Advanced Biotech Research Techniques

I’ve worked on AI my whole life because I’ve always believed it could unlock the ability to answer some of the biggest and most intractable problems in science. Our first big science breakthrough happened five years ago when we announced our solution to the protein structure prediction problem: AlphaFold 2. It has been incredible to see its impact since then. More than 3 million researchers across 190 countries have used this tool for disease understanding, drug discovery and more. And it was an honour of a lifetime for our work to be recognised last year with a Nobel Prize. One of our greatest ambitions is for AI to aid in accelerating drug design and help cure all diseases. This is what led me to found Isomorphic Labs, which is already making amazing progress. We’ve also expanded AlphaFold to predict the interactions of all of life’s molecules. But AlphaFold represents more than a solution to a biological puzzle. It demonstrated how AI can crack ‘root node’ problems - where a single breakthrough unlocks entire new avenues of research. It is a critical step towards a long-held dream of mine: building a virtual cell. Imagine running ‘in silico’ experiments orders of magnitude faster than in a wet lab. Scientists could rapidly test hypotheses, model complex pathways and see how a drug affects a cell. It would be an incredible boon not only for fundamental biology but also for medicine. Although for me, AlphaFold was never just about biology. It was the first major proof point for a much larger thesis: that AI could be the ultimate tool for advancing science. By processing data or helping us come up with new hypotheses, I think AI will help us tackle some of humanity’s greatest challenges and answer fundamental questions about the universe. From materials design to fusion energy to mathematics, I believe we’re on the cusp of a new golden age of discovery. We’re just getting started. Read more about AlphaFold’s impact: https://lnkd.in/eNeqxqQp

Today in @ScienceMagazine, together with my former colleagues at the Novartis Institutes for BioMedical Research, we report the discovery and characterization of first molecular glue degraders of the WIZ transcription factor (TF) for fetal hemoglobin derepression and therapeutic consideration in Sickle Cell Disease. Chemical Biologists may appreciate the CRBN-directed glue degrader library, here used in phenotypic screening of erythropoietic progenitors for HbF induction w/o effect on viability or differentiation. WIZ was discovered by proteomic study of hits and validated by CRISPR. Globin Biologists will enjoy the discovery of WIZ as a repressor of fetal hemoglobin (HbF). This biology has been intently studied, and here we find WIZ loss derepresses HbF by a local decrease in repressive H3K9me2, attributable to the association of WIZ and EHMT1/2. Medicinal chemists will appreciate the potent and selective activity of dWIZ-1 and dWIZ-2, the excellent drug-like properties, oral bioavailability and importantly the excellent tolerability in rodents and monkeys. For Biochemists, we show recruitment to CRBN as dependent on WIZ zinc finger 7, and biophysically characterize association by surface plasmon resonance. The CRBN:dWIZ: WIZ ternary complex buries 413 sq-A of surface area. Drug hunters like me might reflect on molecules that potently target a transcription factor with no pockets – effectively no tertiary structure predicted by @AlphaFold. This chemistry targets instead targets the ZnF secondary structure – truly marking the conceptual end of “undruggable” (if still in doubt). Hematologists will enjoy pharmacologic target validation in multiple pre-clinical models, a relatively selective impact on gene expression, the wide open therapeutic index, and the ease of end-game medicinal chemistry to produce investigational agents. Most importantly, for patients with Sickle Cell Disease. The advance of CRISPR-edited stem cells for transplantation, from our group and others, is a major advance. But patients need more accessible, safe, oral medicines especially in Sub-Saharan Africa. We welcome your feedback on this study, and hope dWIZ-1 and dWIZ-2 immediately prove valuable tools to the community and an inspiration for targeting disordered proteins. Finally, I thank all former colleagues in the NIBR TPD Initiative, and in particular this program’s true champion – Dr. Pamela Ting. Pam, working with you on this brave idea and on the collaborative assembly of the manuscript this last year are treasures I will always cherish, like our friendship. Article: https://lnkd.in/es6k87up Perspective: https://lnkd.in/ewn4eZNT

Our team just published our latest work in Nature revealing how patients' own antibodies can make or break their response to checkpoint immunotherapy. The Question: Why do some cancer patients experience dramatic tumor shrinkage when they received immunotherapy while others see no benefit? Our Approach: Using REAP (Rapid Extracellular Antigen Profiling), we screened blood samples from 374 cancer patients for autoantibodies against 6,000+ proteins. Key Findings: · Cancer patients have an extraordinarily diverse “autoantibody reactome.” We detected ~3,000 unique autoantibody reactivities and clearly had not achieved saturation. · Patients with anti-interferon antibodies were up to 40x more likely to respond to treatment. This is a complete reversal from COVID-19 where these same antibodies increase mortality by 20-200 fold. · Novel finding: Anti-TL1A antibodies enhance treatment by preventing T cell apoptosis in the TME · Red flag: 10% of non-responders had antibodies against BMP receptors, revealing a previously unknown barrier to treatment success Conclusions: Treatment-modifying autoantibodies act as a roadmap for developing better therapies. We can now design drugs that mimic beneficial antibodies or counteract harmful ones, potentially improving outcomes for any patient who receives immunotherapy. This work was only possible through incredible collaboration between the Fred Hutchinson Cancer Center, the Yale Cancer Center, and my company Seranova Bio. Special recognition to lead author Yile Dai and the entire team who made this vision a reality. Read the full paper here: https://lnkd.in/dRxYd4bC

Researchers at Johns Hopkins University have created a revolutionary protein “switch” that tricks cancer cells into manufacturing their own chemotherapy drugs, causing them to self-destruct while sparing healthy cells. Instead of delivering drugs directly to cancer cells, this method uses a harmless “prodrug” that only becomes activated inside cancer cells when the switch detects specific cancer markers. The switch is made by combining two proteins: one that senses cancer markers and another from yeast that converts the inactive prodrug into a potent cancer-killing drug. When the switch detects cancer, it activates the drug inside that cell, turning the cancer cell into a drug factory that destroys itself. To work, the switch must enter cancer cells either by delivering the protein itself or by inserting the gene that makes the protein, allowing the cancer cell’s own machinery to produce the switch. Afterward, patients receive the inactive chemotherapy prodrug, which becomes activated only inside cancer cells. This new approach focuses on producing the drug inside cancer cells rather than just delivering it to them, which could kill more cancer cells while reducing harmful side effects on healthy tissue. Lab tests on human colon and breast cancer cells have shown promise, and animal testing is expected to start within a year. While still early, this technique offers a radically different way to attack cancer. #PNAS #RMScienceTechInvest

Published today in Nature, Camilo Faust Akl et al. (Keith Ligon, Nino Chiocca, Francisco Javier Quintana) reveal that GBM co-opts TRAIL+ astrocytes to suppress anti-tumor immunity. The team identify a distinct subset of TRAIL+ astrocytes within the GBM tumor microenvironment that induces apoptosis in CD4⁺ and CD8⁺ T cells via a GBM-secreted IL-11 -> STAT3 signaling axis. These astrocytes also modulate microglia- and monocyte-derived TAMs, further amplifying T cell dysfunction. Notably, high TRAIL and IL-11 expression correlated with faster recurrence and worse survival in GBM patients, underscoring the clinical relevance of this pathway. Crucially, this immunosuppressive circuit can be therapeutically disrupted using an oHSV engineered to express an anti-TRAIL scFv—providing a compelling proof-of-concept for precision immunovirotherapy in GBM. Mass General Brigham, Harvard Medical School, Baylor College of Medicine, Boston University School of Medicine, Dana-Farber Cancer Institute, Broad Institute of MIT and Harvard, The University of Freiburg, McGill University

I’m often asked where I see AI make a tangible, real impact in the world today. To that, I answer with #AlphaFold, the revolutionary AI model from Google DeepMind, that is able to predict the structure of a protein simply from its amino acid sequence. 5 years ago, AlphaFold solved the 50-year grand challenge of protein folding, followed by the equally meaningful decision to make 200 million protein structures freely available to the scientific community. Since then, Demis Hassabis and John Jumper have been recognized with a Nobel Prize for their work on AlphaFold, and we see over 3.3 million users of it globally, with more than a third of users right here in Asia-Pacific. Here is just a snapshot of those applications: 🔬 Dr. Su Datt Lam at the National University of Malaysia (UKM) is learning more about Melioidosis to better fight the silent killer. 🧬 Researchers Lim Jackwee lim and Yinxia Chao at Singapore’s A*STAR - Agency for Science, Technology and Research and National Neuroscience Institute (NNI) are visualizing proteins linked to Parkinson’s. 🔍 Professor Ji-Joon Song’s team at the Korea Advanced Institute of Science and Technology lead to cancer and other diseases. 🪢Dr. Danny Hsu at Academia Sinica, Taiwan is advancing our understanding of exceptionally complex protein “knots”. ♨️ Dr. Syun-ichi Urayama’s team is uncovering new evolutionary insights from microbes in Japan’s hot springs! Listen to one of their stories below, and read more about all of them here: https://lnkd.in/d7wyACpK #GoogleDeepMind #AIforGood 

✂️ Editing HIV Out of DNA 🌀 A single CRISPR injection may be able to cut HIV-like viruses out of the genome — permanently. Researchers at Temple University report that a gene-editing therapy successfully removed SIV (an HIV-like virus) from infected cells in primates, reaching viral reservoirs throughout the body with no detectable off-target effects. Instead of suppressing HIV with lifelong drugs, this approach aims to excise the virus directly from host DNA, potentially creating a functional cure. Delivered using an AAV9 viral vector, the therapy edited viral DNA across major reservoirs including lymph nodes and spleen after just one injection. ➡️ Viral DNA editing detected in key tissues ➡️ Broad distribution throughout the body ➡️ No toxicity or off-target edits observed ➡️ Single-dose treatment approach This preclinical work helped enable the first FDA-authorized clinical trial of a CRISPR therapy designed to treat HIV in humans. If successful, gene editing could shift HIV treatment from lifelong management to permanent removal. Authors: Kamel Khalili et al.

The 2024 Nobel Prize in Chemistry tells a remarkable story of how AI, once relegated to solving games, has ventured into the molecular secrets of life itself. This is the story of AlphaFold, an AI model that answered a question that scientists had pursued for decades: How do proteins fold? The answer was critical because a protein’s 3D shape determines its function, dictating how it behaves in the body—whether as an enzyme driving chemical reactions, an antibody fighting disease, or a hormone signaling cellular responses. Yet, despite the question’s fundamental importance, accurately predicting protein structures had eluded scientists for half a century. The origins of AlphaFold trace back to Demis Hassabis, co-founder of DeepMind. A chess prodigy and video game designer turned AI researcher, Hassabis was not new to grand challenges. In 2016, DeepMind’s AlphaGo program had defeated the world champion in Go, a game so complex it was once thought unconquerable by machines. But Hassabis viewed games as more than just trophies—they were a training ground, teaching AI to interpret patterns and make decisions. With AlphaGo’s victory, Hassabis and his team set their sights on something more ambitious: using AI to unravel real-world biological mysteries. And so, in 2018, DeepMind entered the CASP (Critical Assessment of Protein Structure Prediction) competition, the ‘Olympics’ of protein folding, where it took home a promising but imperfect win, with around 60% accuracy. Enter John Jumper, a young physicist with a fascination for the cosmos and a knack for protein simulation. Jumper joined DeepMind and saw a path forward using neural networks called transformers, which could process massive data sets to detect the subtleties in amino acid sequences. Under his guidance, the team refined AlphaFold, training it on databases of all known protein structures. In 2020, they unveiled AlphaFold2, achieving an unprecedented level of accuracy, matching the results of traditional, lab-based methods like X-ray crystallography. When CASP judges saw AlphaFold2’s results, they declared that the 50-year-old “protein-folding problem” had finally been solved. AlphaFold’s success was seismic. For the first time, scientists could predict the structures of nearly all known proteins, opening new doors in fields from medicine to environmental science. Researchers began using AlphaFold2 to explore antibiotic resistance mechanisms, design enzymes capable of breaking down plastics, and even develop new treatments for diseases. David Baker, also awarded the Nobel for his contributions to computational protein design, had been tackling these challenges from a different angle, using his Rosetta software not just to predict but to design entirely new proteins with custom functions. Through his methods, Baker’s team has created novel molecules, many of which hold promise in therapeutic development and environmental applications. The future is unimaginable in the most exciting way.

🧪 Exploring an Alternative Pathway in Targeted Protein Degradation: ByeTACs In a recent study by Loy et al. (2025), a new class of bifunctional molecules called ByeTACs (Bypassing E3 ligase Targeting Chimeras) was introduced. Unlike traditional PROTACs, which rely on E3 ligases and ubiquitination to tag proteins for degradation, ByeTACs offer a different strategy. Instead of tagging proteins for destruction, ByeTACs directly recruit the protein of interest to the 26S proteasome by binding the Rpn-13 subunit, a nonessential ubiquitin receptor. This allows for degradation independent of the ubiquitination cascade — a potentially useful option when ligase expression is limited or when ubiquitination is inefficient. 🧬 Highlights: ✅ Demonstrated degradation of engineered (HaloTag-GSK3β) and endogenous proteins (BRD4, BTK) ✅ Effective in multiple cell types at low μM to nM concentrations ✅ Degradation is proteasome-dependent but ubiquitin-independent, confirmed by E1 inhibition (TAK-243) and Rpn-13 knockdown While not a replacement for existing degrader technologies, ByeTACs offer a complementary approach with potential to expand the scope of degradable targets. 📄 Read the paper: J. Med. Chem. 2025, https://lnkd.in/gRjutRtr #TargetedProteinDegradation #ByeTACs #TPD #PROTAC #DrugDiscovery #Biotech #Rpn13 #MedicinalChemistry