Expansion of signaling genes for adaptive immune system evolution in early vertebrates-0

Y) are arranged in a circle (blue line). Red lines connect the genomic map positions of the members in each subfamily: (A) JAK, Janus kinase; (B) SOS, son of sevenless homolog; (C) SHC, SHC (Src homology 2 domain containing)-transforming protein; and (D) VAV, oncogene. Gray lines connect the genomic map positions of the genes in each BV paralogous pair (a paralogous pair that were formed at the Base of the Vertebrate lineage) of paralogons. Additional File contains the genomic maps of all AIS subfamilies.<p><b>Copyright information:</b></p><p>Taken from "Expansion of signaling genes for adaptive immune system evolution in early vertebrates"</p><p>http://www.biomedcentral.com/1471-2164/9/218</p><p>BMC Genomics 2008;9():218-218.</p><p>Published online 14 May 2008</p><p>PMCID:PMC2391169.</p><p></p

Expansion of signaling genes for adaptive immune system evolution in early vertebrates-1

Ory. Asterisks indicate statistically significant enrichment of categories (< 0.05, calculated using hypergeometric distribution with Bonferroni correction). BM, bone marrow; MT, muscle tissue; RepO, reproductive organ; ResO, respiration organ; Epi, epithelium; IO, internal organ and metabolism system; AIS, adaptive immune system; II, innate immunity; NS, nervous system.<p><b>Copyright information:</b></p><p>Taken from "Expansion of signaling genes for adaptive immune system evolution in early vertebrates"</p><p>http://www.biomedcentral.com/1471-2164/9/218</p><p>BMC Genomics 2008;9():218-218.</p><p>Published online 14 May 2008</p><p>PMCID:PMC2391169.</p><p></p

Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations

The dimerization free energies of RNA–RNA duplexes are fundamental values that represent the structural stability of RNA complexes. We report a comparative analysis of RNA–RNA duplex dimerization free-energy changes upon mutations, estimated from a molecular dynamics simulation and experiments. A linear regression for nine pairs of double-stranded RNA sequences, six base pairs each, yielded a mean absolute deviation of 0.55 kcal/mol and an <i>R</i>² value of 0.97, indicating quantitative agreement between simulations and experimental data. The observed accuracy indicates that the molecular dynamics simulation with the current molecular force field is capable of estimating the thermodynamic properties of RNA molecules

Common secondary structure prediction (Problem 11).

<p>Common secondary structure prediction (Problem 11).</p

An evaluation process for Problem 10.

<p>The comparison between every pairwise alignment and the reference alignment is conducted using TP, TN, FP and FN with respect to the aligned-bases.</p

Improved Accuracy in RNA–Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA–protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA–protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA–protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA–protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein–protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA–protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA–protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA–protein complexes

Promising candidates.

a)<p>Genomic coordinate of ci2 genome (Mar. 2005 Assembly).</p

Cumulative allele frequency from 11 populations.

<p>In panel (a) we plot the allele percentage of new ORFs and (b) allele percentage of all HapMap data in UCSC Genome Browser. The percentage (-axis) in panel (b) is based on Allele1, chosen arbitrarily.</p

Schematic diagram of the approximated -type estimator (Definition 12).

<p>The estimator in the top figure shows the -centroid estimator with the marginalized probability distribution, and the one in the bottom figure shows its approximation.</p

Accuracy for detecting the core miRNA hairpins.

<p>(a) The accuracy of miRRim2 together with four previously performed computational predictions is shown. (b) The change of the accuracy when one type of features is excluded. BPP: base-pair potential, BPD: base-pair distance.</p