A.I. Does STEAM-

(Well, the Science and Art parts, anyway! This is originally a year-old story, republished by Cosmos today. I scouted around for some sort of an update, but didn’t find one. I still thought this is interesting, and at least now we know another area in which A.I. might be applied. I think that’s good to know, since A.I. does make mistakes, as noted below.)

The artificial intelligence program AlphaFold is proving to be a gamechanger for biological research, Imma Perfetto reports. This article was originally published in the Cosmos Print Magazine, September 2024.

October 11, 2025 Imma Perfetto

This artwork of an origami bird holds AlphaFold 3 predictions of a complex of two proteins (ScpA and ScpB) in its beak. The protein complex is important during cell division in bacteria. Top: ScpA is cyan and ScpB is green. Bottom: Confidence measures, where dark blue is very high confidence, light blue is confident, yellow is low confidence, and orange is very low confidence in the structural prediction. Credit: AlphaFold 3, Katie Michie.

A protein is made from of a chain of amino acids strung together like beads on a necklace. This chain spontaneously folds, like origami, into intricate pleats, folds, and loops through interactions between its amino acids. The resulting unique 3D structure largely determines its vital function within the lifeform. Solving the structure allows biologists to better understand how the protein works and design experiments to affect and modify it.

The smallest known protein, TAL, influences development of the fruit fly Drosophila melanogaster and has just 11 amino acids. The largest, Titin, is found in human muscle cells and is made up of roughly 35,000.

Proteins are far too tiny to inspect under a regular microscope. For decades researchers used complex experimental techniques, such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryogenic electron microscopy (cryo-EM) to solve their structures. It’s painstaking, time-consuming work that takes specialised skill and sometimes hundreds of thousands of dollars. And, as Kate Michie can attest, success is not always guaranteed.

“I spent four years trying to solve the crystal structure of a complex of two human proteins and got scooped. You know, I got nothing out of four years. I worked really hard at it, and it was a really difficult project. AlphaFold can calculate those in a few hours,” says Michie, who is chief scientist of the Structural Biology Facility at the Mark Wainwright Analytical Centre, of the University of New South Wales Sydney.

On 8 May 2024 Nature dropped a paper introducing the third and latest iteration of the artificial intelligence (AI) system AlphaFold, which predicts the 3D structure of proteins from their amino acid sequences. Google DeepMind and Isomorphic Labs, both subsidiaries of Alphabet, co-developed the new model. They say AlphaFold 3 (AF3) is “a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy”. But, while AF3 has generated significant interest since its release, it has simultaneously sparked criticism among those in the scientific community.

Let’s take a closer look at how AI is changing the world of structural biology.

A revolution in protein structure

AF3’s predecessor, AlphaFold 2, was released as open source code in July 2021 and immediately changed the game in structural biology.

“I contacted the high-performance computation people and said, ‘we really need to get this piece of code running’. And then I asked my colleague, ‘Do you have any structures that you never submitted to the Protein Data Bank?’” says Michie.

The Protein Data Bank (PDB) is the global archive of all the experimentally solved structures for large biological molecules. As of June 2024, its estimated to include more than 220,000 proteins, which sounds like a lot until you consider the number of proteins we know of exceeds 200 million.

“My colleague sent me a sequence of a small protein he never submitted to the PDB, I ran it, and I just sent him the result. His email response to me was: ‘My mind is blown!’ And he said, ‘I immediately thought someone else must have solved the structure.’”

But they hadn’t, AF2 had accurately predicted the 3D structure of the protein from its amino acid sequence alone. What had taken years to describe experimentally had been done in just a few hours.

AF2 is a deep learning algorithm. In the world of AI that means it simulates the neural networks found in human brains. First, it takes the protein sequence of interest and searches several databases for similar proteins. By comparing these sequences, it can identify areas of similarity and difference to understand how the protein has changed across evolution.

For instance, if two amino acids are in close contact in 3D space then a mutation in one will usually be accompanied by a mutation in the other (to conserve the structure of the protein). But if they are far apart then they tend to evolve independently from each other. Using this to work out the relative positions of the amino acids, AF2 then takes its training on PDB structural data and iteratively constructs a 3D model of the protein’s structure with relatively high accuracy.

Scientists can take advantage of that predicted structure to accelerate their science by doing smarter, more strategic experiments in the laboratory right off the bat. “I’ve done work with some scientists working with immune complexes, and the models coming out of AlphaFold enable them to really trim down the number of animal experiments they do,” says Michie. “So instead of making say 20 CRISPR mice, they only might make two.”

Fos and jun transcription factors coloured in blues, yellow and orange depending on their confidence. A green box in the right is shaded to indicate binding confidence. — As seen in AlphaFold 3, a structural prediction of Fos and Jun transcription factors with the DNA sequence they bind. The top panel shows the model and confidence data, and the green chart shows the high confidence of them binding to each other. Credit: AlphaFold 3, Katie Michie.

Crystal clues

An accurate AlphaFold structure can also be the crucial missing piece of the puzzle that allows researchers to experimentally solve the structure using X-ray crystallography.

“One of my other colleagues is virologist and he’d been working on a protein that had eluded structural elucidation for 20–30 years. It was from the world’s first known retrovirus,” says Michie.

“The trick of crystallography is you need to know two components of the maths to solve them,” she continues. The diffraction data provided by X-ray crystallography gives you one of those components, but you don’t have the other: the phase.

Traditional methods of obtaining phase information had proved unsuccessful, until Michie suggested using AlphaFold instead.

“Immediately the structure came out. AlphaFold helped him get the crystals but then actually enabled him to phase the structure. It told us that the Alpha Fold model was very good, but it also fixed up this problem in structural biology.”

To Michie, AlphaFold represents a massive step forward: “it’s genuinely the biggest scientific advance in my career”.

“The Alpha Fold model was very good, but it also fixed up this problem in structural biology.”

Predicting the structures of life’s molecules

Proteins don’t exist in a vacuum. They move around, bind to and modify each other, and even form large, complicated complexes.

Peter Czabotar, joint head of the Structural Biology Division at WEHI, the oldest medical research institute in Australia, says one of the early limitations of AF2 was you could only ever get structural predictions of one protein, alone. “Often what you’re interested in is how different proteins will interact with each other. For example, we work on proteins that are involved with cell death and the interactions between those proteins will dictate whether a cell will live or die.”

The gap has since been bridged by other research groups adapting and building upon AF2’s open source code, and with the AlphaFold-Multimer extension in October 2021.

The newest version, AF3, extends upon this capability by predicting interactions of multiple proteins, and nucleic acids (DNA and RNA). It can predict the impact of ions and post-translational modifications – the addition of chemical groups to amino acids – on these molecular systems too. AF3 can also be used to predict how a selection of small molecules called ligands bind to proteins, though this is restricted to ligands that have high-quality experimental data available in the PDB.

“But where the real power is, something that we do a lot of, is in the drug discovery world,” says Czabotar. “And it is extremely powerful for that, potentially, but they haven’t enabled that in the way that it’s released. We’ve done drug discovery against cell death proteins, for example. I can’t take one of the drugs that we’ve worked with and see how it interacts with my target protein, I can only use the [ligands] that they’ve enabled us to use.”

That capability to predict the structure of novel drug molecules interacting with target proteins seems to be restricted to Isomorphic Labs, which was launched in 2021 to pursue commercial drug discovery.

AF3 uses a very different approach for this new suit of predictions: generative AI. After processing the sequence inputs, it assembles its predictions using a diffusion network, the likes of which power AI image generators. According to Isomorphic Labs’ website: “the diffusion process starts with a cloud of atoms, and over many steps converges on its final, most accurate molecular structure”. Diffusion has been applied to protein structure prediction before, for example, in the seminal RoseTTAFold diffusion (RFdiffusion) by the Baker Laboratory at the Institute for Protein Design, the University of Washington.

But generative AI is not without its limitations. AF3 will occasionally produce structures with overlapping atoms (this is physically impossible) or replace a detail of the structure with its mirror image (chemically impossible). As a generative model, it is also prone to hallucinations in which it invents plausible-looking structures – particularly in disordered regions of the protein that lack a stable 3D structure – similarly to how a text to image AI struggles to create realistic-looking hands. In-built confidence measures help to identify when AF3 isn’t so sure about its structural prediction, but ultimately it takes a scientist with understanding of the underlying structural biology to come along and identify what’s gone wrong, and why.

“It’s very, very powerful. But it doesn’t exclude the need to necessarily confirm things experimentally. Whether that is by solving structures themselves or by, for example, testing the structures in some way in an experiment,” says Czabotar.

Concerns about code

In a major departure from AF2, access to the newest iteration of AlphaFold is limited to a web server and for non-commercial research only. “We have various structure-based drug discovery projects and some of them are purely academic, as students, PhDs and honours projects. But we also have had commercial partnerships, because that’s a way to push your discoveries into a clinical setting,” says Czabotar. “So generally, anything that is going to make an impact is done by an academic lab in a commercial partnership. Now, I guess it puts us in a bit of an awkward situation. Even if we could look at our compounds bound to the target [protein], there’s some projects where we won’t be able to do it because, you know, we’ve ticked a box.”

AF3’s accompanying Nature paper was also published without the source code, but with a ‘pseudocode’ instead – a detailed description of what the code can do and how it works. This prompted an open letter to the Editors of Nature, published 16 May and endorsed by more than 1,000 scientists as of June.

The letter raised concerns that “the absence of available code compromises peer review” and that the pseudocode released would “require months of effort to turn into workable code that approximates the performance, wasting valuable time and resources”. Access to the web server was also initially capped at 10 predictions per day, which the letter stated, “restricts the scientific community’s capacity to verify the broad claims of the findings or apply the predictions on a large scale”.

The sentiments appear to have hit home. Shortly after the letter’s release, DeepMind’s Vice President of research, Pushmeet Kohli announced via X that they would double the daily job limit to 20 and are “working on releasing the AF3 model (incl weights) for academic use … within 6 months”.

On 22 May Nature responded in an editorial, stating its reasoning for publishing the paper without code: “the private sector funds most global research and development, and many of the results of such work are not published in peer-reviewed journals. We at Nature think it’s important that journals engage with the private sector and work with its scientists so they can submit their research for peer review and publication.”

In the meantime, other researchers won’t be sitting idly by until the code release at the end of 2024. Already, multiple teams are racing to develop their own open source versions of AlphaFold 3, without any strings attached.

Good News:

Students With Hearing and Vision Loss Get Funding Back Despite Trump’s Anti-DEI Campaign

Following public outcry, the Department of Education has reversed its decision to cut funding for students who have both hearing and vision loss, opting instead to reroute grants to an organization that will provide funding to these students.

by Jodi S. Cohen and Jennifer Smith Richards

Following public outcry, the U.S. Department of Education has restored funding for students who have both hearing and vision loss, about a month after cutting it.

But rather than sending the money directly to the four programs that are part of a national network helping students who are deaf and blind, a condition known as deafblindness, the department has instead rerouted the grants to a different organization that will provide funding for those vulnerable students.

The Trump administration targeted the programs in its attacks on diversity, equity and inclusion; a department spokesperson had cited concerns about “divisive concepts” and “fairness” in explaining the decision to withhold the funding.

ProPublica and other news organizations reported last month on the canceled grants to agencies that serve these students in Oregon, Washington and Wisconsin, as well as in five states that are part of a New England consortium.

Programs then appealed to the Education Department to retain their funding, but the appeals were denied. Last week, the National Center on Deafblindness, the parent organization of the agencies that were denied, told the four programs that the Education Department had provided it with additional grant money and the center was passing it on to them.

“This will enable families, schools, and early intervention programs to continue to … meet the unique needs of children who are deafblind,” according to the letter from the organization to the agencies, which was provided to ProPublica. Education Department officials did not respond to questions from ProPublica; automatic email replies cited the government shutdown. (snip-MORE)

Bari Weiss: Last Week Tonight with John Oliver (HBO)

This is the same Bari Weiss that is rabidly anti-trans and a religious racist bigot. She is often used as a warrior to get the crimes against trans kids out, and Teldeb that used to come here and spew Weiss’s lies. No matter who many times I debunked and showed that everything Weiss had reported was lies and misinformation rabid trans haters like Teldeb kept pushing her lies. Because the truth doesn’t matter to them, making sure no child can be who they really are or fit the mold they demand children fit in. Now it is trans kids but as we have seen in the US they are coming for every not straight cis kid demanding they fit into the regressive world they demand everyone live in. Weiss is also a Jewish person who is an Islamophobe. She supports the genocide in Gaza. Hugs

“Rarest of Its Kind”

Lava Gull

An Interesting Bit!

Telling of the Scuba Spider & the Slow-Motion Climate Crisis Storm by Jerileewei

How a French Quarter Phantasm Teaches Writers to Stop Drowning Their Audience Read on Substack

Recently some of the Cajun Chronicles Podcast Corporation writer staff enjoyed a well attended writers conference at a ritzy island resort about as far away from Louisiana as you can get. Some of us were aware of the show Mother Nature was putting on there. Not only in terms of their native flowers and fruits, but also the job certain natural Apex Micro-Predators play around the world in the grand scheme of pest control and climate change globally.

Once home, those lessons and lessons about writing creative technical content were sources of wonderment and inspiration. Louisiana is no stranger to all things buggy, nor the climate change side-effects we have always been experiencing with rising waters all around us. Similarly, those among us struggle with solutions to writing and broadcasting the messages we all need to heed on such important topics.

Great Heron casting a scary shadow over the bayou for the Scuba Spider.

A Fishing Spider Story Exercise In Creative Nonfiction Oddity

The thing about the Louisiana bayou country is that its weirdness is not just for show, cher. It’s a matter of absolute, high-stakes survival. It is an ecosystem that has perfected the art of the improbable. Take the Dark Fishing Spider, Dolomedes tenebrosus, the one whose leg span can cover half your hand. She is one of the largest spiders in North America, yet she operates with the silent precision of a naval scout.

You’re floating placidly in the moss-draped gloom of the Atchafalaya Basin, and there she is, perched carrément (directly) on a gnarled bald cypress knee. Her nickname is Scuba Spider. Unlike her cousin, the Six-spotted Fishing Spider (D. triton), who is a permanent waterside resident, D. tenebrosus often wanders about. She’s basically a French Quarter phantasm land tourist with aquatic superpowers. Uniquely, her front four long legs still rest on the water like silent radar antennae.

Here’s the first oddity: She doesn’t spin a trap-web to catch supper. She uses the very surface of the water as a vast, vibrating, liquid snare. That surface tension, which allows a single droplet of dew to hold its perfect sphere, is her hunting ground. To your amazement, a Yellow Fever (Aedes aegypti) mosquito lands, an unlucky Cocahoe Minnow (Fundulus grandis) minnow surfaces, or you see a mayfly struggling.

Those water disturbances, even a tiny ripple, are all the information she needs. She bolts across the water, comme ça (like that), defying gravity and the laws of physics with a waxy-haired gait, grabs her prey, and retreats just as swiftly. She is an apex-predator extraordinaire! As an Eight-Legged Lagniappe

The truly bizarre part of her story happens when danger comes. If a hungry Great Heron swoops too close, or a massive Alligator Gar glides by, this spider doesn’t run toward the shore. She, as we say in Cajun French, simply plonges (plunges/dives). Happily, for her, she’s not drowning. She’s engaging in a peculiar act of biological brilliance.

Her entire body is covered in fine, dense hairs. As she slips beneath the surface, these hairs trap a thin, glistening layer of air, her personal silvery scuba suit, that surrounds her like a portable bubble. She becomes a living submarine. She can cling to an underwater root, or the submerged bark of a Bald Cypress tree.

There she sits, breathing her little pocket of swamp-air, and waiting out the trouble for up to half an hour. She makes the L’Affaire Fini threat simply disappear. That fact, c’est vrai (that’s true), is a mighty fine trick.

Now, here is where the bayou’s natural spider oddity connects to a deeper, more human reality. She shows how to tell scientific facts about climate change and its effect on nature factually without putting your audience to sleep. That’s because the constantly-evolving existential crisis of the climate often feels a lot like that of the ol’ White Heron. It’s a huge bad case of the vois-là, an inevitable danger that you can’t run away from.

The way some creative technical writers are trying to capture that reality is just as strange as a certain spider species’ scuba dive. When you can’t outrun the misère (misery/trouble), you have to find a new way to tell the story.

Silloette of Great Heron and its shadow over the image of a sinking Louisiana into the bayou and a Scuba Spider.

This is so much like very act of writing creative nonfiction through the climate crisis has its own set of odd, profound, and fun facts:

Odd Fun Facts of Writing the Existential Reality

1. The “Slow Violence” Problem Demands New Forms

The climate crisis rarely involves a neat, dramatic explosion. It’s mostly “slow violence.” The gradual, almost invisible rising of the water, the creeping salinity, the erosion of the marsh. The odd challenge for the Louisiana writer, is that they have to invent entirely new, often experimental, narrative techniques just to make a slow-motion disaster feel as urgent as a gunshot.

This is why you sometimes see writers like us using techniques like fractured chronology, list-memoirs, or braided essays. They are desperate attempts to make the un-dramatic and continuous nature of environmental trauma feel viscéral (visceral) to the reader.

2. The Rise of the “Carrier Bag Narrative”

Forget the epic traditional story of the single hero conquering the storm. Many climate writers are advocating for author Ursula K. Le Guin’s concept of the “Carrier Bag Theory of Fiction.” The odd fun fact here is that the best climate stories shouldn’t have a single, satisfying plot arc (a triumph!). They should be a messy “bag” full of diverse voices, ongoing processes, small acts of loss, and fragments of hope.

Strive for mirroring the complex, non-linear reality of the crisis. This form rejects the idea that a single person can ‘solve’ the problem, instead emphasizing the power of collective, ongoing endurance. (snip)

Now Here’s A Thing We Can Use, From Carl Sagan, Bless Him!

Carl Sagan’s Baloney Detection Kit: Tools for Thinking Critically & Knowing Pseudoscience When You See It

Treaties, Illegal Weapons Sales, & More In Peace & Justice History for 10/10:

https://www.peacebuttons.info/E-News/peacehistoryoctober.htm#october10

Representation In Literature

The Big Idea: Courtney Floyd

Posted on October 8, 2025 Posted by Athena Scalzi

Though neurodivergent people tend to love the world of academia and absorbing information, the systems and structure of higher education is often antithetical to the needs of differently abled people, both mentally and physically. Author Courtney Floyd expands on this in the Big Idea for her newest novel, Higher Magic, as she recounts her experience with earning her PhD and seeing how the world of education wasn’t designed with inclusivity and accessibility in mind.

COURTNEY FLOYD:

When I first sat down to write the first draft of Higher Magic, I was two years out of my PhD program and still trying to balance the sum of my time there. My sense of the possible had shifted profoundly as I studied literature, learned to research, traveled to conferences and archives, and honed my analytical and interpretive skills. My life had changed for the better. But I was still discovering the many ways my program had taught me to ignore my body and push through exhaustion and anxiety, no matter the cost.

In higher education, you’re supposed to act as though you’re nothing but a floating brain. Oh, nobody ever says that outright. Especially not when you’re a first generation student who slid sideways into the academy and, to everyone’s bewilderment, stuck around. But the expectation is there. Lurking.

I learned to see it sidelong, in the way I was expected to write without using the first person and also in the lack of understanding some professors showed when I couldn’t attend office hours or study groups because I was juggling several jobs to pay my tuition. It reared its head in my mentor’s office, when she snapped impatiently at me because I got jury duty, and couldn’t defer it. It showed up with the brain fog and intense hand cramps after two-hour midterms in which I had to handwrite entire essays.

I came to see it even more clearly as an instructor, in the way boilerplate attendance policies penalized students who were late because of health issues or irregular bus schedules. It haunted me, one term, when one of my students––a veteran who’d recently undergone major surgery––apologized for every single essay he turned in, not because it was late but because he was worried his medication had made him incoherent.

By the end of my time in grad school, I saw the floating brain edict at work every day. In the exam prep or the job search eating up my own and my peers’ lives, turning us into bleary-eyed shadows. In the exhausted way my officemate staggered back from her two week maternity leave, which we’d gone on strike only a year earlier to get. In the student in my cohort who weighed the cost on her mental health and withdrew from the program.

Mind over matter is a brutal either/or.

Either you’re smart enough to figure it out, or you’ll drop out. Either you’ll burn your candle at both ends, or you’ll snuff yourself out trying.

In her book Teaching to Transgress, Black feminist scholar and educator, bell hooks, writes that in classrooms and other institutionalized spaces, “the person who is the most powerful has the privilege of denying their body,” of becoming the invisible default. The cog at the center of the complicated machine. But, as we’ve seen in the past couple of years, when our bodies become too inconvenient–too vocal or visible or vexing–the people in power (in and beyond the ivory tower) can decide to deny our bodies, too. Or make them disappear.

In SFF, we love a good literalized metaphor. When I first had the idea for Higher Magic, graduate students weren’t being literally disappeared for protesting, but students were being quietly pushed out of the academy for needing access and inclusion. For needing systems built to support white, male, nondisabled scholars to change, just a little, so that others could participate.

Fresh out of PhD school in 2019, I knew I wanted to write about that kind of disappearing. Because bell hooks didn’t just pinpoint a problem, she shared a solution, too: “Once we start talking in the classroom about the body, and about how we live in our bodies, we’re automatically changing the way power orchestrate[s] itself.”

Enter Dorothe Bartleby, a first-generation, neurodivergent grad student who is trying her best to be a floating brain at the start of Higher Magic. She quickly learns it’s not sustainable, and spends the rest of the book slowly figuring out how to be a body and a brain at the same time. While tracking down her disappearing students. And getting ready for her last attempt at passing her qualifying exam. (snip-go finish the rest on the page!)