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ref: -0 tags: evolution simplicity symmetry kolmogorov complexity polyominoes protein interactions date: 04-21-2022 18:22 gmt revision:5 [4] [3] [2] [1] [0] [head]

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

  • Central hypothesis is that simplicity and symmetry arrive not through natural selection, but because these form are overwhelmingly represented in the genotype-phenotype map
  • Experimental example here was "polyominoes", where there are N=16 tiles, each with a 4 numbers (encoded with e.g. 6-bit binary numbers). The edge numbers determine how the tiles irreversibly bind, e.g. 1 <-> 2, 3 <-> 4 etc, with 4 and 2^6-1 binding to nothing.
  • These tiles are allowed to 'randomly' self-assemble. Some don't terminate (e.g. they form continuous polymers); these are discarded; others do terminate (no more available binding sites).
  • They assessed the complexity of both polyominoes selected for a particular size, eg 16 tiles, or those not selected at all, other than terminating.
  • In both complexity was assessed based on how many actual interactions were needed to make the observed structure. That is, they removed tile edge numbers and kept it if it affected the n-mer formation.
  • Result was this nice log-log plot:
  • Showed that this same trend holds for protein-protein complexes (weaker result, imho)
  • As well as RNA secondary structure
  • And metabolic time-series in a ODE modeled on yeast metabolism (even weaker result..)

The paper features a excellent set of references, including:
Letter to a friend following her article Machine learning in evolutionary studies comes of age

Read your PNAS article last night, super interesting that you can get statistical purchase on long-lost evolutionary 'sweeps' via GANs and other neural network models.  I feel like there is some sort of statistical power issue there?  DNNs are almost always over-parameterized... slightly suspicious.

This morning I was sleepily mulling things over & thought about a walking conversation that we had a long time ago in the woods of NC:  Why is evolution so effective?  Why does it seem to evolve to evolve?  Thinking more -- and having years more perspective -- it seems almost obvious in retrospect: it's a consequence of Bayes' rule.  Evolution finds solutions in spaces that have overwhelming prevalence of working solutions.  The prior has an extremely strong effect.  These representational / structural spaces by definition have many nearby & associated solutions, hence appear post-hoc 'evolvable'.  (You probably already know this.)

I think proteins very much fall into this category: AA were added to the translation machinery based on ones that happened to solve a particular problem... but because of the 'generalization prior' (to use NN parlance), they were useful for many other things.  This does not explain the human-engineering-like modularity of mature evolved systems, but maybe that is due to the strong simplicity prior [1]

Very very interesting to me is how the science of evolution and neural networks are drawing together, vis a vis the lottery ticket hypothesis.  Both evince a continuum of representational spaces, too, from high-dimensional vectoral (how all modern deep learning systems work) to low-dimensional modular, specific, and general (phenomenological human cognition).  I suspect that evolution uses a form of this continuum, as seen in the human high-dimensional long-range gene regulatory / enhancer network (= a structure designed to evolve).  Not sure how selection works here, though; it's hard to search a high-dimensional space.  The brain has an almost identical problem: it's hard to do 'credit assignment' in a billions-large, deep and recurrent network.  Finding which set of synapses caused a good / bad behaviior takes a lot of bits.

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ref: -0 tags: protein engineering structure evolution date: 02-23-2021 19:57 gmt revision:1 [0] [head]

From Protein Structure to Function with Bioinformatics

  • Dense and useful resource!
  • Few new folds have been discovered since 2010 -- the total number of extand protein folds is around 100,000. Evolution re-uses existing folds + the protein fold space is highly convergent. Amazing. link

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ref: -2011 tags: two photon cross section fluorescent protein photobleaching Drobizhev gcamp date: 11-04-2020 18:07 gmt revision:9 [8] [7] [6] [5] [4] [3] [head]

PMID-21527931 Two-photon absorption properties of fluorescent proteins

  • Significant 2-photon cross section of red fluorescent proteins (same chromophore as DsRed) in the 700 - 770nm range, accessible to Ti:sapphire lasers ...
    • This corresponds to a S 0S nS_0 \rightarrow S_n transition
    • But but, photobleaching is an order of magnitude slower when excited by the direct S 0S 1S_0 \rightarrow S_1 transition (but the fluorophores can be significantly less bright in this regime).
      • Quote: the photobleaching of DsRed slows down by an order of magnitude when the excitation wavelength is shifted to the red, from 750 to 950 nm (32).
    • See also PMID-18027924
  • Further work by same authors: Absolute Two-Photon Absorption Spectra and Two-Photon Brightness of Orange and Red Fluorescent Proteins
    • " TagRFP possesses the highest two-photon cross section, σ2 = 315 GM, and brightness, σ2φ = 130 GM, where φ is the fluorescence quantum yield. At longer wavelengths, 1000–1100 nm, tdTomato has the largest values, σ2 = 216 GM and σ2φ = 120 GM, per protein chain. Compared to the benchmark EGFP, these proteins present 3–4 times improvement in two-photon brightness."
    • "Single-photon properties of the FPs are poor predictors of which fluorescent proteins will be optimal in two-photon applications. It follows that additional mutagenesis efforts to improve two-photon cross section will benefit the field."
  • 2P cross-section in both the 700-800nm and 1000-1100 nm range corresponds to the chromophore polarizability, and is not related to 1p cross section.
  • This can be useflu for multicolor imaging: excitation of the higher S0 → Sn transition of TagRFP simultaneously with the first, S0 → S1, transition of mKalama1 makes dual-color two-photon imaging possible with a single excitation laser wavelength (13)
  • Why are red GECIs based on mApple (rGECO1) or mRuby (RCaMP)? dsRed2 or TagRFP are much better .. but maybe they don't have CP variants.
  • from https://elifesciences.org/articles/12727

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ref: -0 tags: Lucy Flavin mononucelotide FAD FMN fluorescent protein reporter date: 10-17-2019 19:54 gmt revision:1 [0] [head]

PMID-25906065 LucY: A Versatile New Fluorescent Reporter Protein

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ref: -2016 tags: fluorescent proteins photobleaching quantum yield piston GFP date: 06-19-2019 14:33 gmt revision:0 [head]

PMID-27240257 Quantitative assessment of fluorescent proteins.

  • Cranfill PJ1,2, Sell BR1, Baird MA1, Allen JR1, Lavagnino Z2,3, de Gruiter HM4, Kremers GJ4, Davidson MW1, Ustione A2,3, Piston DW
  • Model bleaching as log(F)=αlog(P)+clog(F) = -\alpha log(P) + c or k bleach=bI αk_{bleach} = b I^{\alpha} where F is the fluorescence intensity, P is the illumination power, and b and c are constants.
    • Most fluorescent proteins have α\alpha > 1, which means superlinear photobleaching -- more power, bleaches faster.
  • Catalog the degree to which each protein tends to form aggregates by tagging to the ER and measuring ER morphology. Fairly thorough -- 10k cells each FP.

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ref: -2019 tags: super-resolution microscopy fluorescent protein molecules date: 05-28-2019 16:02 gmt revision:3 [2] [1] [0] [head]

PMID-30997987 Chemistry of Photosensitive Fluorophores for Single-Molecule Localization Microscopy

  • Excellent review of all the photo-convertable, photo-switchable, and more complex (photo-oxidation or reddening) of both proteins and small molecule fluorophore.
    • E.g. PA-GFP is one of the best -- good photoactivation quantum yield, good N ~ 300
    • Other small molecules, like Alexa Fluor 647 have a photon yield > 6700, which can be increased with triplet quenchers and antioxidants.
  • Describes the chemical mechanism of the various photo switching -- review is targeted at (bio)chemists interested in getting into imaging.
  • Emphasize that critical figures of merit are photoactivation quantum yield Φ pa\Phi_{pa} and N, overall photon yield before photobleaching.
  • See also Colorado lecture

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ref: -0 tags: voltage sensitive dyes fluorescent protein date: 01-02-2013 05:08 gmt revision:0 [head]

PMID-20622860 Imaging brain electric signals with genetically targeted voltage-sensitive fluorescent proteins.

  • Interesting: Most fluorescent fusion proteins form intracellular aggregates during long-term expression in mammalian neurons, although this effect appears to be minimal in Aequorea victoria–derived fluorescent proteins.
  • See also {1185}

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ref: Sakai-2001.06 tags: voltage scensitive fluorescent protein flourophore VSFP1 endoscope date: 01-24-2012 06:07 gmt revision:5 [4] [3] [2] [1] [0] [head]

http://www.blackwell-synergy.com/doi/full/10.1046/j.0953-816x.2001.01617.x PMID-11454036[0]


[0] Sakai R, Repunte-Canonigo V, Raj CD, Knöpfel T, Design and characterization of a DNA-encoded, voltage-sensitive fluorescent protein.Eur J Neurosci 13:12, 2314-8 (2001 Jun)
[1] van Roessel P, Brand AH, Imaging into the future: visualizing gene expression and protein interactions with fluorescent proteins.Nat Cell Biol 4:1, E15-20 (2002 Jan)
[2] Guerrero G, Siegel MS, Roska B, Loots E, Isacoff EY, Tuning FlaSh: redesign of the dynamics, voltage range, and color of the genetically encoded optical sensor of membrane potential.Biophys J 83:6, 3607-18 (2002 Dec)
[3] Jung JC, Mehta AD, Aksay E, Stepnoski R, Schnitzer MJ, In vivo mammalian brain imaging using one- and two-photon fluorescence microendoscopy.J Neurophysiol 92:5, 3121-33 (2004 Nov)
[4] Sjulson L, Miesenböck G, Optical recording of action potentials and other discrete physiological events: a perspective from signal detection theory.Physiology (Bethesda) 22no Issue 47-55 (2007 Feb)

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ref: Graves-2001.04 tags: sleep memory REM protein synthesis review date: 03-25-2009 15:23 gmt revision:1 [0] [head]

PMID-11250009[0] Sleep and memory: a molecular perspective.

  • inhibition of protein synthesis is most effective if it occurs at a time post-training when rapid eye movement (REM) sleep is required for memory consolidation
  • The neurochemical changes that occur across sleep/wake states, especially the cholinergic changes that occur in the hippocampus during REM sleep, might provide a mechanism by which sleep modulates specific cellular signaling pathways involved in hippocampus-dependent memory storage.
    • REM sleep could influence the consolidation of hippocampus-dependent long-term memory if it occurs during windows that are sensitive to cholinergic or serotonergic signaling.
    • PKA activation seems important to hippocampal long-term memory
    • NMDA affects PKA through Ca2+ to adenyl cyclase
    • 5-HT_1A receptor negatively coupled to adenyl cyclase (AC)
    • 5-HT concentrations go down in hippocampus during sleep ?


[0] Graves L, Pack A, Abel T, Sleep and memory: a molecular perspective.Trends Neurosci 24:4, 237-43 (2001 Apr)

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ref: notes-0 tags: grain protein growing framing feed oats alfalfa barley corn wheat date: 06-18-2008 15:14 gmt revision:0 [head]

I found this on my computer tucked away into a dusty corner. Such fascinating information should not be left hidden -

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ref: Walker-2005.12 tags: algae transfection transformation protein synthesis bioreactor date: 03-21-2008 17:22 gmt revision:1 [0] [head]

Microalgae as bioreactors PMID-16136314

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ref: bookmark-0 tags: petaflop gigaflop RIKEN protein folding MDGRAPE date: 09-17-2007 14:55 gmt revision:0 [head]

pretty impressive project, especially considering how much time and money they spent ($15 m, 6 man-months to do the verilog (only!)) http://www.hotchips.org/archives/hc16/3_Tue/1_HC16_Sess6_Pres1_bw.pdf