Tertiary Motifs Revealed in Analyses of Higher-Order RNA Junctions

RNA junctions are secondary-structure elements formed when three or more helices come together. They are present in diverse RNA molecules with various fundamental functions in the cell. To better understand the intricate architecture of three-dimensional (3D) RNAs, we analyze currently solved 3D RNA junctions in terms of base-pair interactions and 3D configurations. First, we study base-pair interaction diagrams for solved RNA junctions with 5 to 10 helices and discuss common features. Second, we compare these higher-order junctions to those containing 3 or 4 helices and identify global motif patterns such as coaxial stacking and parallel and perpendicular helical configurations. These analyses show that higher-order junctions organize their helical components in parallel and helical configurations similar to lower-order junctions. Their sub-junctions also resemble local helical configurations found in three- and four-way junctions and are stabilized by similar long-range interaction preferences such as A-minor interactions. Furthermore, loop regions within junctions are high in adenine but low in cytosine, and in agreement with previous studies, we suggest that coaxial stacking between helices likely forms when the common single-stranded loop is small in size; however, other factors such as stacking interactions involving noncanonical base pairs and proteins can greatly determine or disrupt coaxial stacking. Finally, we introduce the ribo-base interactions: when combined with the along-groove packing motif, these ribo-base interactions form novel motifs involved in perpendicular helix-helix interactions. Overall, these analyses suggest recurrent tertiary motifs that stabilize junction architecture, pack helices, and help form helical configurations that occur as sub-elements of larger junction networks. The frequent occurrence of similar helical motifs suggest nature's finite and perhaps limited repertoire of RNA helical conformation preferences. More generally, studies of RNA junctions and tertiary building blocks can ultimately help in the difficult task of RNA 3D structure prediction.     

Computational Identification of Post Translational Modification Regulated RNA Binding Protein Motifs

RNA and its associated RNA binding proteins (RBPs) mitigate a diverse array of cellular functions and phenotypes. The interactions between RNA and RBPs are implicated in many roles of biochemical processing by the cell such as localization, protein translation, and RNA stability. Recent discoveries of novel mechanisms that are of significant evolutionary advantage between RBPs and RNA include the interaction of the RBP with the 3’ and 5’ untranslated region (UTR) of target mRNA. These mechanisms are shown to function through interaction of a trans-factor (RBP) and a cis-regulatory element (3’ or 5’ UTR) by the binding of a RBP to a regulatory-consensus nucleic acid motif region that is conserved throughout evolution. Through signal transduction, regulatory RBPs are able to temporarily dissociate from their target sites on mRNAs and induce translation, typically through a post-translational modification (PTM). These small, regulatory motifs located in the UTR of mRNAs are subject to a loss-of-function due to single polymorphisms or other mutations that disrupt the motif and inhibit the ability to associate into the complex with RBPs. The identification of a consensus motif for a given RBP is difficult, time consuming, and requires a significant degree of experimentation to identify each motif-containing gene on a genomic scale. We have developed a computational algorithm to analyze high-throughput genomic arrays that contain differential binding induced by a PTM for a RBP of interest–RBP-PTM Target Scan (RPTS). We demonstrate the ability of this application to accurately predict a PTM-specific binding motif to an RBP that has no antibody capable of distinguishing the PTM of interest, negating the use of in-vitro exonuclease digestion techniques.     

植物病毒协生作用及其分子机理

植物病毒协生作用分布广,是造成农作物减产的重要原因之一.抗病毒转基因植物中协生作用的出现,严重限制了基因工程植物的商品化生产.本文对植物病毒协生作用的类型和特点、协生作用中病毒与病毒、病毒与环境间的相互作用及其分子机制进行了阐述.     

植物病毒协生作用及其分子机理

植物病毒协生作用分布广,是造成农作物减产的重要原因之一,抗病毒转基因植物中协生作用的出现,严重限制了基因工程植物的商品化生产。本文对植物病毒协生作用的类型和特点、协生作用中病毒与病毒、病毒与环境间的相互作用及其分子机制进行了阐述。     

Identifying Network Motifs that Buffer Front-to-Back Signaling in Polarized Neutrophils

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Discrete Molecular Dynamics Can Predict Helical Prestructured Motifs in Disordered Proteins

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available.     

Molecular Dynamics Study of Intrinsic Stability in Six RNA Terminal Loop Motifs

Abstract

Single stranded RNA molecules can assume a wide range of tertiary structures beyond the canonical A-form double helix. Certain sequences, termed motifs, are more common than a random distribution would suggest. The existence of such motifs can be rationalized in structural terms. In this study, we have investigated the intrinsic structural stability of RNA terminal loop motifs using multiple MD simulations in explicit water. Representative loops were chosen from the major tetraloop motifs, including also the U-turn motif. Not all loops retain their folded starting structure, but lowering the temperature to 277 K, or adding adjacent base pairs from the stem to which the motif is attached, helps stabilizing the folded loop structure.     

Computational Identification of Potential Molecular Interactions in Arabidopsis

Knowledge of the protein interaction network is useful to assist molecular mechanism studies. Several major repositories have been established to collect and organize reported protein interactions. Many interactions have been reported in several model organisms, yet a very limited number of plant interactions can thus far be found in these major databases. Computational identification of potential plant interactions, therefore, is desired to facilitate relevant research. In this work, we constructed a support vector machine model to predict potential Arabidopsis (Arabidopsis thaliana) protein interactions based on a variety of indirect evidence. In a 100-iteration bootstrap evaluation, the confidence of our predicted interactions was estimated to be 48.67%, and these interactions were expected to cover 29.02% of the entire interactome. The sensitivity of our model was validated with an independent evaluation data set consisting of newly reported interactions that did not overlap with the examples used in model training and testing. Results showed that our model successfully recognized 28.91% of the new interactions, similar to its expected sensitivity (29.02%). Applying this model to all possible Arabidopsis protein pairs resulted in 224,206 potential interactions, which is the largest and most accurate set of predicted Arabidopsis interactions at present. In order to facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource, with detailed annotations and more specific per interaction confidence measurements. This database and related documents are freely accessible at http://www.cls.zju.edu.cn/pair/.The complex cellular functions of an organism rely on physical interactions between proteins. Deciphering the protein-protein interaction network to understand higher level phenotypes and their regulations is always a major focus of both experimental biologists and computational biologists. A number of high-throughput (HTP) assays have been developed to identify in vitro protein interactions from several model organisms (Uetz et al., 2000; Giot et al., 2003; Li et al., 2004). A number of initiatives, such as IntAct (Kerrien et al., 2006), Molecular INTeraction database (Chatr-aryamontri et al., 2007), the Database of Interacting Proteins (Salwinski et al., 2004), Biomolecular Interaction Network Database (BIND; Alfarano et al., 2005), and BioGRID (Stark et al., 2006), have been established to systematically collect and organize the interaction data reported by both proteome-scale HTP experiments and traditional low-throughput studies focusing on individual proteins or pathways.Arabidopsis (Arabidopsis thaliana) has long been studied as a model organism to investigate the physiology, biochemistry, growth, development, and metabolism of a flowering plant at the molecular level. The molecular mechanism studies of various phenotypes and their regulations in Arabidopsis may be facilitated by a comprehensive reference protein interaction network, based on which working hypotheses could be invented with more guidance and confidence. However, due to technological limitations, most experimentally reported protein interactions in available databases were from other organisms. A very limited number of plant interactions could be found in these databases. Therefore, an accurate prediction of the Arabidopsis interactome would be valuable to assist relevant research.Studies on the computational identification of potential interactions started along with the advent of HTP interaction-detection technologies, which often produced a large number of false positives (Deane et al., 2002). Indirect evidence of protein interaction (e.g. protein colocalization and relevance in function) were hence introduced to boost the confidence of HTP results (Jansen et al., 2003). Further investigations demonstrated that direct inference of protein interactions from such indirect evidence alone was possible (Scott and Barton, 2007). The accuracy and effectiveness of using indirect evidence to predict interactions have also been thoroughly assessed (Qi et al., 2006; Suthram et al., 2006). These works offered precious insights into how protein interactions may be predicted accurately on a proteomic scale. In other organisms such as Homo sapiens, the prediction of an entire interactome has already been proven applicable and useful (Rhodes et al., 2005).On the other side, several efforts have been made to collect and organize a comprehensive map of Arabidopsis molecular interactions. For instances, around 20,000 interactions were inferred by homology to known interactions in other organisms (Geisler-Lee et al., 2007). Another work predicted 23,396 interactions based on multiple indirect data and curated 4,666 interactions from the literature and enzyme complexes (Cui et al., 2008). The Arabidopsis reactome database was established describing the functions of 2,195 proteins with 8,269 reactions in 318 superpathways (Tsesmetzis et al., 2008). And a general interaction database, IntAct (Kerrien et al., 2006), had allocated a special unit actively curating all plant protein interactions from literature and submitted data sets, which now contains 2,649 Arabidopsis interactions. However, in yeast, approximately 18,000 protein-protein interactions had been estimated for approximately 6,000 genes (Yu et al., 2008). Assuming the same rate of interaction, approximately 200,000 protein interactions would be expected for approximately 20,000 Arabidopsis genes. Therefore, the current collection of Arabidopsis interactions is still significantly limited. Moreover, most previous prediction works did not provide rigorous confidence measurements for their predicted interactions, which further limited their scope of applications.Recent advances in statistical learning presented a powerful algorithm, support vector machine (SVM), which may be used to predict interactions based on multiple indirect data. Although the basis of SVM had been laid in the 1960s, the idea of SVM was only officially proposed in the 1990s by Vapnik (1998, 2000). Then, research on its theoretical and application aspects thrived. It has been applied in a wide range of problems, including text categorization (de Vel et al., 2001; Kim et al., 2001), image classification and object detection (Ben-Yacoub et al., 1999; Karlsen et al., 2000), flood stage forecasting (Liong and Sivapragasam, 2002), microarray gene expression data analysis (Brown et al., 2000), drug design (Zhao et al., 2006a, 2006b), protein solvent accessibility prediction (Yuan et al., 2002), and protein fold prediction (Ding and Dubchak, 2001; Hua and Sun, 2001). Many studies have demonstrated that SVM was consistently superior to other supervised learning methods (Brown et al., 2000; Burbidge et al., 2001; Cai et al., 2003).In this work, with careful preparation of example data and selection of indirect evidence, we constructed an SVM model to predict potential Arabidopsis interactions. False positives were tightly controlled. With the high-confidence model, we identified altogether 224,206 potential interactions, which were expected to be 48.67% accurate and to cover 29.02% of the entire Arabidopsis interactome. More specific confidence measurements were also assigned on a per interaction basis. To facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource (PAIR; http://www.cls.zju.edu.cn/pair/), featuring detailed annotations and a friendly user interface.     

稻瘟病菌含信号肽小蛋白的计算机分析

结合计算机技术和生物信息学的方法,采用组合的信号肽分析软件SignalPv3.0、TargetPv1.1、Big-PIpredictor、TMHMMv2.0和SecretomeP对已公布的1486个稻瘟菌(magnaporthegrisea)小蛋白基因的N-端氨基酸序列进行信号肽分析,同时系统分析了信号肽的类型及结构。分析结果表明,在1486个稻瘟病菌小蛋白中,119个具有N-端信号肽的典型分泌蛋白。其中116个具有分泌型信号肽,1个具RR-motif型信号肽,2个具信号肽酶II型信号肽。在稻瘟病菌基因组中,分泌型小蛋白的序列是高度趋异的,仅出现少数氨基酸组成完全一致的信号肽,为进一步确认具有相同信号肽的分泌蛋白是否具有同源性,分别用BLAST2SEQUENCES对具有相同信号肽的分泌蛋白进行了序列对比。结果表明,具有相同信号肽的分泌蛋白同源性非常高。同时还采用Sublocv1.0对1486个小蛋白的亚细胞位置进行了预测,结果显示小蛋白的可能功能场所包括细胞质、细胞外、线立体和细胞核,功能场所位于细胞核的小蛋白是最多的。     

Computational and Functional Analyses of a Small-Molecule Binding Site in ROMK

The renal outer medullary potassium channel (ROMK, or Kir1.1, encoded by KCNJ1) critically regulates renal tubule electrolyte and water transport and hence blood volume and pressure. The discovery of loss-of-function mutations in KCNJ1 underlying renal salt and water wasting and lower blood pressure has sparked interest in developing new classes of antihypertensive diuretics targeting ROMK. The recent development of nanomolar-affinity small-molecule inhibitors of ROMK creates opportunities for exploring the chemical and physical basis of ligand-channel interactions required for selective ROMK inhibition. We previously reported that the bis-nitro-phenyl ROMK inhibitor VU591 exhibits voltage-dependent knock-off at hyperpolarizing potentials, suggesting that the binding site is located within the ion-conduction pore. In this study, comparative molecular modeling and in silico ligand docking were used to interrogate the full-length ROMK pore for energetically favorable VU591 binding sites. Cluster analysis of 2498 low-energy poses resulting from 9900 Monte Carlo docking trajectories on each of 10 conformationally distinct ROMK comparative homology models identified two putative binding sites in the transmembrane pore that were subsequently tested for a role in VU591-dependent inhibition using site-directed mutagenesis and patch-clamp electrophysiology. Introduction of mutations into the lower site had no effect on the sensitivity of the channel to VU591. In contrast, mutations of Val¹⁶⁸ or Asn¹⁷¹ in the upper site, which are unique to ROMK within the Kir channel family, led to a dramatic reduction in VU591 sensitivity. This study highlights the utility of computational modeling for defining ligand-ROMK interactions and proposes a mechanism for inhibition of ROMK.     

Network Class Superposition Analyses

Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces'' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., for the yeast cell cycle process [1]), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix , which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with . We show how to generate Derrida plots based on . We show that -based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on . We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for , for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.     

Engineering of a Polydisperse Small Heat-Shock Protein Reveals Conserved Motifs of Oligomer Plasticity

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Computational Molecular Biology(CMB)

<正>Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by BioP ublisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to genomes,using statistical,mathematical,and computational methods as well as new development of     

Computational Molecular Biology(CMB)

<正>Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by BioP ublisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to genomes,using statistical,mathematical,and computational methods as well as new development of     

Computational Molecular Biology(CMB)

正Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to     

Computational Molecular Biology(CMB)

正Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to genomes,using statistical,mathematical,and computational methods as well as new     

Computational Molecular Biology (CMB)

正Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to     

Computational Molecular Biology(CMB)

正Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to     

Computational Molecular Biology(CMB)

正Computational Molecular Biology(ISSN 1927-5587)is an open access,peer reviewed journal published online by Bio Publisher.The Journal is publishing all the latest and outstanding research articles,letters,methods,and reviews in all areas of Computational Molecular Biology,covering new discoveries in molecular biology,from genes to