4 diseases explained at one blow said the protein chemist — part 1

A brilliant paper [ Science vol. 377 eabn5582 pp. 1 –> 20 ’22 ] explains how changing a single amino acid (proline) to another  can cause 4 different diseases, depending on the particular protein it is found in (and which proline of many is changed).

There is so much in this paper that it will take several posts to go over it all.  The chemistry in the paper is particularly fine.  So it’s back to Biochemistry 101 and the alpha helix and the beta sheet.

Have a look at this

https://cbm.msoe.edu/teachingResources/proteinStructure/secondary.html

If you can tell me how to get a picture like this into a WordPress post please make a comment.

The important point is that hydrogen bonds between the amide hydrogen of one amino acid and the carbonyl group of another hold the alpha helix and the beta pleated sheet together.

Enter proline : p//en.wikipedia.org/wiki/Proline.  Proline when not embedded in a protein has a hydrogen on the nitrogen atom in the ring.  When proline is joined to another amino acid by a peptide bond in a protein, the hydrogen on the nitrogen is no longer present.  So the hydrogen bond helping to hold the two structures (alpha helix and beta sheet) is no longer present at proline, and alpha helices and beta sheets containing proline are not has stable.  Prolines after the fourth amino acid of the alpha helix (e. g. after the first turn of the helix) produce a kink.  The proline can’t adopt the alpha helical configuration of the backbone and it can’t hydrogen bond.

But it’s even worse than that (and this observation may even be original).  Instead of a hydrogen bonding to the free electrons of the oxygen in the carbonyl group you have the two electrons on the nitrogen jammed up against them.  This costs energy and further destabilizes both structures.

Being a 5 membered ring which contains the alpha carbon of the amino acid, proline in proteins isn’t as flexible as other amino acids.

This is why proline is considered to be a helix breaker, and is used all the time in alpha helices spanning cellular membranes to cause kinks, giving them more flexibility.

There is much more to come — liquid liquid phase separation, prion like domains, low complexity sequences, frontotemporal dementia with ALS, TDP43, amyloid, Charcot Marie Tooth disease and Alzheimer’s disease.

So, for the present stare at the link to the diagram above.

Apologies for another posting delay

Hopefully the post on the paper I’m so impressed with will be out in the next few days.  I’ve been clearing away the underbrush in Needham’s Visual Differential Geometry and Forms before the final push on the Einstein field equation and Riemannian geometry.

Apologies for the delay

Here’s a clue for you all to think about — what effects does proline have on (1) the alpha helix (2) the beta pleated sheet?

Proline rides again !

Proline is a kinky amino acid.  Kinky in the sense that it is only one of the twenty with a fixed configuration of its alpha carbon because of the ring (which may be why there is more of it in organisms living at high temperature) and kinky in the sense that when present in alpha helices it produces a kink.  The previous post shows how it is used to schlep the body weight’s worth of ATP we make each day out of our mitochondria — https://luysii.wordpress.com/2019/01/30/3939/.

Well here it is in one of the marijuana receptors (CB1).  Binding of delta9 THC in the 7 transmembrane alpha helix bundles of the G Protein Coupled Receptor (GPCR) causes an alteration in the kink allowing transmembrane helix 6 (TM6) to move outward toward the cytoplasm, creating a cavity on the intracellular side, where the G protein trimer can bind.

You can read much more about this in an exquisite paper [ Cell vol. 176 pp. 448 – 458 `19 ] describing the CB1 receptor bound to a synthetic ligand 20 times more potent that delta-9 tetrahydrocannabinol (delta9 THC).  It is a cryoEM study which used 177,000 projections to come up with a 3 Angstrom resolution structure of CB1 bound to MBDB-FUBINACA in complex with its G protein trimer.  They had to use a single chain variable fragment (scFv6) along with a positive allosteric modulator (PAM) called ZCZ-011 to stabilize the complex.

MBDB-FUBINACA is a story in itself.  It is presently the fentanyl of synthetic cannabinoids, which “has been linked to thousands of hospitalizations and numerous fatalities”  [ New England Journal of Medicine vol. 376 pp. 235 – 242 ’17 ].  I’m surprised I’ve never heard of it — have you? But then I’ve been retired from clinical practice for some time. Perhaps the mainstream press, pushing marihuana legalization as it has been, kept it quiet, or more likely there have been no further episodes of mass intoxication from the AMB-FUBINACA (aka the zombie drug) since 2017.

I’ve never knowingly used marihuana.  Frankly it scares me — for why please see — https://luysii.wordpress.com/2014/05/13/why-marihuana-scares-me/.

There are 4 molecular switches buried in GPCRs [ Current Med. Chem. vol. 19 pp. 1090 – 1109 ’12 ]

1. The ionic lock switch between the D/E R Y sequence at the cytoplasmic end of TM3 and E286 at the cytoplasmic end of TM6 (single letter amino acid code used) –http://130.88.97.239/bioactivity/aacodefrm.html

2. TM3 – TM7 lock switch.  In rhodopsin it is between the protonated Schiff base of lysine and a glutamic acid and it broken on light activation,.=

3. Toggle switch linked with the n P x x Y motif in TM7 (x stands for any amino acid) — much more about this later in the post.

4. Transmission switch — produced by agonist binding, the outward movement of TM6 to to ligand binding creating a hole fo the G protein to bind to the receptor on the cytoplasmic side.

So why did I call the Cell paper exquisite?  Because of the molecular detail it provides about just how MDMB FUBINACA activates CB1.  Here’s the structure of AB-FUBINACA — https://en.wikipedia.org/wiki/AB-FUBINACA.   Both look like drugs designed by a committee.  They both have a para-iodophenyl group, an amide, and a fused indole ring with an extra nitrogen (imidazole ring — I never could keep heterocyclic nomenclature straight).    MDMB has a methyl ester (in place of the amide) and a tertiary butyl group (in place of the isoPropyl group).

I don’t have time to look up how Pfizer came up with it.  The FUBINACAs do not resemble delta9 THC at all — https://en.wikipedia.org/wiki/Tetrahydrocannabinol.

The pictures in the paper show how the hydrophobic aromatic side chains of FIVE phenylalanines and 2 tryptophans create a nice oily space for delta9 THC and MBDB-FUBINACA to bind.

F200 (phenylAlanine 200) and W356 are the toggle twin switch which stabilize the inactive conformation of CB1.  The rotation of F200 to interact with the imidazole of FUBINACA, allows W356 to rotate outward, changing the kink produced the the proline #358  in TM6 allowing the helix to straighten and rotate outward toward the cytoplasm, creating a cavity for the G protein to bind to.

Definitely a tour de force for the blind watchman.

Let’s hear it for the blind watchmaker

The blind watchmaker had a lot of foresight in choosing to use a rather  funky looking amino acid (proline) resembling none of the others.  A lot of kindness was also shown to structural molecular biologists by two of the watchmaker’s henchmen – Burkholderia gladioli and the common daisy.

All appear in a fascinating paper [ Cell vol. 176 pp. 435 – 447  ’19 ] in which the structure and better the mechanism of action of the mitochondrial ADP/ATP translocase, a molecule of some interest since our mitochondria make our body weight of ATP each day and need some way to get it out into the cytoplasm where it is used.

The molecule has quite a job to do, getting the rather large ATP molecule out to the intermembrane space (and thence out to the cytoplasm) without allowing protons to sneak out with it, since it is the proton gradient which is used to power ATP synthase the exquisite machine which makes ATP.   This is quite a trick as no chemical moiety is as small as a proton.

The translocase has two states — one in which it is open to the mitochondrial matrix (called the m-state) and another in which it is open (eventually) to the cytoplasm — called the c-state. In the m-state the cytoplasmic portion is shut, and in the c-state the membrane portion is shut.

The rather wierd looking molecule bongkrekic acid  made by Burkholderia gladioli  https://en.wikipedia.org/wiki/Bongkrek_acid binds to the translocase fixing it in the m-state.  Atractyloside, made by daisies binds to the molecule fixing it in the c-state.  They made life much easier for the structural biologist and cryoEMographers who wrote the paper.

Proline comes in because when placed in an alpha helix, proline’s 5 membered ring structure fixes the alpha carbon so that it is essentially inflexible, meaning that it can’t get into the conformation that the other 19 amino acids can get into when an alpha helix is formed.  Translation — proline is a helix breaker, forming a kink in the helix.

The translocase contains 3 modules of 100 amino acids each of which has 2 alpha helices, one of them containing a proline causing a kink in the helix.  The prolines are in the middle of the helix.  The ATP channel is formed by the 6 helices.

Essentially in the middle of the membrane, the kinked alpha helices form a pivot (fulcrum), so the helices rock back and forth, opening one side while simultaneously shutting the other, permitting ATP to bind near the fulcrum without letting anything else through, when the pivot shifts   — out goes the ATP (without letting protons sneak past).

There is far more beautiful protein chemistry on display.  There is a conserved signature motif Proline x Aspartic acid/Glutamic acid X X Lysine/Arginine at the carboxy terminal end of one of the helices of each other 3 modules — this forms a salt bridge shutting the channel on the matrix side.  Glycine and other small amino acids (alanine) allow close packing of the helices on the cytoplasmic side.

It is unfortunate that the most of humanity doesn’t have the background to appreciate the elegance and beauty of Nature’s solution to the problem.  I say Nature rather than God to be scientifically correct, but it’s elegant chemistry like this that makes it hard for me to accept that it arose by the machinations of a blind watchmaker.

A very UNtheoretical approach to cancer diagnosis

We have tons of different antibodies in our blood. Without even taking mutation into account we have 65 heavy chain genes, 27 diversity segments, and 6 joining regions for them (making 10,530) possibilities — then there are 40 genes for the kappa light chains and 30 for the lambda light chains or over 1,200 * 10,530. That’s without the mutations we know that do occur to increase antibody affinity. So the number of antibodies probably ramming around in our blood is over a billion (I doubt that anyone has counted then, just has no one has ever counted the neurons in our brain). Antibodies can bind to anything — sugars, fats, but we think of them as mostly binding to protein fragments.

We also know that cancer is characterized by mutations, particularly in the genes coding for proteins. Many of the these mutations have never been seen by the immune system, so they act as neoantigens. So what [ Proc. Natl. Acad. Sci. vo. 111 pp. E3072 – E3080 ’14 ] did was make a chip containing 10,000 peptides, and saw which of them were bound by antibodies in the blood.

The peptides were 20 amino acids long, with 17 randomly chosen amino acids, and a common 3 amino acid linker to the chip. While 10,000 seems like a lot of peptides, it is a tiny fraction (actually 10^-18
of the 2^17 * 10^17 = 1.3 * 10^22 possible 17 amino acid peptides).

The blood was first diluted 500x so blood proteins other than antibodies don’t bind significantly to the arrays. The assay is disease agnostic. The pattern of binding of a given person’s blood to the chip is called an immunosignature.

What did they measure? 20 samples from each of five cancer cohorts collected from multiple geographic sites and 20 noncancer samples. A reference immunosignature was generated. Then 120 blinded samples from the same diseases gave 95$% classification accuracy. To investigate the breadth of the approach and test sensitivity, the immunosignatures 75% of over 1,500 historical samples (some over 10 years old) comprising 14 different diseases were used as training, then the other 25% were read blind with an accuracy of over 98% — not too impressive, they need to get another 1,500 samples. Once you’ve trained on 75% of the sample space, you’d pretty much expect the other 25% to look the same.

The immunosignature of a given individual consists of an overlay of the patterns from the binding signals of many of the most prominent circulating antibodies. Some are present in everyone, some are unique.

A 2002 reference (Molecular Biology of the Cell 4th Edition) states that there are 10^9 antibodies circulating in the blood. How can you pick up a signature on 10K peptides from this. Presumably neoAntigens from cancer cells elicit higher afifnity antibodies then self-antigens. High affiity monoclonals can be diluted hundreds of times without diminishing the signal.

The next version of the immunosignature peptide microArray under development contains over 300,000 peptides.

The implication is that each cancer and each disease produces either different antigens and or different B cell responses to common antigens.

Since the peptides are random, you can’t align the peptides in the signature to the natural proteomic space to find out what the antibody is reacing to.

It’s a completely atheoretical approach to diagnosis, but intriguing. I’m amazed that such a small sample of protein space can produce a significant binding pattern diagnostic of anything.

It’s worth considering just what a random peptide of 17 amino acids actually is. How would you make one up? Would you choose randomly giving all 20 amino acids equal weight, or would you weight the probability of a choice by the percentage of that amino acid in the proteome of the tissue you are interested in. Do we have such numbers? My guess is that proline, glycine and alanine would the most common amino acids — there is so much collagen around, and these 3 make up a high percentage of the amino acids in the various collagens we have (over 15 at least).