linkstream2 microblog

Cutting Edge: Building bridges between cellular & mol. structural biology https://ELIFEsciences.org/articles/25835 quote: “The use of structured biological #annotation is not common practice in
#cryoEM…[but] by the end of the meeting there was a clearer appreciation of the importance of [this]”

QT:{{”
As previously explained, structured biological annotation is the association of data with identifiers and ontologies taken from well-established bioinformatics resources. The use of structured biological annotation is not common practice in the electron microscopy or structural biology communities. Therefore, ontology experts were invited to the workshop to explain why these are useful and what resources and tools are available for assigning annotations. Use-cases such as mouse imaging data helped to explain the principles and practice of structured biological annotation. By the end of the meeting there was a clearer appreciation of the importance of structured biological annotation for searching and linking imaging data across different scales, between different imaging and structural databases and with other bioinformatics resources.”
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QT:{{”
• Find 3.8 Å resolution cryo-EM structure of the ∼200 kDa magnesium channel CorA • Mg2+-free CorA exhibits dramatic loss of symmetry in the cytoplasmic domain …
• Inter-subunit Mg2+ is important for stabilizing the closed state of CorA “}}

#CryoEM Structures of the Mg++ Channel CorA Reveal Symmetry Break upon Gating https://www.ScienceDirect.com/science/article/pii/S0092867415017195 In closed conformation: 5 identical subunits arranged tightly around 5-fold axis & pore. In open: they lose bridging ions & open to differing degrees with helix-hinging motions.

Matthies D1, Dalmas O2, Borgnia MJ1, Dominik PK2, Merk A1, Rao P1, Reddy BG2, Islam S2, Bartesaghi A1, Perozo E3, Subramaniam S4. Cell. 2016 Feb 11;164(4):747-56. doi: 10.1016/j.cell.2015.12.055.

”
Box 1 of the following paper has a nice definition for differential privacy in genomics sense (phenotypic differential privacy): http://www.cell.com/cell-systems/fulltext/S2405-4712(16)30121-1 “

Accurate Prediction of Contact Numbers for Multi-Spanning Helical #MembraneProteins – via #NeuralNetwork w/ dropout
http://pubs.ACS.org/doi/abs/10.1021/acs.jcim.5b00517 In turn, this can enable accurate prediction of the rotation of TM helices & then the 3D #StructurePrediction of the whole protein

Atomic structure of the entire mammalian mitochondrial complex I https://www.Nature.com/articles/nature19794 Synthesizing #cryoEM w/ (high-FP) cross-linking data to gets a 3.9A structure w/ 78 helices

Fiedorczuk, K., Letts, J.A., Degliesposti, G., Kaszuba, K., Skehel, M., Sazanov, L.A. (2016) Atomic structure of the entire mammalian mitochondrial complex I. Nature 538; 406-410.

related to:
• Letts, J.A., Fiedorczuk, K., Sazanov, L.A. (2016) The architecture of respiratory supercomplexes. Nature 537; 644-648.

https://www.nature.com/articles/nature22367

https://academic.oup.com/bioinformatics/article/33/12/1892/2975325

Quantifying the local resolution of #cryoEM density maps
https://www.Nature.com/articles/nmeth.2727 “Theory…based on the following idea: a L Angstrom feature exists at a pt…if a 3D local sinusoid of wavelength L is statistically detectable above noise at that point.”

QT:{{”
We propose a mathematical theory and an efficient algorithm for measuring local resolution that address all of the above limitations. The theory (Online Methods) is based on the following idea: a λ-Å feature exists at a point in the volume if a three-dimensional (3D) local sinusoid of wavelength λ is statistically detectable above noise at that point. A likelihood-ratio hypothesis test of the local sinusoid versus noise can detect this feature at a given P value (typically P = 0.05). We define the local resolution at a point as the smallest λ at which the local sinusoid is detectable, and we account for multiple testing with an FDR procedure.
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Might be useful for noncoding comparisons

Alignment-free seq. comparison: benefits, apps & tools
https://GenomeBiology.biomedcentral.com/articles/10.1186/s13059-017-1319-7 Great tidbits, viz: Shannon asked von Neumann what to call his info measure – “Why don’t you call it entropy…no one understands entropy…so in any discussion, you’ll be in a position of advantage.”

QT:{{”
“Reportedly, Claude Shannon, who was a mathematician working at Bell Labs, asked John von Neumann what he should call his newly developed measure of information content; “Why don’t you call it entropy,” said von Neumann, “[…] no one understands entropy very well so in any discussion you will be in a position of advantage […]” []. The concept of Shannon entropy came from the observation that some English words, such as “the” or “a”, are very frequent and thus unsurprising” ….
“The calculation of a distance between sequences using complexity (compression) is relatively straightforward (Fig. ). This procedure takes the sequences being compared (x = ATGTGTG and y = CATGTG) and concatenates them to create one longer sequence (xy = ATGTGTGCATGTG). If x and y are exactly the same, then the complexity (compressed length) of xy will be very close to the complexity of the individual x or y. However, if x and y are dissimilar, then the complexity of xy (length of compressed xy) will tend to the cumulative complexities of x and y.”
…
“Intriguingly, BLOSUM matrices, which are the most commonly used substitution matrix series for protein sequence alignments, were found to have been miscalculated years ago and yet produced significantly better alignments than their corrected modern version (RBLOSUM) []; this paradox remains a mystery.”
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https://academic.oup.com/bioinformatics/article/26/19/2458/229387