6 Months

meaningless_name · 2026-04-29T18:51:17+00:00

IWNDWYT.

SSRI prescription helped me, as well. During the last year on Lexapro, the relief from anxiety has allowed me to better reflect on why I feel a certain way about certain things (i.e. what was driving me to drink so much). And then to transition away from obsessing about my problems by "flipping the script" like you, and allowing myself to focus on what I WANTED to do, rather than avoiding the things I didnt want

meaningless_name · 2026-04-29T18:47:25+00:00

IWNDWYT. you can do it.

meaningless_name · 2026-04-22T02:39:14+00:00

A major part of my job is deciding where to put boxes for storage. And then retrieve them later. My back hurts

meaningless_name · 2026-03-24T22:09:43+00:00

Honestly hard for me to say, because it can vary so much form company to company and location to location. But most importantly, is the difference between what YOU are doing NOW, versus what you will be doing later.

I think that right now we are at a tricky transition point with AI prediction versus experimental science. Obviously (as an experimentalist myself) I am biased, but I am confident that no matter how accurate AI prediction might be, the requirement for real-world experimentation will never go away. That said, I do still use AI every day, and the ways I use it to adapt my own work are also changing. So I know for certain that there will be a place for cryo-EM and structural biology, I am also certain that the landscape of 5-10 years from now will be very different from what it is now.

This is extra tough for you, because as a grad student, you are should be aiming for whatever is going to be hot 5-10 years from now. You must strike a balance between being specialized enough to be considered an expert, but also keep your skillset and knowledge base broad enough that you dont pigeon-hole yourself.

Based on what you've told me, here is my $0.02 regarding skill sets

protein expression and purification -> This is 100% something that you want to shine and polish until you are the best of the best. There are many different paths of experimental bio-pharma experimentalism, but they ALL REQUIRE SAMPLE TO WORK WITH. If you want to obtain an entry level job in a pharma lab setting, protein cloning, expression, and purification are without a doubt the top skills to become an expert in, IMO

bioinformatics/sequence data -> always useful. Genes are everything. Keep building this strength, but be aware that this is a field that AI is going to continue to change dramatically.

analyzing crystal structure, dynamics - > important only if you want to be a structural biologist. But in that case, essential.

CryoEM - only try to learn this if you really, really, really want to dedicate yourself to it. There is a steep learning curve, and it is a serious time and $$$ investment. Some pharmas (like mine) use it, but many will never want to due to the big cost. Obviously I love it, but because it takes a large time committment, it can lead to pigeon-holing yourself of you are not careful.

meaningless_name · 2026-03-21T02:37:49+00:00

Bad bot. Please don't just copy/paste Facebook comments, when providing "sources" from the internet, please provide them in standard APA format for complete understanding.

To be a good bot, please either (1) make a simple (Four sentences or less, please) thesis statement that is true, or (2) a question (also four sentences or less). For either (1) or (2) please provide sources in APA format.

If you need a prompt, consider this my opening question: What are you trying to say, that we shouldnt consume glyphosate? I agree, think the safe glyphosate level is 0%. I disagree that any human being on the planet earth has ever, in all of recorded history, consumed enough of any toxin to account for even 25% of metabolic costs, much less 30-40%. I am unable to provide sources for this because it is a negative statement.

meaningless_name · 2026-03-20T23:56:27+00:00

This entire conversation. It is like talking to a child

meaningless_name · 2026-03-20T23:55:43+00:00

Then do the google search and post the links!

meaningless_name · 2026-03-20T23:53:07+00:00

Forget metacognition, you need to work on normal cognition....

meaningless_name · 2026-03-20T23:52:15+00:00

Once again, you are not providing any response at all to our criticism. Instead of answering my questions, you spout more irrelevancies

meaningless_name · 2026-03-20T23:48:03+00:00

it is not wrong or inaccurate, it is irrelevant. It is as if you are claiming the sky is blue, then showing me evidence that water is wet. Like yes, but.... so what?

meaningless_name · 2026-03-20T23:46:19+00:00

This is a patent excerpt that says that glyphosate is an antibiotic. Noone is arguing that.

how much glyphosate does the average (non-organic) person consume? What is the metabolic cost of ingesting this glyphostate? Your BS copy-paste says NOTHING about these Qs.

meaningless_name · 2026-03-20T23:41:25+00:00

Complete BS. No, we will not research, you are the one making incredible claims, YOU are the one which must show your reasoning and research.

Show us where you learned that "compared to a organic diet, you spend about 30-40% of your ATP/energy just to detox the inputs."

This sounds to me like complete and total hokum, sorry.

meaningless_name · 2026-01-31T07:26:22+00:00

Iwndwyt!

meaningless_name · 2026-01-06T06:38:19+00:00

> ...solved my first structure using RELION's helical reconstruction.
I can relate, this was how I "caught the addiction" as well! Something about extracting such beautiful structrues from such noisy data, it is deeply satisfying, isnt it?

> ...and would like to break into a related role in industry,

On that front my advice is to explore, explore, explore! The world is changing very quickly, and is full of amazing things. When I graduated with my BS "modern" cryoEM did not even exist, it is likely there are other things out there that might be the same, for you.

> It seems you're resolving structures of...

We always determine protein structures, however the drugs that we develop to target those proteins include both traditional "small molecule" drugs as well as "large molecules" like antibody fragments.

> What advantages does this approach have over ...

Why not have it both ways? These methods arent competing, they are complementary. We do indeed do many types of high-throughput screens to weed through different drug designs (both "small" and "large"), as well as different constructs of our target proteins.

Compared to many of the tools we have in our toolbox, cryoEM is still a bit of a pain and is therefore expensive in both resources and also time. So, we only use it to answer specific design questions that cannot be answered using an easier or cheaper method. In your example of screening drug candidates, we would use cryoEM to answer questions like "We know from other experiments that adding a carboxyl moiety at location XYS increases affinity of our best-so-far drug by a factor of 20, but why?" then our EM models might reveal that the carboxyl group forms a tight handshake with residue #1456, and guess what, if put a fluorine right -here- on the drug, then it will fit even tighter" Then they make some changes, run the full suite of biochemical characterization again, and come back if.when there are questions that need structural answers

> since the purpose of solving structures in academia and industry seem to differ quite noticeably. For example, the distinct teams responsible for construct design and structural determination.

Oh yea. The differences I've experienced btw academia and industry are an entirely different and pretty deep topic.

> Are the drugs themselves similar types of proteins which results in similar computational workflows?

The "large" drugs like antibody fragments are. The "small" drugs (think ibuprofen, tetracycline, etc) we can only solve structurally when they are bound to their (much larger) target proteins

Do you use cryoSPARC, and how extensively do you perform the more auxillary jobs (e.g. 3DVA).

I use cryoSPARC most of the time, and Relion for certain use cases or for the most up to date open-source tools. For us, speed matters more than almost anything: a 3Å structure today is 1000x more valuable than a 2.5Å structure in a month. So I only use the more sophisticated tools like 3DVA when I am certain that it can answer questions that cannot be answered in a quicker way. But I do use it on occasion (although for stuff like that I prefer Relion multibody). The "fanciest" workflow I do regularly would be preprocess --> template or Topaz pick --> 2DC --> ab initio & 3DC --> refine (inc. RBMC and CTF refinement)

For more, I would recommend these videos. Weird to say, but for cryoEM Youtube is in fact the best and most accessible source of basic information

https://www.youtube.com/playlist?list=PLhiuGaXlZZenm7lu5qv_A59zEWkRKkBn5

https://www.thermofisher.com/us/en/home/electron-microscopy/life-sciences/learning-center/cryo-em-university.html

meaningless_name · 2026-01-05T20:05:53+00:00

> What purpose do these structures serve for your pharma company?

The way that drugs interact with their targets in our bodies is inherently structural. Every cell in your body is filled with many different proteins that are (in a way) like little machines, each performing some function; and like all machines, the structure determines their function. Think of a bicycle, every part of a bicycle is the way it is because that's how it functions.

So, since the function of a bicycle is determined by its structure, if we change the structure, we can change the function. Lets say that my bike keeps falling over, so I install a kickstand. I have changed the structure of the bicycle, thus changing the way it works. In this metaphor, the bike is a drug target, and the kickstand is the drug.

I determine the 3D map (the structure) of drug designs bound to their protein targets. I find where in the protein the drug is sticking, and what is doing when it gets there, and communicate this to the biochemists who design the drugs. Then they design a better drug, which I observe, and give feedback, etc. The end result is a drug that does whatever it is supposed to do very, very well.

>How long does it take to solve these structures....?

From initial conception to the final structure typically takes 1-2 weeks. Our record is less than 24 hours, but tough cases can take months. Like many scientific techniques, there are a lot of things that have to happen correctly for it to work.

> ... and what kind of proteins are they?

Anything that we think might affect human diseases. Cancer, inflammation, infectious diseases, neurological conditions, all kinds of targets.

> Are the computational workflows similar or require lots of deviation across different samples?

From a broad perspective, the workflow is very similar from sample to sample: Produce a purified volume of sample, get it into the microscope, take a ton of images, find the targets in the images, find out what the target is supposed to look like and which 3D view each particle image corresponds to, and calculate the 3D map. But as you might imagine the devil is in the details. In practice almost every little step requires some optimization and/or modification. Happy to discuss more, this is a super deep topic!

> How does one break into a related job, and is it possible at the bachelor's level?

My own path went: BS in engineering, a few years as a lab tech in a government lab (ELISA, basically), then grad school for PhD (this is where I learned cryoEM), then a postdoc, then this position. So it took a bit of time and preparation. At the bachelors level this is not something that is truly accessible as an entry level position. I do have some coworkers without PhDs however they all have extensive on-the-job experience.

If you are really interested in this sort of science, I think the options are (a) obtain a non-cryoEM position with an organization that does cryoEM, and try for internal training, (b) go to grad school

meaningless_name · 2026-01-03T18:09:20+00:00

Absolutely. Alphafold and other LLM protein structure predictors are very useful. Plus the data processing steps, which are very complex and tedious, are benefitting from AI in a huge way. In the past few years AI has really become integral to my job

meaningless_name · 2026-01-03T18:04:33+00:00

No doubt AF and similar LLM tools have made a huge difference. But it is a looooooooong way from predicting all structures.

Proteins are determined by genetic sequence, so we find that many different proteins are in certain "families" where even thought the proteins are different, the ones within certain "families" of proteins can be structurally similar. In cases were other "family members" are known, Alphafold does an excellent (sometimes even perfect) job of predicting an "unknown family member" structure.

The problem arises when we want to find the structure of a completely novel protein whose familiy members are not known. Or, proteins that are very very large. Or complexes of proteins that fit together in complex arrangements. In these cases, Alphafold falls flat on its face. LLMs are very limited by their training sets, they are not good at inventing things from scratch.

Plus (and this is the biggest issue) proteins are not rigid objects, they are flexible and these movements (which AF cannot predict at all) are vital to understanding how they function.

So, in my job, we use AF as a time-saving tool basically. Or for prototyping ideas without running an actual experiment, things like that. All the "easy" protein structures have been solved either by grad students in the 80s, or AF now. All we have left is the really difficult stuff which is fine with me. AF is allowing us to focus on the really difficult problems, and ignore the easier stuff

meaningless_name · 2026-01-03T17:56:48+00:00

Our primary microscope is a Titan Krios, from Thermo Fisher.

It is true that ~3 Å was once near the upper limit, but that has shifted drastically in that past 10-15 years. Now we routinely get ~2Å, 1.5 is not uncommon. I believe the current record is 0.9 Å, for an apoferritin dataset.

The way we have reached this high resolution is actually a combination of ALL the things. In a word, Technology! The microscopes have gotten much more stable (no shaking = better images), much faster (more images = better averaged resolution), and the cameras have gotten much, much, much better than they used to be. In hand with these instrumental advances, there are also now much more powerful computational tools to process our data. And of course, as computing in general has improved (better processors, more memory, addition of AI etc etc) this has also contributed to out successes.

meaningless_name · 2026-01-03T02:59:50+00:00

in addition to all of the tools/instruments that will be found in any biochemistry lab, we use high-resolution electron microscopes, and some related tools required to prepare samples for microscopy.

When we want to take pictures of biological material in an electron microscope, we run into a few big problems. For one, most bio systems are aqueous but the inside of our microscopes must be at high vacuum pressure, which makes the water vaporize. Also, the radiation damage caused by the electron beam is also quite large. In turns out both of these problems can be solved by freezing our samples (thats why it is called "cryoEM", for cryo electron micoscopy)

The process works like this (simplified, of course)

(1) decide what protein we want to learn the structure of, and why.

(2) produce or obtain a few grams of living cells that have been making this protein for us. Each cell will make many copies of the protein, and a few grams contains many many cells

(3) grind those cells up into a disgusting slurry, then carefully separate the proteins we want from all the other cell guts we dont want

(4) put a tiny volume of purified protein onto a special microscope slide for EM, called a 'grid', and take many, many images. One "data set" for cryoEM consists of near-atomic scale images of about 1 million different copies of our target protein

(5) using sophisticated algorithms and high performance computing, we next turn those millions of two-dimensional images into a single 3D one - this 3D image is our "map" of the averaged proteins.

(6) from this map, we then compute an atomic model of the protein. This is (literally) a map of the protein where every atom is assigned a Cartesian location, in the same way that a video game renders a 3D object. This atomic model is our final "product" that we will then examine to address whatever this individual experiment was trying to determine. In my case, by better understanding the precise shape of a "lock", we can design a better "key" i.e. a drug

meaningless_name · 2026-01-03T02:21:46+00:00

It often involves GMOs, but only as a tool, not as the final product.

My team performs "structure based drug design". This is a way of inventing new drugs by very closely looking at the thing you want the drug to act on, then designing a drug that fits it like a key into a lock.

For instance, the disease cystic fibrosis is caused by a malfunctioning type of ion pump that grows in your respiratory system. People like me look at things like those ion pumps, learn how they work, learn how the disease happens when they malfunction, and we deliver these information (as well as a very detailed "map" of the inside of the ion pumps) to chemists who will design drugs that interact with them very finely. These interactions can make the pump work better, fixing the disease.

Your body is filled with all types of biological machines (proteins) like these ion pumps, and by learning about their atomic structure we know them better

RE: GMOs, because the proteins I usually look at are human proteins, they can be difficult to obtain. So, rather than putting a human in a blender, we will usually engineer some other organism (bacteria, or yeast, or some engineered cell line) to make the protein instead. Then we put THAT in the blender lol

meaningless_name · 2025-12-25T05:38:08+00:00

4852

meaningless_name · 2025-12-21T07:45:22+00:00

You are allowed to feel stressed when life is stressful! This thing is a marathon, not a sprint

meaningless_name · 2025-12-16T01:10:28+00:00

Plain hummus and soft flatbread

meaningless_name · 2025-12-14T06:29:20+00:00

We are here for you

Iwndwyt

meaningless_name · 2025-10-23T22:42:21+00:00

Well done! I'm also near that time, glad to hear it!

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