52 research outputs found
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><sup>2</sup> 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
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
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