192,863 research outputs found
SAH-domains in eukaryotic genomes.
No full textThe plot contrasts the total number of SAH-domains per dataset with the total number of sequences containing at least a single SAH-domain. Each diamond represents a dataset. The number of sequences with multiple SAH-domains is not a characteristic of certain species but depends on the total number of sequences with SAH-domains per species. The more sequences contain SAH-domains, the more sequences with multiple SAH-domains will be found. For orientation, labels were given for likely the best annotated dataset, human [all], the most extreme case mouse [ab initio], and the two datasets with the largest deviation from the line, danio [ab initio] and rice [ab initio]. The datasets with SAH-domains identified with a window size of 14 amino acids were taken. Abbreviations: Dr, Danio rerio; Hs, Homo sapiens; Mm, Mus musculus; Os, Oryza sativa.</p
Replication Data for: Optimizing impact of low-efficacy influenza vaccines
No full textThe dataset associated with our paper:
Sah P, Medlock J, Fitzpatrick MC, Singer BH, Galvani AP. Optimizing impact of low-efficacy influenza vaccines. Proc Natl Acad Sci USA. 2018.
Note: The associate code can be accessed from https://github.com/prathasah/optimizing-flu-vaccine
Please cite the paper above, if you use our data or code in any form or create a derivative work
SAH-domains in eukaryotic genomes.
No full textThe plot presents the percentage of SAH-domains with respect to total numbers of processed sequences per datasets. The blue bars represent all SAH-domains, while the red bars represent all sequences containing at least a single SAH-domain. The numbers on top of the blue bars denote the total numbers of predicted SAHs (see also Table 2). The window size for identifying SAHs was 21 amino acids. For an explanation of the difference between “all” and “ab initio” datasets see Table 1.</p
Replication Data for: Optimizing impact of low-efficacy influenza vaccines
No full textThe dataset associated with our paper:
Sah P, Medlock J, Fitzpatrick MC, Singer BH, Galvani AP. Optimizing impact of low-efficacy influenza vaccines. Proc Natl Acad Sci USA. 2018.
Note: The associate code can be accessed from https://github.com/prathasah/optimizing-flu-vaccine
Please cite the paper above, if you use our data or code in any form or create a derivative work
Protein-RNA recognition in complex structures of RlmCD-SAH-U747<sub>L</sub> and RlmCD-SAH-U1939<sub>L</sub>.
No full text(A, B) Interaction details between nucleotide of U747 or U1939 and surrounding RlmCD residues. Hydrogen bonding interactions are all indicated as black dashed lines. The yellow “W” represents water molecule and the purple line indicates face-to-edge stacking. (C) Comparison of aromatic stacking interactions in RlmCD-SAH-U747L (marine) and RlmCD-SAH-U1939L (yellow). (D) Side-chain of F145 adopts multiple conformations in apo and RNA-bound structures of RlmCD, while its conformations remain similar in the apo or RNA-bound structure of RumA.</p
Change in the P-index at 24 hours after SAH.
No full text<p>A: The P-index of NPE was significantly higher than non-NPE in SAH rats. @ <i>p</i><0.05 vs. NPE; B: BBG decreased the incidence of NPE without reaching significance; C: BBG significantly reduced the P-index at 24 hours after SAH. Error bars represent mean ± standard error of the mean. * <i>p</i><0.05 vs. Sham; ** <i>p</i><0.01 vs. Sham; # <i>p</i><0.05 vs. SAH+vehicle group.</p
Natural abundance of <sup>15</sup>N of a spruce forest ecosystem under acid rain and manipulated clean rain field conditions
No full textReplication Data for: Unraveling the disease consequences and mechanisms of modular structure in animal social networks
No full textThe dataset and link to codes used in our paper "Unraveling the disease consequences and mechanisms of modular structure in animal social networks".
Note: The code for generating random modular graphs can be accessed from https://github.com/bansallab/modular_graph_generator.
Additional codes to replicate the figures in the paper can be found at: https://github.com/bansallab/modularity_disease_implications
The animal social networks used in the paper are available at: https://bansallab.github.io/asnr/</p
Replication Data for: Unraveling the disease consequences and mechanisms of modular structure in animal social networks
No full textThe dataset and link to codes used in our paper "Unraveling the disease consequences and mechanisms of modular structure in animal social networks".
Note: The code for generating random modular graphs can be accessed from https://github.com/bansallab/modular_graph_generator.
Additional codes to replicate the figures in the paper can be found at: https://github.com/bansallab/modularity_disease_implications
The animal social networks used in the paper are available at: https://bansallab.github.io/asnr/</p
SAH recognition.
No full text<p>(A) SAH interaction with TbPRMT6 (left) and rat PRMT1 (right) The residues are shown in the stick model and labeled, and the water molecules are shown as red spheres and labeled with W1 and W2 A representative omit (Fo-Fc) electron density map (green) shows the bound SAH The dashed lines represent hydrogen bonds (B) The conformational change in the active site of TbPRMT6 induced by SAH binding The left figure is the superposition of the apo (grey) and SAH-bound (cyan) structures of TbPRMT6 The movements of the key residues are highlighted by arrows and distances The right figures are enlarged views of the active site of TbPRMT6 in the free and SAH-bound states The dashed lines represent hydrogen bonds (C) ITC-based measurements of the bindings of AcH4-21 to apo and SAH-bound TbPRMT6 The fitted <i>K<sub>d</sub></i> of AcH4-21 to SAH-bound TbPRMT6, including the standard errors in the measurements, are indicated in the panel (D) Enzymatic assays of TbPRMT6 mutants with mutations in the residues that undergo significant rearrangement upon SAH binding.</p
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