Good to see Charlie’s still at it

Good to see Charlie Perrin is still pumping out papers, and interesting ones to boot.  I knew him in grad school.  He’s got to be over 80.

This one —J. Am. Chem. Soc. 141, 4103 (2019) –is about something that any undergraduate organic chemist can understand (if not the techniques he used) — keto/enol tautomerism, in which the hydrogen bounces between two oxygens, so that, given N molecules in solution, N/2  have the hydrogen bound to one oxygen and N/2 have it bound to the other.

No so in what Charlie found — a compound where the hydrogen is smack dab in the middle.  Some fancy NMR techniques were used to show this.

Hydrogen bonds are extremely subtle (which is why we don’t understand water as well as we might).  Due to the small mass of the proton it isn’t appropriate to treat the proton in hydrogen bonded systems as a classical particle.  When quantum mechanics enters, aspects such as zero point motion, quantum delocalization and tunneling come into play.  These are called quantum nuclear effects (aka Ubbelohde effects).

Dynamic allostery

It behooves drug chemists to know as much as they can about protein allostery, since so many of their drugs attempt to manipulate it.  An earlier post discussed dynamic allostery which is essentially change in ligand binding affinity without structural change in the protein binding the ligand.  A new paper challenges the concept.

First here’s the old post, and then the new stuff

Remember entropy? — Take II

Organic chemists have a far better intuitive feel for entropy than most chemists. Condensations such as the Diels Alder reaction decrease it, as does ring closure. However, when you get to small ligands binding proteins, everything seems to be about enthalpy. Although binding energy is always talked about, mentally it appears to be enthalpy (H) rather than Gibbs free energy (F).

A recent fascinating editorial and paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 4278 – 4280, 4424 – 4429 ’17 ]shows how the evolution has used entropy to determine when a protein (CzrA) binds to DNA and when it doesn’t. As usual, advances in technology permit us to see this (e.g. multidimensional heteronuclear nuclear magnetic resonance). This allows us to determine the motion of side chains (methyl groups), backbones etc. etc. When CzrA binds to DNA methyl side chains on the protein move more, increasing entropy (deltaS) as well. We all know the Gibbs free energy of reaction (deltaF) isn’t just enthalpy (deltaH) but deltaH – TdeltaS, so an increase in deltaS pushes deltaF lower meaning the reaction proceeds in that direction.

Binding of Zinc redistributes these side chain motion so that entropy decreases, and the protein moves off DNA. The authors call this dynamics driven allostery. The fascinating thing, is that this may happen without any conformational change of CzrA.

I’m not sure that molecular dynamics simulations are good enough to pick this up. Fortunately newer NMR techniques can measure it. Just another complication for the hapless drug chemist thinking about protein ligand interactions.

A recent paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 6563  – 6568 ’17 ] went into more detail about measuring side chain motions  as a surrogate for conformational entropy.  It can now be measured by NMR.  They define complete restriction of  the methyl group symmetry axis as 1, and complete disorder, and state that ‘a variety of models’ imply that the value is LINEARLY related to conformational entropy making it an ‘entropy meter’.  They state that measurement of fast internal side chain motion is largely restricted to the methyl group — this makes me worry that other side chains (which they can’t measure) are moving as well and contributing to entropy.

The authors studied some 28 protein/ligand systems, and found that the contribution of conformational entropy to ligand binding can be favorable, negligible or unfavorable.

What is bothersome to the authors (and to me) is that there were no obvious structural correlates between the degree of conformation entropy and protein structure.  So it’s something you measure not something you predict, making life even more difficult for the computational chemist studying protein ligand interactions.

Now the new stuff [ Proc. Natl. Acad. Sci. vol. 114 pp. 7480 – 7482, E5825 – E5834 ’17 ].  It’s worth considering what ‘no structural change’ means.  Proteins are moving all the time.  Bonds are vibrating at rates up to 10^15 times a second.  Methyl groups are rotating, hydrogen bonds are being made and broken.  I think we can assume that no structural change means no change in the protein backbone.

The work studied a protein of interest to neurological function, the PDZ3 domain — found on the receiving side of a synapse (post-synaptic side).  Ligand binding produced no change in the backbone, but there were significant changes in the distribution of electrons — which the authors describe as an enthalpic rather than an entropic effect.  Hydrogen bonds and salt bridges changed.  Certainly any change in the charge distribution would affect the pKa’s of acids and bases. The changes in charge distribution the ligand would see due to hydrogen ionization from acids and binding to bases would certainly hange ligand binding — even forgetting van der Waals effects.

Remember entropy? — Take II

Organic chemists have a far better intuitive feel for entropy than most chemists. Condensations such as the Diels Alder reaction decrease it, as does ring closure. However, when you get to small ligands binding proteins, everything seems to be about enthalpy. Although binding energy is always talked about, mentally it appears to be enthalpy (H) rather than Gibbs free energy (F).

A recent fascinating editorial and paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 4278 – 4280, 4424 – 4429 ’17 ]shows how the evolution has used entropy to determine when a protein (CzrA) binds to DNA and when it doesn’t. As usual, advances in technology permit us to see this (e.g. multidimensional heteronuclear nuclear magnetic resonance). This allows us to determine the motion of side chains (methyl groups), backbones etc. etc. When CzrA binds to DNA methyl side chains on the protein move more, increasing entropy (deltaS) and as well all know the Gibbs free energy of reaction (deltaF) isn’t just enthalpy (deltaH) but deltaH – TdeltaS, so an increase in deltaS pushes deltaF lower meaning the reaction proceeds in that direction.

Binding of Zinc redistributes these side chain motion so that entropy decreases, and the protein moves off DNA. The authors call this dynamics driven allostery. The fascinating thing, is that this may happen without any conformational change of CzrA.

I’m not sure that molecular dynamics simulations are good enough to pick this up. Fortunately newer NMR techniques can measure it. Just another complication for the hapless drug chemist thinking about protein ligand interactions.

A recent paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 6563  – 6568 ’17 ] went into more detail about measuring side chain motions  as a surrogate for conformational entropy.  It can now be measured by NMR.  They define complete restriction of  the methyl group symmetry axis as 1, and complete disorder, and state that ‘a variety of models’ imply that the value is LINEARLY related to conformational entropy making it an ‘entropy meter’.  They state that measurement of fast internal side chain motion is largely restricted to the methyl group — this makes me worry that other side chains (which they can’t measure) are moving as well and contributing to entropy.

The authors studied some 28 protein/ligand systems, and found that the contribution of conformational entropy to ligand binding can be favorable, negligible or unfavorable.

What is bothersome to the authors (and to me) is that there were no obvious structural correlates between the degree of conformation entropy and protein structure.  So it’s something you measure not something you predict, making life even more difficult for the computational chemist studying protein ligand interactions.

Remember entropy?

Organic chemists have a far better intuitive feel for entropy than most chemists. Condensations such as the Diels Alder reaction decrease it, as does ring closure. However, when you get to small ligands binding proteins, everything seems to be about enthalpy. Although binding energy is always talked about, mentally it appears to be enthalpy (H) rather than Gibbs free energy (F).

A recent fascinating editorial and paper [ Proc. Natl. Acad. Sci. vol. 114 pp. 4278 – 4280, 4424 – 4429 ’17 ]shows how the evolution has used entropy to determine when a protein (CzrA) binds to DNA and when it doesn’t. As usual, advances in technology permit us to see this (e.g. multidimensional heteronuclear nuclear magnetic resonance). This allows us to determine the motion of side chains (methyl groups), backbones etc. etc. When CzrA binds to DNA methyl side chains on the protein move more, increasing entropy (deltaS) and as well all know the Gibbs free energy of reaction (deltaF) isn’t just enthalpy (deltaH) but deltaH – TdeltaS, so an increase in deltaS pushes deltaF lower meaning the reaction proceeds in that direction.

Binding of Zinc redistributes these side chain motion so that entropy decreases, and the protein moves off DNA. The authors call this dynamics driven allostery. The fascinating thing, is that this may happen without any conformational change of CzrA.

I’m not sure that molecular dynamics simulations are good enough to pick this up. Fortunately newer NMR techniques can measure it. Just another complication for the hapless drug chemist thinking about protein ligand interactions.

Functional MRI research is a scientific sewer

First a primer about the science underlying functional Magnetic Resonance Imaging (fMRI). Chemists use MRI all the time, but they call it Nuclear Magnetic Resonance. Docs and researchers quickly changed the name to MRI because no one would put their head in something with Nuclear in the name.

There are now noninvasive methods to study brain activity in man. The most prominent one is called BOLD (Blood Oxygen Level Dependent), and is based on the fact that blood flow increases way past what is needed with increased brain activity. This was actually noted by Wilder Penfield operating on the brain for epilepsy in the 1930s. When a patient had a seizure on the operating table (they could keep things under control by partially paralyzing the patient with curare) the veins in the area producing the seizure turned red. Recall that oxygenated blood is red while the deoxygenated blood in veins is darker and somewhat blue. This implied that more blood was getting to the convulsing area than it could use.

BOLD depends on slight differences in the way oxygenated hemoglobin and deoxygenated hemoglobin interact with the magnetic field used in magnetic resonance imaging (MRI). The technique has had a rather checkered history, because very small differences must be measured, and there is lots of manipulation of the raw data (never seen in papers) to be done. 10 years ago functional magnetic imaging (fMRI) was called pseudocolor phrenology.

Some sort of task or sensory stimulus is given and the parts of the brain showing increased hemoglobin + oxygen are mapped out. As a neurologist as far back as the 90s, I was naturally interested in this work. Very quickly, I smelled a rat. The authors of all the papers always seemed to confirm their initial hunch about which areas of the brain were involved in whatever they were studying. Science just isn’t like that. Look at any issue of Nature or Science and see how many results were unexpected. Results were largely unreproducible. It got so bad that an article in Science 2 August ’02 p. 749 stated that neuroimaging (e.g. functional MRI) has a reputation for producing “pretty pictures” but not replicable data. It has been characterized as pseudocolor phrenology (or words to that effect). Keep reading you’re about to find out just why this was.

What was going on? The data was never actually shown, just the authors’ manipulation of it. Acquiring the data is quite tricky — the slightest head movement alters the MRI pattern. Also the difference in NMR signal between hemoglobin without oxygen and hemoglobin with oxygen is small (only 1 – 2%). Since the technique involves subtracting two data sets for the same brain region, this doubles the error.

Under two years ago, it was shown that 70% of people having functional MRIs (fMRIs) were asleep during the test, and that until then fMRI researchers hadn’t checked for it. For details please see
https://luysii.wordpress.com/2014/05/18/how-badly-are-thy-researchers-o-default-mode-network/. You don’t have to go to med school, to know that the brain functions quite differently in wake and sleep.

Recent work shows that functional MRI work is even worse. A devastating report in [ Proc. Natl. Acad. Sci. vol. 113 pp. 7699 – 7600, 7900 – 7905 ’16 ] showed that certain common settings in 3 software pacakages (SPM, FSL, AFNI) used to analyze fMRI data gave false positive results ‘up to’ 70% of the time. Some 3,500 of the 40,000 fMRI studies in the literature over the past 20 years used these settings. The paper also notes that a bug (now corrected after being used for 15 years) in one of them also led to false positive results.

Here’s a bit more detail on what they did. It turns out that analyzing one voxel (essentially a single MRI pixel) at a time produces valid results. The problem comes when multiple voxels (clusters) are analyzed together. Clusterwise inference considers both the strength of activity at spots throughout the brain as well as the size of the spots. When a parameter called the cluster defining threshold (CDT) is set too low, the analysis is more likely to be false positive. This was true for all 3 packages tested. Parametric statistical methods produce the problem (not for voxels but for clusters). It relies on Gaussian Random Field Theory (RFT) for clusters , which depends on two other assumptions (1) the spatial autocorrelation function has a squared exponential shape — e.g. Gaussian (2) the spatial smoothness of the fMRI signal is constant over the brain. Neither of these assumptions is correct. Those of you who’ve read Nassim Nicholas Taleb about the stock market know about ‘fat tails’. It turns out that the spatial correlation function has them. Here’s what a fat tail is all about. Human height goes fall quite nicely into a Gaussian distribution. There are 7 and 8 footers about but they are rare. If the human height distribution wasn’t Gaussian but had a fat tail, we’d see 12 and 15 footers.

If that wasn’t bad enough,the following is even worse (in my opinion). 40% of 241 recent fMRI studies didn’t report using well known methods for correcting for multiple testing. They may have done so, but every biomedical paper, and drug study says so explicitly. Not only that but drug studies are required to explicitly state the hypothesis (or hypotheses) they are testing.

This is probably why in the early days, fMRI researchers always confirmed their original hypothesis. They could test the massive fMRI for statistical rarity, and since the data was so large, find it and post hoc propter hoc publish it. Possibly they did so out of ignorance, but even so this is inexcusable