user ranking in social networks compared to the baseline methods.

Steric complementarity directs sequence promiscuous leader binding in RiPP biosynthesis

Enzymes that generate ribosomally synthesized and posttranslationally modified peptide (RiPP) natural products have garnered significant interest, given their ability to produce large libraries of chemically diverse scaffolds. Such RiPP biosynthetic enzymes are predicted to bind their corresponding peptide substrates through sequence-specific recognition of the leader sequence, which is removed after the installation of posttranslational modifications on the core sequence. The conservation of the leader sequence within a given RiPP class, in otherwise disparate precursor peptides, further supports the notion that strict sequence specificity is necessary for leader peptide engagement. Here, we demonstrate that leader binding by a biosynthetic enzyme in the lasso peptide class of RiPPs is directed by a minimal number of hydrophobic interactions. Biochemical and structural data illustrate how a single leader-binding domain can engage sequence-divergent leader peptides using a conserved motif that facilitates hydrophobic packing. The presence of this simple motif in noncognate peptides results in low micromolar affinity binding by binding domains from several different lasso biosynthetic systems. We also demonstrate that these observations likely extend to other RiPP biosynthetic classes. The portability of the binding motif opens avenues for the engineering of semisynthetic hybrid RiPP products.

Sampling-Tree Model: Efficient Implementation of Distributed Bayesian Inference in Neural Networks

Experimental observations from neuroscience have suggested that the cognitive process of human brain is realized as probabilistic reasoning and further modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented by network of neurons in the brain. Here a novel implementation of neural circuit, named the sampling-tree model, is proposed to fulfill this aim. By using a deep tree structure to implement sampling with simple and stackable basic neural network motifs for any given Bayesian networks, one can perform local inference while guaranteeing the accuracy of global inference. We show that these task-independent motifs can be used in parallel for fast inference without intensive iteration and scale-limitation. As a result, this model utilizes the structure benefit of neuronal system, i.e., neuronal abundance and multihierarchy, to perform fast inference in an extendable way.

Protein three-dimensional structures at the origin of life.

Proteins are relatively easy to synthesize, compared to nucleic acids and it is likely that there existed a stage prior to the RNA world which can be called the protein world. Some of the three-dimensional (3D) peptide structures in these proteins have, we argue, been conserved since then and may constitute the oldest biological relics in existence. We focus on 3D peptide motifs consisting of up to eight or so amino acid residues. The best known of these is the 'nest', a three- to seven-residue protein motif, which has the function of binding anionic atoms or groups of atoms. Ten per cent of amino acids in typical proteins belong to a nest, so it is a common motif. A five-residue nest is found as part of the well-known P-loop that is a recurring feature of many ATP or GTP-binding proteins and it has the function of binding the phosphate part of these ligands. A synthetic hexapeptide, ser-gly-ala-gly-lys-thr, designed to resemble the P-loop, has been shown to bind inorganic phosphate. Another type of nest binds iron-sulfur centres. A range of other simple motifs occur with various intriguing 3D structures; others bind cations or form channels that transport potassium ions; other peptides form catalytically active haem-like or sheet structures with certain transition metals. Amyloid peptides are also discussed. It now seems that the earliest polypeptides were far from being functionless stretches, and had many of the properties, both binding and catalytic, that might be expected to encourage and stabilize simple life forms in the hydrothermal vents of ocean depths.

PeSA: A software tool for peptide specificity analysis

The discovery and characterization of molecular interactions is crucial towards a better understanding of complex biological processes. Particularly protein-protein interactions (i.e., PPIs), which are responsible for a variety of cellular functions from intracellular signaling to enzyme-substrate specificity, have been studied broadly over the past decades. Position-specific scoring matrices (PSSM) in particular are used extensively to help determine interaction specificity or candidate interaction motifs at the residue level. However, not all studies successfully report their results as a candidate interaction motif. In many cases, this may be due to a lack of suitable tools for simple analysis and motif generation. Peptide Specificity Analyst (PeSA) was developed with the goal of filling this information gap and providing an easy to use software to aid peptide array analysis and motif generation. PeSA utilizes two models of motif creation: (1) frequency-based using a user-defined peptide list, and (2) weight-based using experimental binding results. The ability to produce motifs effortlessly will make studying, interpreting and disseminating peptide specificity results in an effortless and straightforward process.

Development of novel microsatellite markers for Alkanna tinctoriaby comparative transcriptomics.

Premise Alkanna tinctoria (Boraginaceae) is an important medicinal herb with its main distribution across the Mediterranean region. To reveal its genetic variation and population structure, microsatellite markers were developed and validated in four Greek populations.Methods and Results RNA‐Seq data of the related species Arnebia euchroma and Echium plantagineum were assembled and mined to identify conserved ortholog sets containing simple sequence repeat motifs. Fifty potential loci were identified and then tested on A. tinctoria, of which 17 loci were polymorphic. The number of alleles ranged from one to nine, and the levels of observed and expected heterozygosity ranged from 0.000 to 1.000 and 0.000 to 0.820, respectively. Most of these loci could be successfully amplified in eight other species of Boraginaceae (Alkanna graeca, A. hellenica, A. sfikasiana, Echium vulgare, E. plantagineum, Lithospermum officinale, Borago officinalis, and Anchusa officinalis).ConclusionsThis study provides the first set of microsatellite loci for studying the genetic variation and population structure of A. tinctoria and shows their potential usefulness in other Boraginaceae species.

On structural and dynamical factors determining the integrated basin instability of power-grid nodes.

In electric power systems delivering alternating current, it is essential to maintain its synchrony of the phase with the rated frequency. The synchronization stability that quantifies how well the power-grid system recovers its synchrony against perturbation depends on various factors. As an intrinsic factor that we can design and control, the transmission capacity of the power grid affects the synchronization stability. Therefore, the transition pattern of the synchronization stability with the different levels of transmission capacity against external perturbation provides the stereoscopic perspective to understand the synchronization behavior of power grids. In this study, we extensively investigate the factors affecting the synchronization stability transition by using the concept of basin stability as a function of the transmission capacity. For a systematic approach, we introduce the integrated basin instability, which literally adds up the instability values as the transmission capacity increases. We first take simple 5-node motifs as a case study of building blocks of power grids, and a more realistic IEEE 24-bus model to highlight the complexity of decisive factors. We find that both structural properties such as gate keepers in network topology and dynamical properties such as large power input/output at nodes cause synchronization instability. The results suggest that evenly distributed power generation and avoidance of bottlenecks can improve the overall synchronization stability of power-grid systems.

Understanding the behavior of stutter through the sequencing of STR alleles

This work explores the influence of several variables on stutter formation across sequenced autosomal STR loci (simple, compound, and complex motifs) and different alleles within each locus. The variables are sequence variations within the repeating motifs and flanking region [1,2]; longest uninterrupted stretch (LUS) [3]; parental allele length [3]; and base pair content and length value of each repeating motif from which the stutter has generated [3,4]. Over six hundred unrelated individuals from different populations were amplified with the prototype PowerSeq 46GY System and sequenced on the Illumina MiSeq platform. Raw FASTQ files were analyzed with STRait Razor v3 [5]. Stutter ratio was calculated for motifs that exhibited stutter using the ratio of the observed coverage of the stutter sequence at (N-1) position to the observed coverage of the allelic sequence. Understanding the behavior (abundance, reproducibility, sequence context) of non-allelic artifacts will help in establishing probabilistic models for the prediction of stutter rate and interpretation of sequence-based STR profiles.

ON selectivity in the Drosophila visual system is a multisynaptic process involving both glutamatergic and GABAergic inhibition.

Sensory systems sequentially extract increasingly complex features. ON and OFF pathways, for example, encode increases or decreases of a stimulus from a common input. This ON/OFF pathway split is thought to occur at individual synaptic connections through a sign-inverting synapse in one of the pathways. Here, we show that ON selectivity is a multisynaptic process in the Drosophila visual system. A pharmacogenetics approach demonstrates that both glutamatergic inhibition through GluClα and GABAergic inhibition through Rdl mediate ON responses. Although neurons postsynaptic to the glutamatergic ON pathway input L1 lose all responses in GluClα mutants, they are resistant to a cell-type-specific loss of GluClα. This shows that ON selectivity is distributed across multiple synapses, and raises the possibility that cell-type-specific manipulations might reveal similar strategies in other sensory systems. Thus, sensory coding is more distributed than predicted by simple circuit motifs, allowing for robust neural processing.

Gene networks that compensate for crosstalk with crosstalk

Crosstalk is a major challenge to engineering sophisticated synthetic gene networks. A common approach is to insulate signal-transduction pathways by minimizing molecular-level crosstalk between endogenous and synthetic genetic components, but this strategy can be difficult to apply in the context of complex, natural gene networks and unknown interactions. Here, we show that synthetic gene networks can be engineered to compensate for crosstalk by integrating pathway signals, rather than by pathway insulation. We demonstrate this principle using reactive oxygen species (ROS)-responsive gene circuits in Escherichia coli that exhibit concentration-dependent crosstalk with non-cognate ROS. We quantitatively map the degree of crosstalk and design gene circuits that introduce compensatory crosstalk at the gene network level. The resulting gene network exhibits reduced crosstalk in the sensing of the two different ROS. Our results suggest that simple network motifs that compensate for pathway crosstalk can be used by biological networks to accurately interpret environmental signals.

Transcriptome sequencing of okra (Abelmoschus esculentus L. Moench) uncovers differently expressed genes responding to drought stress

Drought affects the growth and development of plants and can cause crop failure. As an important edible and medicinal plant, okra (Abelmoschus esculentus L. Moench) is very popular in China and shows strong resistance to drought. In this study, gene expression profiles of okra leaves were investigated under water-limited conditions at the seedling stage. A total of 94,769 unigenes with an average length of 1921 bp and an N90 of 981 bp were obtained through next-generation sequencing. Furthermore, 38.18% of total unigenes were annotated in NT, NR, COG, KEGG, and SwissProt databases. A total of 2276, 5626, 11,719, 14,892, 855, 4593, and 5170 differentially expressed genes (DEGs) in seven pairwise comparisons (A5 vs. A0; A7 vs. A0; A15 vs. A0; A20 vs. A0; A7 vs. A5; A15 vs. A7, and A20 vs. A15) were identified. Among these, 22,751 DEGs were up-regulated, and the number was slightly more than that of the down-regulated DEGs. The number of up-regulated DEGs among different samples increased with increasing drought stress. We identified that the majority of DEGs were related to “integral component of membrane”, “oxidation–reduction process”, and “metal ion binding” categories. Pathway analysis revealed that most DEGs were enriched in “Biosynthesis of secondary metabolites”, “Carbon metabolism”, and “Glycolysis/Gluconeogenesis pathways”. Additionally, a total of 17,456 simple sequence repeat motifs, 47,9831 single nucleotide polymorphisms, and 3746 transcription factors were identified in this study. RNA-Seq data were also verified by RT-qPCR. Our findings engender a further understanding of molecular mechanisms in response to drought stress.

Modular Design of Small Enzymatic Reaction Networks Based on Reversible and Cleavable Inhibitors.

Systems chemistry aims to mimic the functional behavior of living systems by constructing chemical reaction networks with well-defined dynamic properties. Enzymes can play a key role in such networks, but there is currently no general and scalable route to the design and construction of enzymatic reaction networks. Here, we introduce reversible, cleavable peptide inhibitors that can link proteolytic enzymatic activity into simple network motifs. As a proof-of-principle, we show auto-activation topologies producing sigmoidal responses in enzymatic activity, explore cross-talk in minimal systems, design a simple enzymatic cascade, and introduce non-inhibiting phosphorylated peptides that can be activated using a phosphatase.

Modular Design of Small Enzymatic Reaction Networks Based on Reversible and Cleavable Inhibitors

AbstractSystems chemistry aims to mimic the functional behavior of living systems by constructing chemical reaction networks with well‐defined dynamic properties. Enzymes can play a key role in such networks, but there is currently no general and scalable route to the design and construction of enzymatic reaction networks. Here, we introduce reversible, cleavable peptide inhibitors that can link proteolytic enzymatic activity into simple network motifs. As a proof‐of‐principle, we show auto‐activation topologies producing sigmoidal responses in enzymatic activity, explore cross‐talk in minimal systems, design a simple enzymatic cascade, and introduce non‐inhibiting phosphorylated peptides that can be activated using a phosphatase.

Systematic minimization of RNA ligase ribozyme through large-scale design-synthesis-sequence cycles.

Template-directed RNA ligation catalyzed by an RNA enzyme (ribozyme) is a plausible and important reaction that could have been involved in transferring genetic information during prebiotic evolution. Laboratory evolution experiments have yielded several classes of ligase ribozymes, but their minimal sequence requirements remain largely unexplored. Because selection experiments strongly favor highly active sequences, less active but smaller catalytic motifs may have been overlooked in these experiments. We used large-scale DNA synthesis and high-throughput ribozyme assay enabled by deep sequencing to systematically minimize a previously laboratory-evolved ligase ribozyme. After designing and evaluating >10 000 sequences, we identified catalytic cores as small as 18 contiguous bases that catalyze template-directed regiospecific RNA ligation. The fact that such a short sequence can catalyze this critical reaction suggests that similarly simple or even simpler motifs may populate the RNA sequence space which could have been accessible to the prebiotic ribozymes.

Novel algorithms for LDD motif search

BackgroundMotifs are crucial patterns that have numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity between families of proteins, etc. Several motif models have been proposed in the literature. The (l,d)-motif model is one of these that has been studied widely. However, this model will sometimes report too many spurious motifs than expected. We interpret a motif as a biologically significant entity that is evolutionarily preserved within some distance. It may be highly improbable that the motif undergoes the same number of changes in each of the species. To address this issue, in this paper, we introduce a new model which is more general than (l,d)-motif model. This model is called (l,d1,d2)-motif model (LDDMS) and is NP-hard as well. We present three elegant as well as efficient algorithms to solve the LDDMS problem, i.e., LDDMS1, LDDMS2 and LDDMS3. They are all exact algorithms.ResultsWe did both theoretical analyses and empirical tests on these algorithms. Theoretical analyses demonstrate that our algorithms have less computational cost than the pattern driven approach. Empirical results on both simulated datasets and real datasets show that each of the three algorithms has some advantages on some (l,d1,d2) instances.ConclusionsWe proposed LDDMS model which is more practically relevant. We also proposed three exact efficient algorithms to solve the problem. Besides, our algorithms can be nicely parallelized. We believe that the idea in this new model can also be extended to other motif search problems such as Edit-distance-based Motif Search (EMS) and Simple Motif Search (SMS).

Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression

It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles' heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input-output function describing how the level of expression depends upon the parameters of the regulated gene-for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles' heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life.

Bis-Porphyrin Structural Motiff for Chirality and Molecular Sensing

Bis-porphyrin structures are widely used as model systems to mimic various natural processes and have numerous practical applications in the research and technoligical fields such as polymer and nano science, light harvesting and nonlinear optics, chemical sensors and catalysis, molecular and chiral recognition, various supramolecular and chiroptical devices, chirogenesis and absolute configuration determination. Several years ago, we have discovered that a simple ethane-bridged bis-porphyrin structural motif (M = Zn, Mg, Cu, Co, 2H), can be effectively served for different application purposes owing to the specific molecular and supramolecular functionalities. In particular, the unique functional property of this bis-porphyrin host is based upon the structural semi-flexibility of covalent linkage between two porphyrin unitsresulting in the environmentally assisted syn-anti conformational switching and tweezer formation depending upon the guest structure, which can be effectively employed in different sensing areas, such as chirogenic probes, chiral recognitions, and chemical sensors .1-13 Further developments and prospects toward new chiral and achiral bis- and multi porphyrin systems on the basis of heterometallic bis- and multi-porphyrin structures will be discussed. Borovkov, Symmetry 2014, 6, 256. Borovkov, Symmetry 2010, 2, 184. Borovkov, Y. Inoue, Eur. J. Org. Chem. 2009, 189. A. Hembury, V. V. Borovkov, Y. Inoue, Chem. Rev. 2008, 108, 1. V. Borovkov, Y. Inoue, Top. Curr. Chem. 2006, 265, 89. V. Borovkov, G. A. Hembury, Y. Inoue, Acc. Chem. Res. 2004, 37, 449.Giancane, V. Borovkov, Y. Inoue, L. Valli, J. Colloid Interface Sci. 2012, 385, 282.Giancane, V. Borovkov, Y. Inoue, S. Conoci, L. Valli,Soft Matter 2013, 9, 2302.Bettini, E. Maglie, R. Pagano, V. Borovkov, Y. Inoue, L. Valli, G. Giancane, Beilstein J. Nanotechnol. 2015, 6, 2154.Colombelli, M.G. Manera, V. Borovkov, G. Giancane, L. Valli, R. Rella, Sensor Actuat. B-Chem. 2017, 246, 1039. Buccolieri, D. Manno, A. Serra, A. Santino, M. Hasan, V. Borovkov, G. Giancane,Sensor Actuat. B-Chem. 2018, 257, 685.Buccolieri, M. Hasan, S. Bettini, V. Bonfrate, L. Salvatore, A.Santino, V. Borovkov, G.,Giancane, Anal. Chem. 2018, 90, 6952.Bettini, R. Pagano, V. Borovkov, G. Giancane , L. Valli, J. Colloid Interface Sci. 2019, 533, 762-770.

DNA-Based Photoacoustic Nanosensor for Interferon Gamma Detection.

Tracking protein levels in the body is vital in both research and medicine, where understanding their physiological roles provides insight into their regulation in homeostasis and diseases. In medicine, protein levels are actively sampled since they continuously fluctuate, reflecting the status of biological systems and provide insight into patient health. One such protein is interferon gamma, a clinically relevant protein with immunoregulatory functions that play critical roles against infection. New tools for continuously monitoring protein levels in vivo are invaluable in monitoring real-time conditions of patients to allow better care. Here, we developed a DNA-based nanosensor for the photoacoustic detection of interferon gamma. This work demonstrates how we transformed a simple DNA motif, receptors, and a novel phthalocyanine dye into a proof-of-concept photoacoustic nanosensor for protein detection. Surface plasmon resonance kinetic analysis demonstrated that the nanosensor is responsive and reversible to interferon gamma with an affinity in the nanomolar range, KD1 = 167 nM and KD2 = 316 nM. As a reporter, our design includes a novel phthalocyanine-based photoacoustic dye that stacks in a J-aggregate, causing a 22.5% increase in signal. Upon receptor binding, the DNA structure bends to induce phthalocyanine dye stacking, resulting in a 55% increase in photoacoustic signal in the presence of 10 μM interferon gamma. This proof-of-concept nanosensor is a novel approach to the development of a photoacoustic sensor and may be adapted for other proteins of interest in the future for in vivo tracking.

Maximizing accuracy of RNA structure in refinement against residual dipolar couplings.

Structural information about ribonucleic acid (RNA) is lagging behind that of proteins, in part due to its high charge and conformational variability. Molecular dynamics (MD) has played an important role in describing RNA structure, complementing information from both nuclear magnetic resonance (NMR), or X-ray crystallography. We examine the impact of the choice of the empirical force field for RNA structure refinement using cross-validation against residual dipolar couplings (RDCs) as structural accuracy reporter. Four force fields, representing both the state-of-the art in RNA simulation and the most popular selections in NMR structure determination, are compared for a prototypical A-RNA helix. RNA structural accuracy is also evaluated as a function of both density and nature of input NMR data including RDCs, anisotropic chemical shifts, and distance restraints. Our results show a complex interplay between the experimental restraints and the force fields indicating two best-performing choices: high-fidelity refinement in explicit solvent, and the conformational database-derived potentials. Accuracy of RNA models closely tracks the density of 1-bond C-H RDCs, with other data types having beneficial, but smaller effects. At lower RDC density, or when refining against NOEs only, the two selected force fields are capable of accurately describing RNA helices with little or no experimental RDC data, making them available for the higher order structure assembly or better quantification of the intramolecular dynamics. Unrestrained simulations of simple RNA motifs with state-of-the art MD force fields appear to capture the flexibility inherent in nucleic acids while also maintaining a good agreement with the experimental observables.

Enhanced mammalian genome editing by new Cas12a orthologs with optimized crRNA scaffolds

CRISPR-Cas12a/Cpf1, a single RNA-guided endonuclease system, provides a promising tool for genome engineering. However, only three Cas12a orthologs have been employed for mammalian genome editing, and the editing efficiency as well as targeting coverage still requires improvements. Here, we harness six novel Cas12a orthologs for genome editing in human and mouse cells, some of which utilize simple protospacer adjacent motifs (PAMs) that remarkably increase the targeting range in the genomes. Moreover, we identify optimized CRISPR RNA (crRNA) scaffolds that can increase the genome editing efficiency of Cas12a.