Stochastic Binary Modeling of Cells in Continuous Time as an Alternative to Biochemical Reaction Equations

We have developed a coarse-grained formulation for modeling the dynamic behavior of cells quantitatively, based on stochasticity and heterogeneity, rather than on biochemical reactions. We treat each reaction as a continuous-time stochastic process, while reducing each biochemical quantity to a binary value at the level of individual cells. The system can be analytically represented by a finite set of ordinary linear differential equations, which provides a continuous time course prediction of each molecular state. In this letter, we introduce our formalism and demonstrate it with several examples.Comment: 10pages, 3 figure

GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures

BACKGROUND: We introduce GASH, a new, publicly accessible program for structural alignment and superposition. Alignments are scored by the Number of Equivalent Residues (NER), a quantitative measure of structural similarity that can be applied to any structural alignment method. Multiple alignments are optimized by conjugate gradient maximization of the NER score within the genetic algorithm framework. Initial alignments are generated by the program Local ASH, and can be supplemented by alignments from any other program. RESULTS: We compare GASH to DaliLite, CE, and to our earlier program Global ASH on a difficult test set consisting of 3,102 structure pairs, as well as a smaller set derived from the Fischer-Eisenberg set. The extent of alignment crossover, as well as the completeness of the initial set of alignments are examined. The quality of the superpositions is evaluated both by NER and by the number of aligned residues under three different RMSD cutoffs (2,4, and 6Å). In addition to the numerical assessment, the alignments for several biologically related structural pairs are discussed in detail. CONCLUSION: Regardless of which criteria is used to judge the superposition accuracy, GASH achieves the best overall performance, followed by DaliLite, Global ASH, and CE. In terms of CPU usage, DaliLite CE and GASH perform similarly for query proteins under 500 residues, but for larger proteins DaliLite is faster than GASH or CE. Both an http interface and a simple object application protocol (SOAP) interface to the GASH program are available at

Prediction of dinucleotide-specific RNA-binding sites in proteins

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA–protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach.</p> <p>Results</p> <p>We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%.</p> <p>Conclusions</p> <p>Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at: <url>http://tardis.nibio.go.jp/netasa/srcpred</url>.</p

Structural Determinants of the APOBEC3G N-Terminal Domain for HIV-1 RNA Association

APOBEC3G (A3G) is a cellular protein that inhibits HIV-1 infection through virion incorporation. The interaction of the A3G N-terminal domain (NTD) with RNA is essential for A3G incorporation in the HIV-1 virion. The interaction between A3G-NTD and RNA is not completely understood. The A3G-NTD is also recognized by HIV-1 Viral infectivity factor (Vif) and A3G-Vif binding leads to A3G degradation. Therefore, the A3G-Vif interaction is a target for the development of antiviral therapies that block HIV-1 replication. However, targeting the A3G-Vif interactions could disrupt the A3G-RNA interactions that are required for A3G's antiviral activity. To better understand A3G-RNA binding, we generated in silico docking models to simulate the RNA-binding propensity of A3G-NTD. We simulated the A3G-NTD residues with high RNA-binding propensity, experimentally validated our prediction by testing A3G-NTD mutations, and identified structural determinants of A3G-RNA binding. In addition, we found a novel amino acid residue, I26 responsible for RNA interaction. The new structural insights provided here will facilitate the design of pharmaceuticals that inhibit A3G-Vif interactions without negatively impacting A3G-RNA interactions

Fast and accurate prediction of protein side-chain conformations

Summary: We developed a fast and accurate side-chain modeling program [Optimized Side Chain Atomic eneRgy (OSCAR)-star] based on orientation-dependent energy functions and a rigid rotamer model. The average computing time was 18 s per protein for 218 test proteins with higher prediction accuracy (1.1% increase for χ1 and 0.8% increase for χ1+2) than the best performing program developed by other groups. We show that the energy functions, which were calibrated to tolerate the discrete errors of rigid rotamers, are appropriate for protein loop selection, especially for decoys without extensive structural refinement

Malt1-Induced Cleavage of Regnase-1 in CD4+ Helper T Cells Regulates Immune Activation

SummaryRegnase-1 (also known as Zc3h12a and MCPIP1) is an RNase that destabilizes a set of mRNAs, including Il6 and Il12b, through cleavage of their 3′ UTRs. Although Regnase-1 inactivation leads to development of an autoimmune disease characterized by T cell activation and hyperimmunoglobulinemia in mice, the mechanism of Regnase-1-mediated immune regulation has remained unclear. We show that Regnase-1 is essential for preventing aberrant effector CD4+ T cell generation cell autonomously. Moreover, in T cells, Regnase-1 regulates the mRNAs of a set of genes, including c-Rel, Ox40, and Il2, through cleavage of their 3′ UTRs. Interestingly, T cell receptor (TCR) stimulation leads to cleavage of Regnase-1 at R111 by Malt1/paracaspase, freeing T cells from Regnase-1-mediated suppression. Furthermore, Malt1 protease activity is critical for controlling the mRNA stability of T cell effector genes. Collectively, these results indicate that dynamic control of Regnase-1 expression in T cells is critical for controlling T cell activation