Aaron Foote, Ph.D.

From the Bollong lab Cysteine reactive groups are a mainstay in the design of covalent drugs and probe molecules, yet only a handful of electrophiles are routinely used to target this amino acid. Here, we report the development of scalable thiol reactivity (STRP), a method which enables the facile interrogation of large chemical libraries for intrinsic reactivity with cysteine. High throughput screening using STRP identified the azetidinyl oxadiazole as a moiety that selectively reacts with cysteine through a ring opening-based mechanism, capable of covalently engaging cysteine residues broadly across the human proteome. We show the utility of this reactive group with the discovery of an azetidinyl oxadiazole containing a small molecule that augments the catalytic activity of the deubiquitinase UCHL1 in vitro and in cells by covalently modifying a cysteine distal to its enzymatic active site. This study adds a novel cysteine targeting group to the electrophilic lexicon and provides robust methodology to rapidly surveil libraries for reactivity with cysteine.

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Let’s get this straight. It’s Train/Dev/Test set for your data splits And “validation” set if you want to further validate your model with 1 or more sets of unseen data Do you agree? Train/Val/Test and then Validation again is just confusing! Andrew Ng, to my rescue, talks about Train/Dev/Test, in this book: https://lnkd.in/e7_hxTFv Even Wikipedia has a small section about the confusing terminology https://lnkd.in/eqAF7vB7 (From Wikipedia) “However, in both industry and academia, they are sometimes used interchanged, by considering that the internal process is testing different models to improve (test set as a development set) and the final model is the one that needs to be validated before real use with an unseen data (validation set). "The literature on machine learning often reverses the meaning of 'validation' and 'test' sets. This is the most blatant example of the terminological confusion that pervades artificial intelligence research."[16]”

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"The hype will play out in the long term," James Fraser said. "The hard part of drug discovery (is) it's a pretty long business. You can't take the wet lab part out of it yet. Stuff needs to play out in the clinic." Ron Leuty takes an in-depth look at Profluent and how #AI has become a force in drug development (biologics & small molecule). We are seeing this play out in applications to the QB3 mentoring program. It is striking how many new companies are touting that their use of AI/ML provides a competitive advantage. It's almost more remarkable if a drug discovery company is NOT using AI. And it is very hard to judge the longer-term commercial prospects of a new company whose approach relies on AI/ML. Obviously the quality of the data the models are using is paramount. Anyone with thoughts about how to assess these companies, please weigh in! https://lnkd.in/gaVr3CPj

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Percentages are always thrown around to make something more "scientific." I've always wondered if you can infer something about population size from the value - like if a study reports a percentage of 66%, does that imply a larger population size than one that reports 67%? It turns out, you can make predictions. For example, here's the most likely population sizes implied by seeing a percentage of 13%. This arises from how 13 factors with 100, and from the uneven prior distribution of sample sizes reported in literature. This leads to interesting effects, where indeed 66% has a posterior mean population size that is double that of 67% - because 66% can only arise from larger populations than 67%. This assumes there is some bound on population sizes (the effect washes out as numbers become larger). See the complete write-up here - and see which percentages are more "trustworthy" than others. https://lnkd.in/gaB2PBNj

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From the preprint: In this study, we introduce HiBiT-RR and nLucK0, variants devoid of lysine residues, to mitigate such artifacts. Our findings demonstrate that HiBiT-RR maintains similar sensitivity and binding affinity with the original HiBiT. Moreover, the comparison between nLucWT and nLucK0 constructs reveals variations in degradation patterns induced by certain PROTAC molecules, emphasizing the importance of choosing appropriate tagging systems to ensure the reliability of experimental outcomes in studying protein degradation processes.

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A couple of announcements for ISoP Members, 1. Today (August 5th) is the last day for early bird ACoP 2024 conference registration. Please see link below to register! https://lnkd.in/eeYTx_WY 2. ISoP QSP SIG Virtual QSP Week is next week (August 12-15). My own research interests certainly bias me here, but I think this will be the best Virtual QSP Week yet! There are several talks discussing how AI/ML can be leveraged for identifying biomarkers, clustering patient populations to understand treatment response, and the use of generative AI and LLMs in QSP model development and applications. Don't forget to register and add these events to your calendar in advance (instead of trying to find the zoom link day of). The way it works is that you need to click the registration links below for each event, you will get an email, and then you need to click add the event to your calendar for each of the 4 separate webinars. Please see more information on each of webinars below. Agenda and Speakers: Session 1. 12 Aug 2024, Monday, 9 am EDT: Registration Link: https://lnkd.in/er9jeTXM AI-driven exploration of dementia biomarkers Chairs: Suruchi Bakshi, Shaina Short Presenter: Dea Gosishvili, University of Amsterdam Session 2. 13 Aug 2024, Tuesday, 9 am EDT: Registration Link: https://lnkd.in/eaxqPyf5 From molecules to systems: leveraging NEURON for multiscale modeling      Chairs: Suruchi Bakshi, Shaina Short Presenter: Robert McDougal, Yale University   Session 3. 14 Aug 2024, Wednesday 11 am EDT Registration Link: https://lnkd.in/ew2ZqusU Using multi-omics clustering and machine learning to identify key characteristics driving treatment response in SLE virtual patients       Chairs: Prakash Packrisamy, Vincent Hurez Presenter: Renee Myers, Rosa & Co. LLC Application of GenAI in QSP model development       Chairs: Prakash Packrisamy, Vincent Hurez Presenters: Anuraag Saini and Ali Farnoud, Boehringer Ingelheim Session 4. 15 Aug 2024, Thursday11 am EDT Registration Link: https://lnkd.in/ekKGdZht Large Language Model tools for QSP modeling       Chair: Carolyn Cho Presenters: Chris Rackaukas and Emily Nieves, Massachusetts Institute of Technology Link to QSP SIG Website and Virtual QSP Week: https://lnkd.in/e96SdAsh Lastly, if you have not yet checked out the new ISoP Members Community please do so. Trying to build a vibrant environment for the community to engage in conversation is extremely difficult. We are trying to figure out the features of this new application, your help is appreciated throughout this process! Link below: https://lnkd.in/eBZWrscu

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🛠️ New Personal Project Initiation As someone working at the intersection of chemistry and data science, I’ve noticed a significant gap in data visualization resources tailored specifically for chemists. This realization has motivated me to take action. To bridge this gap, I will be summarizing my experiences and insights into a series of articles over the coming months. 🔗 I’m excited to share my first article: Visualization of Mass Spectrometry Deconvoluted Data. Check it out here 👉 https://lnkd.in/gHNHWpHk 📣 A Call to Action for Chemists in My Network: If there are specific data types or visualization techniques you’d like me to cover, please leave a comment! Your feedback will help shape the direction of this project. #DataVisualization #Chemistry #MassSpectrometry

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For most of us in biotech, John Hopfield is a name we might remember from grad school for work on enzymes. His theory of kinetic proofreading, demonstrated how any enzymatic reaction could be made arbitrarily accurate by requiring the consumption of energy. This principle, theoretical at the time, has been found to underlie everything from homologous recombination to antigen discrimination. It also tells us a lot about science and how technology develops. Hopfield was curious as to how biological systems can exhibit powerful, "emergent" properties that do not obviously originate in their component parts. A study of how small collections of neurons might create stable memories led to the first non-trivial neural network. Others seized on this work, often coming from neuroscience, or in the case of Geoff Hinton, experimental psychology. In attempting to understand how the senses, especially vision, could interpret the world around them, Hinton and others developed deep neural networks into a powerful computational tool. A similar story can be told about David Baker, first a biochemist, only later a computational structural biologist. The first simulation and prediction tools for protein structure, Rosetta, came from the need of laboratory scientists to better understand what was happening in their laboratory experiments. Often crude, hard to use, and complex, these sequence to structure tools helped to formalize a scientific question, provided metrics to judge against, and enabled thousands worldwide to learn from each others' experiences. They also immediately opened the doors to de novo design of molecules never before seen in nature. Bringing protein folding out of the world of theory and into that of empirical computational methods, proved to be the key step. Physics-based methods, dependent on complex chemical calculations, did not scale. Black box computational methods, often inspired by biology and physics, but fundamentally incomprehensible, started to gain traction a decade ago. Ultimately, a descendent of Hopfield and Hinton's neural networks won the day --- and in so doing, has changed how we think about proteins forever. While four Nobel Prizes for "AI" may seem distant from the biology they have been used to understand, it is whence they came. We stand as ever on the shoulders of giants. #biotech #ai #nobelprizes #ml #biopharma

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This study examines the binding dynamics of E3 ligases (VHL, CRBN, and cIAP) with proteins of interest, focusing on dynamics, cooperativity, selectivity, linker length, and PROTAC conformations. The influence of interface residues and linker lengths on specific conformations for target proteins and E3 ligases is highlighted. Utilizing molecular dynamics and steered molecular dynamics simulations, the study provides comprehensive parameters on the behavior and stability of diverse ternary complexes. These insights are crucial for designing PROTACs targeting disease-causing proteins and advancing the development of degradable ternary complexes for therapeutic applications.

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Our scientists along with Professor Greg Thurber’s laboratory, including Dr Anna Kopp at the University of Michigan Chemical Engineering have co-written a paper recently published in The AAPS Journal, on the functional design of immune cell targeting therapeutics.   The paper demonstrates the importance of fine-tuning features such as affinity, valency, linker length, in the context of target receptor expression on tumour or immune cells. The results in the paper further highlight the flexibility and modularity of our Humabody VH platform, as well as the importance of the careful consideration given by our expert team to the design of these molecules.   This paper marks the successful culmination of another chapter of our productive collaboration with Professor Greg Thurber and his team.   To read the full paper, visit: https://lnkd.in/ezzwY989   #Biotech #Therapeutics #Discovery

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ISoP Community. As the co-chair of this years communications committee, alongside Eleni Karatza, we have some pretty big updates so please read through this post. First and foremost, ACoP 2024 registration is open. Please see the following link (also found below): https://lnkd.in/eNZ2PAcq Note hotel reservations are open and the cut off date for group rate is October 6th. However, as always, if you plan to attend I would suggest booking sooner than later. You can reserve a room by calling 877-800-4888 or through the Reservation site here: https://lnkd.in/ea95yZQS If you haven't checked out the new ISoP website, it looks great! There is a brand new members only section with various benefits. There is a main home feed which contains content/posts from various "communities". Honestly, it feels/looks like an internal Facebook exclusive for our society (I don't mean this in a negative way). You can update your profile, connect with others, and keep up to date with ISoP events/communications. Here is the link the updated website and members section (you must have an active membership): https://lnkd.in/eDVHwgSZ Right now there is only one main community called "Members Connect Community", which contains all ISoP members and acts as a way to make a broad post to everyone. In the future, we could create ISoP SIG specific groups, alumni groups, local ISoP chapters, and others. Even individual working groups within ISoP (e.g., QSP SIG WGs) could create their own channel. Here is the link to the Member Connect Community channel: https://lnkd.in/eBZWrscu If there are any questions feel free to reach out to me directly or comment below! Happy Father's Day!

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This is fun, a pending Illumina patent (US20240158849A1) that describes a completely new way to do sequencing (surfaced by Nava Whiteford on the ASeq discord). Inventors are Rico Otto and Hayden Black. My interpretation (maybe the inventors will chime in to correct🤪): - Build a chip with pairs of electrodes on it, each pair of electrodes fitted with a circuit [160] that measures very small electrical changes (e.g. current) between the electrodes. - Assemble a long polymer bridge between the two electrodes. - Here's where it gets crazy: coat the bridge in gold 👑 so it's electrically connected at each end to an electrode, and etch the gold off the middle of the bridge (what could do wrong 😳) so there is an exposed detector [113] - Mix up a soup of bases like A [123] that have a conductive tail [133]; each base gets its own version of the tail, and each of the four tails is electrically different e.g. has a different resistance. - Send a polymerase [105] chunking along the DNA strand to be sequenced [150], incorporating bases like [123] to form a complementary strand [140] - Each time a base is incorporated into [140] its conductive tail [133] somehow (😳) flops onto the bridge detector [111] so changes the electrical properties of the bridge which can measured by the detector circuit. Easy peasy, sequencing squeezy! Patents shows some scanning tunneling microscope pictures that may or may not show this working 🤷‍♂️ Why do this? If this works in practice (big if around any sequencing technology) it's pretty cool as it's a single (maybe CMOS) chip with no light source, optical detectors, integration time, nanopore (organic or otherwise). As the investors write, it "is robust, reproducible, sensitive, accurate, works in real time, detects single molecules, and has high throughput." Anyway, nice to see there's some greenfield research going on at Illumina 👏👏👏

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Why do folks in drug discovery talk about Eroom's law and the decrease in NMEs/$ (or per $B to make the numbers sane) and try to compare that to the progress in semiconductors as exemplified by Moore's "law"? The proper comparison would be NMEs/$ to Chip fabs/$, not transistors /IC (or transistors/$ as Moore's law is often misdescribed). I'd say we're actually doing pretty well as an industry. Admittedly we're comparing apples to pineapples here but I don't think the ~30x cost to bring a drug to market over 5 decades is something people should be freaking out about and using to indict the industry for its supposed lack of productivity. Drug development is hard, slow, and expensive, and much of the low hanging fruit has been picked (apples, not pineapples, I guess) but it's still one of the most amazing and important endeavors undertaken by us humans. (Thanks ChatGPT for the handy table - take with a grain of salt)

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Evan Williams is Professor of Chemistry at UC Berkeley give another prospective on the current and future state of play woth charge detection mass spectrometry (CDMS) 🎯 Summary: Quoting the authors on current issues "Another significant issue is the speed of the devices. A lot of the data being published involves long accumulation times – sometimes an hour or two just to get a decent ion count. That’s acceptable when you’re developing a new technique, but in practical applications, it’s not something most users are willing to endure. Our goal is to reduce this time to be more in line with an LC time scale" Authors vvies on advantage of CDMS to the biopharma industry "The advantage of CD-MS for large particles is similar to the advantage of conventional mass spectrometry for smaller molecules (below a MDa): it provides very accurate mass measurements, quickly, with high sensitivity and specificity. This is valuable for characterizing anything involving large particles, whether it’s viral particles, synthetic lipid nanoparticles, or other examples we’ve been working on. For instance, we can determine how much RNA is in lipid nanoparticles, what the average size of the particles is, and how they are affected by processes like freezing" #massspectrometry #massspec #chemistry #biopharmaceutical #pharma #biotherapeutics #mrna

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Does the integrity of your plasmid DNA keep you up at night? If not, maybe it should. A recent Nature article highlights results from a large-scale study on plasmid integrity, and the results are not good. The study examined 1000s of plasmids from labs around the world and uncovered a shocking result – most plasmids have design flaws or undetected sequence errors that are likely contributing to unexpected experimental failures. The study notes several common issues with plasmids: First, poor plasmid design on the part of the experimenter compromises plasmid functionality (like failing to add a stop codon to a gene). Second, the physical sequence of the plasmid does not match the corresponding electronic sequence map. These errors are due the outdated technology that many labs use to produce their plasmids coupled with poor quality control.  With a subset of plasmids subjected to sequencing in the study, the authors found that only 65% of provided plasmids matched their electronic plasmid map (meaning that 35% of plasmids either contained undesired mutations or were actually completely different plasmids than expected). Shockingly, when they examined plasmids used for gene therapy applications, they found that 36% of plasmids had mutations in one of the two ITR regions (sequences that play a critical role in the functionality of viral-based gene therapies), meaning that these plasmids will likely not function as intended. The authors concluded that “In total, we estimate that 45-50% of lab-made plasmids have undetected design and/or sequence errors that could potentially compromise the intended applications.” They further conclude that “The high error rate of lab-made plasmids suggests that many labs lack the sophisticated and nuanced expertise needed to properly design vectors and furthermore, there is insufficient quality control of the plasmids being constructed and propagated in labs.” Serving as CTO at a company developing new tech for plasmid manufacturing, I can tell you these results are not surprising, nor are they only a problem in academic labs. At Novel Biotechnology Inc., we perform extensive quality control on all the plasmids we work with, including plasmids that we receive from clients and outside vendors where undetected mutations are extremely common. In one extreme case, we analyzed a plasmid manufactured by another vendor for one of our clients and found that it was highly contaminated with multiple other plasmids which our client had never worked with – meaning the vendor had sent them a plasmid prep that was a mix of other customers’ plasmids! This not only compromised the experiments our client had planned, but given the highly proprietary nature of plasmid designs, could seriously compromise the IP position of those other companies! This wasn’t a small, unknown vendor either. This is a serious conversation that needs to be had, especially as genetic medicines rise in prominence.

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Electrochemical detection of sequencing at Illumina

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There are now many providers of metadata of scientific papers: details about license, bibtex, retraction status, author list, COI statements. We aggregated five of these into one client with fallbacks in PaperQA2. We use it for adding context to LLM calls, but this could be a great tool for exploring science of reserach papers

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On this day in science heike kamerlingh onnes was the first person to produce liquefied helium. In room E' of his Natuurkundig Laboratorium, opposite Van der Werff Park in the heart of Leiden, at the end of a long and exhausting day, he succeeded in liquefying the last of the ‘permanent' gases. In so doing, he achieved a temperature of a few degrees above absolute zerop, making Leiden the coldest place in the world. Helium has the lowest boiling point of the elements at -269 °C, or 4 Kelvin on the absolute temperature scale. It must be cooled down to between 1 and 4 K to exist as a liquid. When working with frozen mercury in 1911 Kamerlingh Onnes discovered superconductivity, the phenomenon whereby electrical resistance suddenly completely disappears below a certain (extremely low) temperature. This achievement, together with the liquefaction of helium, earned him the Nobel Prize in 1913. #science #scienceandtechnology #pharmacy #chemistry #physics #helium #superconductors #superconductivity #leiden #Holland #Netherlands

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