YouSaid · the spoken record

Lauren Richardson

lines on the record: 34
first: 2020-04-26
most recent: 2020-04-26
sittings or episodes: 1
sources: podcast

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“Experiments that take longer and that require mouse work or that require kind of more complex experimental design. Seeing these two papers side by side really speaks to what can be done, what can we learn that's important and valuable in the shortest amount of time. Thanks, Judy, for discussing these articles with me. And that's it for Journal Club this week. You can find all episodes at a16z.com. Thanks for listening.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that what we have here is a demonstration of the types of science that you can get data on really quickly. In the past, to get a structure of this quality, you would have had to do x-ray crystallography, which requires first getting the protein to crystallize in this very specific manner, and it's a tricky thing to do. But with cryoEm, you just get the protein sample super, super cold and shoot a beam of electrons off it. Then determine the structure based off how the electrons bounced off. There has been a real revolution in cryoEM recently, making this method capable of determining the structure of very large protein complexes. Also, the results of both of these papers are in vitro. So we're getting really important information that can help vaccine design and therapeutics design. These are also the fastest type of research that can be done on these. As the publication cycles continue and the research continues, we'll get different insights coming out with”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“They're opposite results, but they're not necessarily mutually exclusive results because RAPIDO is only looking at three antibodies. You know, there's an absolutely enormous number of possible antibodies against these viruses and even just the spike protein. So the fact that three didn't work, you know, that could just be scientific bad luck. There's also the fact that we read other papers to prepare for this segment that supported both the rap conclusion that there is no cross-reactivity and the Walls conclusion that there is cross-reactivity. So the jury is still very much out. And the fact that all these studies are using slightly different designs, different sources of antibodies, different kinds of antibodies, different readouts, this is still definitely evolving and it's part of what makes the research right now so interesting is that so many groups are applying their expertise, their”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Also, a big difference in what they measure using the monoclonal antibodies in rap, they're measuring just binding. So do the monoclonal antibodies bind to the spike protein at all. Whereas in walls, what they're measuring is whether the polyclonal antibody set prevents the virus from entering into human cells. This is more of a phenotypic. It's more of like a does the entire mechanism that you're trying to stop not happen versus just the binding alone, which is a piece of the process. In this case, I'd say the two papers show opposite results. Walls shows that you can stop viral entry using these polyclinal antibodies. And RAP shows that there's no binding of the monoclonal antibodies from 2002 SARS to the 2019 SARS virus.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Protein from the virus. So the mice's immune system saw this as protein. It generated antibodies in response. The researchers then took a blood sample, purified the sera. That sera contains all sorts of different antibodies that bind to all different regions of the spike protein. RAP et al used three monoclonal antibodies which had already been previously characterized as binding to just a subdomain within the 2002 SARS spike protein. So whereas in the walls of all paper there's all sorts of antibodies in this mix that combined all over the spike. These are three highly specific antibodies that bind to three highly specific sites in just one tiny portion.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“You get to see the evolution of the field and the thinking kind of at the moment it's happening. And of course that really bolsters the findings that do concord, like finding that ACE2 is the receptor and that the spike protein is really similar since those groups got those same answers without influencing each other, that really is kind of the gold standard of replication in scientific publishing is having two independent groups finding things in parallel. Okay, let's talk about the next area where we saw differences between these two articles. So this was in the section where they looked at whether antibodies against 2002 SARS combined to 2019 SARS. There were key differences between these two papers and the types of antibodies that they were looking at. In the walls at all paper, they are looking at polyclonal antibodies which were generated by injecting mice with the purified S.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and I think it's hard to say they're using different techniques, and this science is happening so live. It's actually really cool to think about this from the perspective of being at the bench. You're working on something. And if a paper comes out that contradicts what you're finding while you're still working on it, that affects your work because it changes how you trust your own work and it changes what kind of data you're going to be looking for. But in this case, this is all happening so quickly that they're finding what they're finding and they're publishing it. And I think it's really cool to see it side by side.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“So, using different methods, the walls paper found that the 2019 SARS virus and the 2002 SARS virus bind to ACE2 with roughly the same affinity, but then RAP et al. showed that the 2019 SARS virus binds with 10 to 20 times higher affinity, which is a huge difference. They conjecture that this tighter binding is part of what makes it so virulent because it's able to more successfully get into cells following that binding event.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, the downside of that would be that since ACE2 is involved in extremely critical human physiology, that it might be almost impossible to drug without having some kind of adverse side effect. Sets of papers look at the strength of the interaction between 2019 SARS and 2002 SARS with the ACE2 receptors. What did they find here?”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“ACE2 is a really common protein found on the surface of many different cell types in the human body. It stands for Agiotensin converting enzyme. And this is a receptor that is involved in regulating blood pressure. So it has nothing to do with viral entry, and it definitely didn't evolve for that. But many of these viruses have found ACE2 to be a sort of convenient receptor that they can co-op to be able to get into the cell. And I think one of the reasons for this is that the protease activity that the virus is using, this sort of cut and pull in mechanism that we talked about before, that's part of the natural mechanism of how ACE2 functions. What's important here is that this receptor is the exact same one that was discovered to be involved in the 2002 SARS entry mechanism. And that's important because if we have a good understanding of how to drug the ACE2 receptor following on the 2000”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Think that's so interesting. And one of the facts mentioned in the discussion of both papers was that a similar fear and cleavage site is seen in an analogous protein in highly virulent, avian, and human influenza strains. Let's talk more about how the virus binds the host cell and what happens next. Tell me about this ACE2 molecule.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Steps. The first is the virus arriving at Once it arrives at the lungs, Proteins that are on the surface of the cell called proteases will reach over and cleave a piece of this spike protein. And that helps to pull the virus in closer to the cell to the point where a fusion can occur between the membranes. And so at that point, the virus is inside the cell and it can replicate. What has been found in the SARS 2019 virus versus the 2002 virus is that there's an additional cleavage site, an additional set of amino acids in the protein that are cleaved and that's called a furine site and that is not present in the 2002 virus. So there's some hypotheses out there that this furine cleavage occurs first and it actually enhances the probability and sort of the success of the second cleavage and then subsequent viral entry. So there's some theories that the reason this virus is actually more effective at infecting cells than the 2002 SARS virus is because of the sphereing cleavage site.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“So speaking of the immune system, one of the really interesting aspects of these viruses is that the spike proteins are covered in different sugars. Like molecules, and those are actually sometimes called a glycan shield, which is glycin for sugar. And then shield because they actually prevent the immune system from recognizing the viral proteins as easily as they otherwise could. Both of these papers showed that Lot of glycans on the spike protein of the 2019 SARS virus. That they actually”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Of these articles are comparing and contrasting the spike proteins on the current coronavirus with the 2002 SARS virus. That's really important to know the structure of the spike protein because that's the part of the virus that is actually going to bind to the host cell. It's really important to know where it binds on the host cell, what protein it's using, which has been discovered to be ACE2. That's the same protein that the 2002 SARS virus used to infect human cells. So once we know the structure of this spike protein, we can start to understand how it's actually entering cells and have some hypotheses for what types of drugs or what particular molecules could be used to disrupt that interaction and actually treat this disease or prevent the ability of the virus to initiate an infection into new cells.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“And they're both closely related, and each of these articles that we discuss compare SARS-COV, which we informally refer to as 2002 SARS, with SARS-COV2, which we refer to as 2019 SARS. Finally, one last bit of important context to know. The two papers also have similar experimental designs, where they investigate how the spite protein binds to the host cell and whether antibodies that recognize 2002 SARS spike protein will recognize 2019 SARS spike protein. This is important because it helps guide vaccine and drug design. Since the results of the two papers are not identical, we discuss why this might be and what we can learn. But first, we start with the similarities.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“We refer to this article throughout as Walls at All or the Walls Paper. The second article is CryoEM structure of the 2019 NCOV spike in the prefusion confirmation by David Rapp, Neon Chuang Wang, Jason McClellan, and colleagues published in Science. We refer to this article as rap et all. Note that these two articles use different names for the novel coronavirus, SARS, COV2 versus 2019 NCOV, which was the original name for the novel coronavirus, but it has now been settled officially as SARS COV2. And this is a bit confusing because the virus that caused the pandemic, SARS, which is an acronym for severe acute respiratory syndrome, primarily in Asia in 2002-2004, is called SARS-COV. They are both coronaviruses.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Thanks Andy and Vijay for joining me for this first segment. So to quickly wrap up on a high level, there are two takeaways from this article. First, it uncovers new candidates for future development as antibiotics, most critically Hallison, which was rigorously validated. Second, it demonstrates the ability of deep neural networks to make accurate predictions for drug lead identification in a mechanism agnostic manner. This is a broadly useful approach with huge potential to shape the future of drug discovery and development and further highlights the increasingly important role of AI and medicine. If you enjoyed this conversation, check out our podcast featuring senior author Jim Collins called All About Synthetic Biology. In this next segment, A16Z BioDEL partner Judy Savitskaya and I discuss two articles on the novel coronavirus causing the COVID-19 pandemic. The first article is structure, function, and antigenicity of the SARS-COV-2 spike glass.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Whole bunch of molecules in some multitask like framework where one model is predicting all of it, you can learn from all of it and that you develop, even though you might not have a lot of data in any single project or any single area here, the sum of all this data now is huge and helps to regularize your predictions to make them less overfit and more robust such that the sort of the predictive capability that emerges from all of that is better than what anyone would be in between. I think that really is the big, big future is taking the fact that there is this breadth of what it can do and not just recognizing all those possibilities, but using all those possibilities to actually improve any one of those predictions.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“It's broad topic, but that's the fun thing about it. There's a reason why it's a broad topic because there's a broad range of things it can do. I mean, you could talk about identifying targets and there's a lot of work to do there. And especially novel targets, it's a really interesting time to go after novel targets. You could talk about identifying leads. And this is a lot of basically what's been done here, the identification of leads and then the testing of them. These compounds from zinc are leads, but presumably they're not drug-like, so they have to be optimized. So there's these types of methods helping in lead optimization. And then along the way, hopefully you'd want to also be screening for talks. And so there's a ton of methods that are getting really surprisingly accurate. And basically, the beautiful thing about a machine learning approach like this is that the approach for the most part is pretty agnostic to what you're predicting and that the sort of processes you're building up can be useful. One last thing, and this is maybe the Holy Grail dream, is that if you're predicting a lot of properties for a lot of different systems with a”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I mean, I think one of the things that really stands out here is that full stack of experiments that they've done where it goes all the way from looking at the MIC in a dish to going through mice. And one of the appealing things about studying antibiotics and this often even pertains antivirals that the animal models are pretty good with something like Alzheimer's on the far extreme where animals are generally not very good. And so it's appealing that one could do all this with probably not requiring a huge budget and therefore get something on the other side that looks kind of intriguing.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, the fact that they found the antibiotic, they showed it worked in vitro, they showed it worked in vivo, and then they also did some experiments to get at its mechanism of action, suggesting that Hallicin selectively disrupts the pH potential across the bacterial membrane. This saps the proton moto force, which is like the battery of the cell. So like all antibiotics, it's disrupting an essential cellular function, but this appears to be a distinct and new function that's being targeted. It's like super elegant work, such a complete, well-rounded story.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Without adapt, certainly a really potent inhibitor of E. coli and further investigation showed that Hallison has strong growth inhibitory effects on a wide phylogenetic spectrum of pathogens. They tried it on CDIF, ABAMANI, and which is one of the highest priority pathogens that is urgently required in terms of antibiotics. And then more interestingly, it was even able to eradicate E. coli processor cells that remained. It also checks the box of something that is really structurally divergent from conventional antibiotics. And so certainly a very powerful new class of antibiotics that could potentially be strong candidate for further development.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, so deep learning helps us figure out what we don't know versus focusing only on what we already know or what we think we know. What makes Hallison an attractive candidate for further research and development? What are some of the properties that they discovered?”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“And so similarly, you know, what's interesting is that you can feed any representation of a molecule into a computer, but which parts are the interesting ones, you can have just like old school computer vision, you could have a human being say, ah, these are the important ones. But the beauty of DNN approach, which is used here, but also in many precursor works, that the DNN helps understand what are the key aspects and what are the interesting ones. And that is really, I think, the big difference between what you can get in modern deep learning with machine learning versus classical machine learning with like random forest or something like that.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, you know, what you're describing is still a fingerprint, right? It's a one-dimensional vector to describe molecules. I think. Perhaps what's different is that deep learning approach, you can try to infer what the right descriptor should be. That's the hallmark of all of the deep learning approaches for drug design is that, and deep learning in general, that recall, like even when we're just talking about convolutional neural nets for image recognition, the idea is that CNNs for image recognition versus classical computational vision is that in the classical approach the person sort of defines what the right features are.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Preset information about the chemical structures of the molecules. It actually really built new representations, they were called. For years, a lot of people represented molecules with these fingerprint vectors, reflected things like presence or absence of functional groups or descriptors and computable molecular properties. But relying on known fingerprints didn't really work that well. And that's why a lot of the old antibiotic screening process gives you a lot of the same classes of molecules over and over again. And what they did here, they actually have these fingerprint descriptors that were built from scratch.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, sure. They took this machine learning model that they made and they trained it on about 2,500 molecules and used that to train binary classification models to predict probability of weather a new compound would inhibit the growth of E. coli or not. And then turn to the drug repurposing hub library, a library of 6,000 compounds that are already in human clinical development for a wide variety of indications. And at this point, they compared several different machine learning models and after narrowing down those molecules and actually predicting toxicity using different neural networks, they came up with this particular molecule in Halisin. And then thirdly, lastly in the process, they went on to apply this machine learning model after iteration and optimization to a much broader set, the Zinc 15 data set, with over a billion and a half structures. On the machine learning side, what's key here is that deep learning network that they use didn't really rely on any”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“There's a long tail of YouTubers who actually can come up with interesting content. This could be academic labs, startups maybe funded through philanthropy, maybe governments. There may be now the beginnings of the potential for long tail of new drugs coming out that go after indications that will not be blockbusters, but that will still have huge fundamental impact on humanity. There are a lot of interesting different models. We could talk about here for how technology like this coupled with a new business approach and now are possible because of techniques like this could make a huge change in our ability to develop novel antibiotics.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, and then given resistance, I think the nightmare scenario that we're all worried about is that we don't have drugs that work as the last lines of defense go and we don't have any mechanism for creating new ones. The business side is very critical here because if there can't be a way to be rewarded for making drugs, it's just hard to put hundreds of millions of dollars into doing it. It's almost like the business of making Blockbuster movies that you need to have a drug that will make enough money on the other side to be able to support all the effort that goes into it, all the R&D effort, as well as all the effort running clinical trials. Whereas in this case, once you can actually bring down the cost at least to get something out of preclinical quickly, now you have actually the opportunity for one to go after indications that aren't blockbusters that are small ones, kind of almost like the shift where you have certain things that are still going to be done with like Marvel and movies and so on.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“In most indications, if you create a drug and it works better than other drugs, that immediately becomes your top choice when you're prescribing. But for antibiotics, they actually get put to the end of the line because they want to save them for when all the other drugs that are already gaining resistance fail completely.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Makes a challenge is not only the scientific side, but then also the business side. Not only are these antibiotics complex to develop, but the most innovative new products also cannot be sold freely. They are put on the shelf in reserve for more serious cases, and they're actually dubbed these drugs of last resort. And so all of these scientific and business headwinds actually make something synthesizing a brand new antibiotic really challenging to do.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“A huge takeaway from this article was the breadth of experimental work that was done to demonstrate the accuracy of the predictions involved. And so while there has been a lot of work about using neural nets for predicting drug compounds, this was probably one of the landmark examples to date of a predictive perspective approach validated experimentally for something non-trivial in terms of function. Typically doing something in vitro is pretty straightforward, but going to in vivo models was an important step forward to convince especially drug hunters and experts in the field that this has real validity. So I think putting all those pieces together I think is what really made this a paper stand up.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Has excellent antibiotic properties, both in vitro and in two different mouse models of bacterial infection. Excitingly, Hallison has a distinct structure and appears to have a distinct mechanism of action from other antibiotics, which is important given the problem of antibiotic resistance and the need to find new drugs. Our discussion of the paper covers the business of antibiotics, the methods, and how deep learning can identify novel drug structures, and other applications for deep learning in drug discovery and development. But we begin with what made this paper appeal to us, and the first voice you'll hear is VJs.”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source
“Hello and welcome to the inaugural episode of the A16Z Journal Club. I'm Lauren Richardson, one of our bio editors, and in this episode we'll cover two topics. First, a novel machine learning-based approach to identify new antibiotics, and second, we'll discuss two articles characterizing the novel coronavirus causing the current pandemic. Journal Club will cover a variety of articles every few weeks, so stay tuned here and we'll announce its own feed soon. First up is my conversation with A16Z general partner, Vijay Pande, and deal partner on the bio team, Andy Tran. We dive into a deep learning approach to antibiotic discovery by Jonathan Stokes, Regina Barsley, James Collins, and colleagues. In this article, published in Cell, the authors create a novel machine learning-based method to identify new antibiotic drugs from two large databases. They then validated one of their candidates, a drug named Hallison, showing that it”
2020-04-26 · a16z Podcast · Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us · IDENTIFIED FROM THE TRANSCRIPT · source