Building the Middle Class

Why are poor people in the US enslaved to tyrannical, immiserating institutions?

Santa Claus brought an interesting gift this Christmas, Barbara Ehrenreich's "Nickle and Dimed". This is a memoir of her experiment as a low wage worker. Ehrenreich is a well-educated scientist, feminist, journalist, and successful writer, so this was a dive from very comfortable upper middle class circumstances into the depths both of the low-end housing market and the minimum wage economy. While she brings a great deal of humor to the story, it is fundamentally appalling, an affront to basic decency. Our treatment of the poor should be a civil rights issue.

The first question is why we have a minimum wage at all. What is the lowest wage that natural economic conditions would bear, and what economic and social principles bear on this bottom economic rung? In ancient times, slavery was common, which meant a wage of zero. This was replicated in the ante-bellum American South- minimum wage of zero. So as far as natural capitalism is concerned, there is no minimum wage needed and people can rather easily be coerced by various social and violent means to work for the barest subsistence. The minimum wage is entirely a political and social concept, designed to express a society's ideas of minimal economic, civic, and social decency. Maybe that is why, as with so many other things, the US reached a high point in its real minimum wage in the late 1960's, 66% higher than what it is now.

Real minimum wage in the US, vs nominal.

The whole economy of low wage work is very unusual. One would think that supply and demand would operate here, and that difficult work would be rewarded by higher pay. But it is precisely the most difficult work- the most grinding, alienating, dispiriting work that is paid least. There is certainly an education effect on pay, but the social structure of low end work is mostly one of power relations, where desperate people are faced with endlessly greedy employers, who know that the less they pay, the more desperate their workers will be to get even that little amount. It is remarkable what we have allowed this sector to do in the name of "free" capitalism- the drug tests, the uniforms, the life-destroying scheduling chaos, the wage theft, the self-serving corporate propaganda, the surveillance.

Is it a population issue, that there is always an excess of low-wage workers? I think it is really the other way around, that there is a highly flexible supply of low-wage work, thanks to the petty-tyrannical spirit of "entrepreneurs". No one needs the eighth fast food restaurant, the fifteenth nail salon, or the third maid cleaning service. We use and abuse low wage labor because it is there, not because these are essential jobs. If a shortage of low-wage workers really starts to crimp an important industry, it has recourse to far more effective avenues of redress, such as importing workers from abroad, outsourcing the work, or if all else fails, automating it. What people are paid is largely a social construct in the minds of us, the society of employers who couldn't imagine paying decently for the work / servitude of others. To show an exception that illustrates the rule, nurses during the pandemic did in some cases, if they were willing to travel and negotiate, make out like bandits. But nurses who stayed put, played by the rules, and truly cared for those around them, were routinely abused, forced into extra work and bad conditions by employers who did not care about them and had .. no choices. In exceptional cases where true need exists, supply and demand can move the needle. But social power plays a very large role.

Some states have raised their minimum wage, such as California, to $15. This is a more realistic wage, though the state has astronomic housing and other costs as well. Has our economy collapsed here? No. It has had zero discernable effect on the provision of local services, and the low wage economy sails on at a new, and presumably more humane, level. When I first envisioned this essay, I thought that a much more substantial increase in the minimum wage would be the proper answer. But then I found that $15 per hour provides an annual income that is almost at the US level of median income, 34k annually for an individual. The average income in the US is only 53k. So there is not a lot of wiggle room there. We are a nation of the poorly paid, on average living practically hand-to-mouth. On the household level, things may look better if one has the luck to have two or more solid incomes.

My own individual incomes analysis, drawn from reported Social Security data.

Any any rate, a livable wage is not much different from the median wage, and even that is too low in many economically hot areas where real estate is unbearably expensive. This is, incidentally, another large dimension of US poverty, that the stand-pat, NIMBY, no-growth zoning practices of what is now a majority of the country have sentenced the poor and the young to an even lower standard of living than what the income statistics would indicate, as they fork over their precious earnings to the older, richer, and socially settled landlords among us.

So what is the answer? I would advocate for a mix of deep policy change. First is a minimum wage that is livable, which means $15 nationwide, indexed for inflation, and higher as needed in more high-cost states. It should be a basic contract with the citizenry and workers of all types that working should pay decently, and not send you to a food pantry. All those jobs and businesses that can not survive without poorly paid workers... we don't need them. Second would be a government employer of last resort system that would offer a job to anyone who wants one. This would be paid at the minimum wage, and put people to work doing projects of public significance- cleaning up roadways, building schools, offering medical care, checkups, crossing guards, etc. We can, as a society and as civil governments, do a better job employing the poor in a useful way than can the much-vaunted entrepreneurs. Instead of endless strip malls of bottom-feeding commerce, let local governments sweep up available labor for cleaning the environment, instead of fouling it. Welfare should be, instead of a demeaning odyssey through DMV- like bureaucracies, a straight payment to anyone not employed, at half the minimum wage.

Third, we need more public services. Transit should be totally free. Medical care should be completely free. Education should be free. And incidentally, secondary education should be all public, with private schools up to 12th grade banned. When we wonder why our country and politics have become so polarized, a big reason is the physical and spiritual separation between the rich and poor. While the speaker in the video linked below advocates for free housing as well, that would be perhaps a bridge too far, though housing needs to be addressed urgently by forcing governments to zone for their actual population and taking homelessness as a policy-directing index of the need to zone and build more housing.

Fourth, the rich need to be taxed more. The corrosion of  our social system is not only evident at the bottom where misery and quasi-slavery is the rule, but at the top, where the rich contribute less and less to positive social values. The recent Twitter drama showed in an almost mythical way the incredible narcisism and callous ethics that pervade the upper echelons (... if the last administration hadn't shown this already). The profusion of philanthropies are mere performative narcissism and white-washing, while the real damage is being done by the flood of money that flows from the rich into anti-democratic and anti-government projects across the land.

And what is all this social division accomplishing? It is not having any positive eugenic effect, if one takes that view of things. Reproduction is not noticeably affected, despite the richness at the top or the abject poverty at the bottom. It is not having positive social effects, as the rich wall themselves off with increasingly hermetic locations and technologies. They thought, apparently, that cryptocurrencies would be the next step of unshackling the Galtian entrepreneurs of the world from the oppression of national governments. Sadly, that did not work out very well. The rich can not be rich without a society to sponge off. The very idea of saving money presupposes an ongoing social and economic system from which that money can be redeemed by a future self. Making that future society (not to mention the future environment) healthy and cohesive should be our most fervent goal.

• How about universal basic services, like housing and transit?
• But not in this congress.
• Bernie is still on the case.
• Meanwhile, how does a soak-the-poor tax system sound?
• Mint that coin!
• Economic regression.

One Tough Molecule: Cholesterol

In praise of cholesterol.

Membranes are an underappreciated aspect of biology. The recent pandemic was caused by a virus that has a very sophisticated system to commandeer many aspects of our cellular apparatus, including our membrane systems, creating complicated vesicular bodies in which to develop and hide. Membranes may not have participated in the very origin of life, (which seems to have involved energy-rich mineral systems), but were essential at the origin of cells, as all cells are surrounded by a classic bilayer membrane, composed of two-faced molecules with water- soluble heads and fatty tails, the latter of which make up the middle of the bilayer.

Membranes everywhere. Eukaryotic cells are filled with membrane-bound compartments. Here, Covid-causing virus (black arrows) hides out in vesicles enclosed within additional membranes. These are post-mortem samples, examined by electron microscopy. In E, from lung cells, asterisks mark the presence of viral particles, while the number sign marks another lamellar structure of membranes involved in lung surfactant synthesis and secretion.

Membranes were also central to the next greatest innovation in life, the eukaryotic cell. Not only are eukaryotes full of membrane-bound compartments, like mitochondria, endosomes, lysosomes, endoplasmic reticulum, golgi apparatus, and others, but their membrane composition changed as well, with the advent of sterol-related molecules. Plants use phytosterols, while animals use cholesterol as an additive to their membranes. Cholesterol has gotten decades of bad press due to its association with atherosclerosis and the whole bad/good HDL story, about the particles that carry cholesterol around the body. But cholesterol is an essential and amazing molecule, painstakingly developed through evolution to strengthen our membranes and provide special nano-localization services.

Cholesterol (right) compared with a normal phospholipid that makes up the bulk of most membranes. Hydrophilic areas are in red/purple/blue, while hydrophobic areas are gray. The phospholipid is sphingomyelin, which appears to be fully saturated, meaning it has no double bonds or kinks in its hydrophobic tails. These on their own tend to be highly floppy, while cholesterol is far more structurally stable.

Cholesterol is a shockingly complex and expensive molecule to make. Its synthesis requires 37 steps, lots of molecular oxygen, and a hundred molecules of ATP. No wonder few bacteria make anything like it in such vast amounts. At the same time, there must be simpler chemicals that could afford similar functions- cholesterol is probably a relic from a lengthy exploration of membrane additives, to find one that is empirically ideal. Historically, cholesterol seems to have arisen after the general oxygenation event, enabling its peculiar synthesis, the symbiosis with mitochondria, and the evolution of eukaryotes generally. Our cells can still all make their own cholesterol, and our bodies have extensive means to regulate amounts, though evidently these mechanisms don't always work optimally for modern, aging humans. 

At any rate, it is now realized that dietary cholesterol has relatively little impact on internal levels or health outcomes. In our cells, cholesterol concentrations are rigorously controlled and highly diverse, being as high as ~40% of all lipids on the external face of the plasma membrane, while only 5% in the mitochondrial membranes. The reasons for this distribution are not entirely understood, but our genomes encode numerous proteins devoted to transferring cholesterol and phospholipids to various places and sides of membranes. A recent paper discussed the fact that cholesterol significantly strengthens membranes, allowing eukaryotes to attain the amoeboid lifestyle, rather than having to grow exoskeletons (i.e. cell walls) as bacteria generally do. 

Cholesterol makes membranes significantly stronger, less bendable, more viscous, and yet does not impair lateral fluidity.

The surface area per lipid goes down drastically (and strength and stiffness go up) as cholesterol is added to a regular phospholipid membrane. This is less meaningful than portrayed in the paper, however, since cholesterol counts as a lipid in this calculation, and with only one fat tail vs two slender tails, it is likely that the reduction in surface area arises as much from cholesterol's smaller cross-section (see cartoon above) as from its organizing / ordering effects on the neighboring phospholipids. 

Not only does it make membranes tougher, but it alters their thickness (by straightening up the phospholipid tails) and selectively prefers to bind certain partner phospholipids (sphingolipids), thereby creating nano-domains. These domains are called "lipid rafts" and at 50 nanometers across, they are exceedingly small, given that membranes are about 5 nanometers thick. These rafts are the prefered places for many hormone and immune system receptors to operate, which, when bound to their partners, lead to greater raft agglomerations that facilitate signaling and particularly the separation of some signals from others. This is just one example of the many roles that cholesterol has gained in cell and molecular biology.

Some reviewers note that while we often imagine nano-tech and nano-bots to be machines of metal, essentially miniaturized versions of our macro-tech, with tiny gears, etc., real nano tech may more properly lie in soft materials that are resilient at this scale, adapted to its challenges of constant thermal motion and mutable structure. Reeds that bend in the wind, not rocks that slowly break down in it. Membranes are being used in the form of liposomes as drug and vaccine delivery vehicles, and deserve a greater appreciation from both biological and technical perspectives.

This video, produced by detailed atomic computer simulation, illustrates how frenetic Brownian motion is. The membrane molecules (teal) are in constant motion, fending off the water molecules (red/white). The adoption of a second membrane component that intercalates, strengthens, and imposes some order here is a highly significant advancement.

• Maybe giving in to nuclear bluffing and blackmail is not a good idea.

Evolution of Dogs, and Dog Brains

Deeper genetic studies of the history of dogs reveal causal genes and pathways.

Do traits run in families? Are mental and behavioral attributes heritable? Of course they are, though well-intentioned liberals tend to argue otherwise, that everyone is the same by nature, and education, social services, and perhaps psychotherapy are the only things holding anyone back from limitless potential. Well, there is a place for both nurture and nature, but plain observation and mountains of science, such as twin studies, show that nature plays a dominant role, especially in relatively stable societies where nurture is not grossly deficient. While plenty of evidence exists for this in humans, it is particularly evident in model animals, such as those we have bred to have certain dispositions, like dogs. 

A recent landmark study on the genetics of dogs delves into some of the genetic and molecular detail of these traits. The authors find clear lineage differences between groups of dogs bred for different purposes, and dredge up a telling details about where those differences lie in the dog genome. First off, they have a wealth of data to draw from- full genomes sequenced for hundreds of dogs, and mutation variation panels for many more. They claim data from 4,261 individual dogs and 226 breeds, running the gamut from pure bred to village mutts. Wild dogs, wolves and coyotes were also added as outgroup references. 

The second big advance was to use a highly refined method of data reduction. The scale of this data is huge, and how to pull the needles of meaningful, breed- or trait-correlated variation from the haystack of backbground variation? Most of the variation they find was already present in wolves, meaning that while some new mutations occured during domestication, humans mostly spent their time selecting desirable combinations out of a very rich trove of natural variation already present from the start. The traditional way to do this is by principal component analysis (PCA), which plots the data in high dimensional space, and finds the two orthogonal axes that align with the greatest asymmetry in that data, and casts those two axes to two dimensions for visualization.

That is pretty simple, and crude, and a recent paper showed that a more sensitive way (named PHATE) to explore high dimensional data is able to uncover far more structure from it. It is just the kind of thing that these genomic scientists needed to wring more meaning from their huge data set.

Comparison of different dimensional reduction methods, from the same data set, in this case gene expression from embryonic cell types. One can easily see that PCA analysis is far less effective in revealing structure than is the newer PHATE technique.

This method, used over the dog data, yielded extremely clear differentiation between the major lineages, such as herding dogs vs retrievers vs scent hounds vs pointing dogs. As expected, the mutts, village dogs, and wolves clustered near the middle, not having traveled very far from the ancestral condition (except for one ramification along with "sight hounds", like grey hounds and other hunters, shared with Middle Eastern village dogs). Conversely, lineages like terriers formed a clearly separated path from the ancestral condition to more exquisitely bred extremes, at the ends of the distribution. Incidentally, their geographic view of this data showed that the ends of their distributions consistently were occupied by dogs bred in Britain, stemming from the virtual mania for animal husbandry and breeding (not to say eugenics) prevalent in Victorian times. Darwin was fascinated by this as well, devoting much of his "Origin" to the variation and breeding of pigeons.

Structured differences found in the genomic and other variation data gathered from thousands of dogs, of hundreds of breeds and geographic origins. The genomic data naturally fall into the breeds and types of dogs we are familiar with, while wild and feral dogs tend more to the central, ancestral areas.

This data treatment was not just done for visual clarity, but provided the clean classification that these authors could then use to search for the differentiating mutations in genomes separated by these breeding histories. They also do a bit of psychoanalysis, correlating the various lineages with major trait dimensions, such as trainability, aggressiveness, predatory drive, fear, and energy. This helped to give some rationale to aspects that various lineages might share, despite their separation in the main axes. For example, terriers had high levels of predatory chasing, while herders showed high levels of fear. This just buttresses that the dimensional reduction analysis (done on genomes) uncovered real dimensions of dog mentality, not just labeled by conventional breed types, but also by correlation with imputed general traits. What was the headline of this lineage analysis? 

"Lineage-associated variants are largely non-coding regions implicated in neurodevelopment"

There are two very interesting aspects to unpack here. First is that the vast majority of the mutations (aka variants) were non-coding. They state that of 16,250 variants that passed some threshold of statistical significance with regard to lineage divergences, only 76 were protein coding changes with any significant impact. So instead of changing proteins being made in the body, the story is one of control- the regulation over where, when, and how much of these proteins gets made. This is significant, as many genetic tests for humans are still focused on what is called the "exome", which is to say, the protein-coding parts of our genomes, where certainly many devastating mutations exist.  But it isn't where the vast majority of interesting variations occur, either for disease or particularly for normal trait variation. Those happen in the far larger and murkier regions around each gene that are strung with regulatory control sites. Mutations there can have very subtle effects.

Secondly, of course, is that they found brain and neural development genes to dominate the analysis. This only makes sense for our breeding efforts, which have had to firstly tame what was once a wolf, and then develop its talents in very particular, and sometimes peculiar directions. For instance, they note that scent / blood hounds have relatively low trainability, since they were bred to lead the way and follow their noses, not so much their humans. While the official dog shows focus on looks, coats, and colors, the much harder, and more significant job has clearly been to remake the mind of the dog to serve us. Nothing shows this more clearly than the border collie and related herders, whose ability to work with experienced handlers on difficult tasks is legendary.

The figure below gives an overview of what they found. At the top is the dog genome, with scoring of differential herding dog variants on the Y axis. Highlighted in green are genes that are mentioned below (panel C) as being quite densely involved in neural development and maintenance. Many of these are indeed very highly scoring in the genome graph, but others are less so. The authors are evidently being quite selective in calling out genes of interest, and there are many genes at least equally significant that are not being discussed. For instance, while there are by my count about 50 genes that rise to the "10" level in the graph, only seven or eight of which were called out for presentation in this neural pathways collection. And there are easily hundreds if not a thousand that satisfy the "5" level in the graph, making the selection of genes like SRGAP3 which has a score in this range somewhat willful.

Distinctive variations of sheepdogs are heavily involved in brain development, with a selection illustrated at bottom. At top is a graph of dispersion scores vs genomic location, with some genes involved in neural function called out (green). In the middle, a few of these genes are blown up to show that the variants do not generally occur in the coding regions of these genes, but in surrounding regulatory areas. At bottom is a shown an overlay of the genes found and called out above, lain over an independently curated/assembled diagram depicting molecular details of neuronal guidance, from KEGG.

At any rate, the middle panel of this diagram provides a few magnified examples of where the variations are relative to the coding regions of their respective genes. The coding regions are depicted at top with an arrow showing the start of transcription, and tiny vertical lines showing each "protein-coding" exon fragment, interspersed with large non-coding introns. Clearly the variations are clustered in the regulatory regions near, but not in, these genes.

And at bottom is a curated pathway, assembled from huge amounts of work from many labs, of some molecular aspects of axon guidance- the process by which neurons send axons out from where they start in embryogenesis to the targets, sometimes very far away in the brain, where they synapse with other neurons to make up our (or here the dog's) brain anatomy. The concentration of relevant variations in such genes speaks volumes about what has been going on in this process of rather rapid, directed evolution. The domestication of dogs is thought to have begun, very roughly, about 30 thousand years ago. The speed of this process and its resulting variety suggest (as it did to Darwin, and countless others) that evolution by natural selection has had plenty of time to work the biological wonders we see around us.

• Somewhat boring lecture on axon guidance mechanisms that allow organized brain development and maintenance.
• Social capital and social climbing.
• Eugenics, Israeli-style.
• Brothers at arms.
• Yes, genes can arise from junk DNA. And they are important genes.

A New Way of Doing Biology

Structure prediction of proteins is now so good that computers can do a lot of the work of molecular biology.

There are several royal roads to knowledge in molecular biology. First, and most traditional, is purification and reconstitution of biological molecules and the processes they carry out, in the test tube. Another is genetics, where mutational defects, observed in whole-body phenotypes or individually reconstituted molecules, can tell us about what those gene products do. Over the years, genetic mapping and genomic sequencing allowed genetic mutations to be mapped to precise locations, making them increasingly informative. Likewise, reverse genetics became possible, where mutational effects are not generated randomly by chemical or radiation treatment of organisms, but are precisely engineered to find out what a chosen mutation in a chosen molecule could reveal. Lastly, structural biology contributed the essential ground truth of biology, showing how detailed atomic interactions and conformations lead to the observations made at higher levels- such as metabolic pathways, cellular events, and diseases. The paradigmatic example is DNA, whose structure immediately illuminated its role in genetic coding and inheritance.

Now the protein structure problem has been largely solved by the newest generations of artificial intelligence, allowing protein sequences to be confidently modeled into the three dimensional structures they adopt when mature. A recent paper makes it clear that this represents not just a convenience for those interested in particular molecular structures, but a revolutionary new way to do biology, using computers to dig up the partners that participate in biological processes. The model system these authors chose to show this method is the bacterial protein export process, which was briefly discussed in a recent post. They are able to find and portray this multi-step process in astonishing detail by relying on a lot of past research including existing structures and the new AI searching and structure generation methods, all without dipping their toes into an actual lab.

The structure revolution has had two ingredients. First is a large corpus of already-solved structures of proteins of all kinds, together with oceans of sequence data of related proteins from all sorts of organisms, which provide a library of variations on each structural theme. Second is the modern neural networks from Google and other institutions that have solved so many other data-intensive problems, like language translation and image matching / searching. They are perfectly suited to this problem of "this thing is like something else, but not identical". This resulted in the AlphaFold program, which has pretty much solved the problem of determining the 3D structure of novel protein sequences.

"We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14), demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods."

The current authors realized that the determination of protein structures is not very different from the determination of complex structures- the structure of interfaces and combinations between different proteins. Many already-solved structures are complexes of several proteins, and more fundamentally, the way two proteins interact is pretty much the same as the way that a protein folds on itself- the same kinds of detailed secondary motif and atomic complementarity take place. So they used the exact AlphaFold core to create AF2Complex, which searches specifically through a corpus of protein sequences for those that interact in real life.

This turned out to be a very successful project, (though a supercomputer was required), and they now demonstrate it for the relatively simple case of bacterial protein export. The corpus they are working with is about 1500 E. coli periplasmic and membrane proteins. They proceed step by step, asking what interacts with the first protein in the sequence, then what interacts with the next one, etc., till they hit the exporter on the outer membrane. While this sequence has been heavily studied and several structures were already known, they reveal several new structures and interactions as they go along. 

Getting proteins from inside the cell to outside is quite complicated, since they have to traverse two membranes and the intermembrane space, (periplasm), all without getting fouled up or misdirected. This is done by an organized sequence of chaperone and transport proteins that hand the new proteins off to each other. Proteins are recognized by this machinery by virtue of sequence-encoded signals, typically at their front/leading ends. This "export signal" is recognized, in some instances, right as it comes out of the ribosome and captured by the SecA/B/E/Y/G machinery at the inner bacterial membrane. But most exported proteins are not recognized right away, but after they are fully synthesized.

The inner membrane (IM) is below, and the outer membrane (OM) is above, showing the steps of bacterial protein export to the outer membrane. The target protein being transported is the yellow thread, (OmpA), and the various exporting machines are shown in other colors, either in cartoon form or in ribbon structures from the auther's computer predictions. Notably, SurA is the main chaperone that carries OmpA in partially unfolded form across the periplasm to the outer membrane.

SecA is the ATP-using pump that forces the new protein through the SecY channel, which has several other accessory partners. SecB, for example, is thought to be mostly responsible for recognizing the export signal on the target protein. The authors start with a couple of accessory chaperones, PpiD and YfgM, which were strongly suspected to be part of the SecA/B/E/Y/G complex, and which their program easily identifies as interacting with each other, and gives new structures for. PpiD is an important chaperone that helps proline amino acids twist around, (a proline isomerase), which they do not naturally do, helping the exporting proteins fold correctly as they emerge. It also interacts with SecY, providing chaperone assistance (that is, helping proteins fold correctly) right as proteins pass out of SecY and into the periplasm. The second step the authors take is to ask what interacts with PpiD, and they find DsbA, with its structure. This is a disulfide isomerase, which performs another vital function of shuffling the cysteine bonds of proteins coming into the periplasmic space, (which is less reducing than the cytoplasm), and allows stable cysteine bonds to form. This is one more essential chaperone-kind of function needed for relatively complicated secreted proteins. Helping them form at the right places is the role of DsbA, which transiently docks right at the exit port from SecY. 

The author's (computers) generate structures for the interactions of the Sec complex with PpiD, YfgM, and the disulfide isomerase DbsA, illuminating their interactions and respective roles. DbsA helps refold proteins right when then come out of the transporter pore, from the cytoplasm.

Once the target protein has all been pumped through the SecY complex pore, it sticks to PpiD, which does its thing and then dissociates, allowing two other proteins to approach, the signal peptidase LepB, which cleaves off the export signal, and then SurA, which is the transporting chaperone that wraps the new protein around itself for the trip across the periplasm. Specific complex structures and contacts are revealed by the authors for all these interactions. Proteins destined for the outer membrane are characterized by a high proportion of hydrophobic amino acids, some of which seem to be specifically recognized by SurA, to distinguish them from other proteins whose destination is simply to swim around in the periplasm, such as the DsbA protein mentioned above. 

The author's (computers) spit out a ranking of predicted interactions using SurA as a query, and find itself as one protein that interacts (it forms a dimer), and also BamA, which is the central part of the outer membrane transporting pore. Nothing was said about the other high-scoring interacting proteins identified, which may not have had immediate interest.

"In the presence of SurA, the periplasmic domain [of transported target protein OmpA] maintains the same fold, but remarkably, the non-native β-barrel region completely unravels and wraps around SurA ... the SurA/OmpA models appear physical and provide a hypothetical basis for how the chaperone SurA could prevent a polypeptide chain from aggregating and present an unfolded polypeptide to BAM for its final assembly."

At the other end of the journey, at the outer membrane, there is another channel protein called BamA, where SurA docks, as was also found by the author's interaction hunting program. BamA is part of a large channel complex that evidently receives many other proteins via its other periplasmic-facing subunits, BamB, C, and D. The authors went on to do a search for proteins that interact with BamA, finding BepA, a previously unsuspected partner, which, by their model, wedges itself in between BamC and BamB. BepA, however, turns out to have a crucial function in quality control. Conduction of target proteins through the Bam complex seems to be powered only by diffusion, not by ATP or ion gradients. So things can get fouled up and stuck pretty easily. BepA is a protease, and appears, from its structure, to have a finger that gets flipped and turns the protease on when a protein transiting through the pore goes awry / sideways. 

The author's (computers) provide structures of the outer membrane Bam complex, where SurA binds with its cargo. The cargo , unstructured, is not shown here, but some of the detailed interface between SurA and BamA is shown at bottom left. The beta-barrel of BamA provides the obvious route out of the cell, or in some cases sideways into the membrane.

While filling in some new details of the outer membrane protein export system is interesting, what was really exciting about this paper was the ease with which this new way of doing biology went forth. Intimate physical interactions among proteins and other molecules are absolutely central to molecular biology, as this example illustrates. To have a new method that not only reveals such interactions in a reliable way, from sequences of novel proteins, but also presents structurally detailed views of them, is astonishing. Extending this to bigger genomes and collections of targets, vs the relatively small 1500 periplasmic-related proteins tested here remains a challenge, but doubtless one that more effort and more computers will be able to solve.

• Keep supporting Ukraine.
• A coda from Brian Wilson.
• Loneliness.
• Research is facing diminishing returns.
• Some very curiously advanced bacteria.