It is useful sometimes to raise your head from the ground and to have a look on the opposite site of a scientific field. Conceptually, most of the research in general is done via two approaches: top-down and bottom-up. In memory research, while some scientist are trying to identify the right receptor or gene and manipulate it with molecular preciseness (bottom-up), the others put electrodes into different brain areas and fire the entire groups of neurons (top-down).
Naturally, both approaches have their pros and cons. The greatest question in going bottom-up is “will the mechanism work on the next level of complexity?” When you go from the top, however, you will always be left with a question “How in the world did it work?”
A recent overview in Nature prompted me to look at the websites of groups doing that kind of research. There’s certainly lots of mathematical modeling and pattern recognition involved, which can lead to quite remarkable results in reverse-engeneering of neural circuitry. At the same time, reading the publication titles left me with a perception of how little we know even about such seemingly trivial circuits as CA1-CA3 in the hippocampus, which is known from 60-es or 70-es to be crucial for the memory formation. This kind of argument poses a big question mark behind the Human Brain Project. Will the neuron-by-neuron reverse-engineering of the brain help us with understanding its function? Probably not, unless someone digs from the opposite side.
With the recent developments in antisense oligonucleotide therapy, the need for more stable and easy to synthesize nucleic acid mimics is increasing. Continue reading “Some highlights from chemical biology of nucleic acids”
The team from John Hopkins reported in PNAS a new pathway regulating the trafficking of AMPA receptor subunit GluA1. It involves previously unknown phosphorylation of the receptor by PAK3 kinase. However, the mechanism is not straightforward and some controversial data are reported. It appears that stimulation of EphrinB2, another player in activity-based synaptogenesis, leads to phosphorylation of the GluA1 subunits and increases the recruitment of them to the synaptic membrane. On the other hand, mutation of serine S863, which is supposed to be phosphorylated, to alanine or aspartate leads to the same increased surface recruitment of the subunit. The latter can be explained by similarity of carboxylic group to phosphorylated serine, but alanine is the obvious outlier.
All in all, the discovery of PAK3 as the AMPA trafficking regulator is unambiguous and may provide a mechanistic rationale for X-linked intellectual disability. But data are still insufficient to build a robust regulatory pathway, and in my opinion the scheme proposed by the authors doesn’t explain all the observations.
During my job-searching campaign I was once asked to show all the structures that I have synthesized. Drawing 200+ molecules seemed no fun to me. Even opening all .cdx files generated in 3.5 years, to copy-paste in a single one, was too boring. So I’ve used openbabel for this job.
Once I had all the .cdx in one folder I’ve ran
babel *.cdx allStruc.svg -xe -xl -xC
rsvg-convert -f pdf -o allStruc.pdf allStruc.svg
But the output was weird. All the charged molecules were assigned unrealistic charges over +2000, so all my potassium trifluoroborate and ammonium salts were crap.
Then I turned to molconvert tool from Chemaxon, which is free for academic non-commercial use. To convert all .cdx files to correct smiles I used a simple script:
for i in $(ls -1 .|grep .cdx)
~/marvin/bin/molconvert smiles $i -o tmp.smi
cat tmp.smi >> smiles.smi
Followed by openbabel (I’ve decided to sort the molecules by molecular weight so the complexity will increase more or less steadily down the list):
babel smiles.smi allStruc.svg -xe -xl -xC --sort MW
rsvg-convert -f pdf -o allStruc.pdf allStruc.svg
Still, the conversion wasn’t ideal. Particularly, BF3¯ groups were represented as BF2·F¯. Fortunately, simple replacement of SMILES code ‘B(F)F’ to ‘[B-](F)(F)F’ and removal of extra fluoride (‘[F-].’ in SMILES) solved the problem.
So, here we go, the work of 3.5 years as almost square matrix 15×14:
In the new paper scientists from Isis Pharmaceuticals report on the development of the new mouse model for spinal muscular atrophy types I and II. The disease emerges from corrupted splicing of the SMN genes. The problem with previous models was that they were either too severe (with complete knockout of the ‘good’ protein), or too mild. So authors attempted to balance the copy number of the protein and create an ‘intermediate’ mouse line. They achieved that by combining ‘mild’ and ‘severe’ alleles and inserting additional human SMN2 gene into corresponding murine locus. So the resulting mice could live long enough and develop the expected neuromuscular pathology with relatively late onset of sympoms.
What’s more exciting is that when mutant mice were treated with the antisense oligo (ASO) targeting the pre-mRNA of SMN2 gene, the lethality and symptoms were improved. Even more surprising was the finding that delivery of the drug into CNS was not required for the improvement. The question remains if this feature translates into patients. Potentially this can lead to better understanding of the SMA pathology, namely if the disease originates in muscles or in neurons and what are the feedback loops between two cell types.