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Posts by Avner Schlessinger

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Two Harmful Variants Can Restore Protein Function Researchers found that paired pathogenic variants can restore protein function rather than make it worse. This phenomenon could occur in four percent of human genes.

Great The Scientist piece: two harmful mutations can restore protein function—challenging how we view genetic risk. I share my take on the structural logic behind this and relevance to drug discovery.

My perspective www.the-scientist.com/two-harmful-...

Original study www.pnas.org/doi/10.1073/...

2 months ago 1 1 0 0

Impressive!

3 months ago 1 0 0 0

Congrats!

4 months ago 0 0 0 0
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Expanding the utility of variant effect predictions with phenotype-specific models - Nature Communications V2P predicts variant pathogenicity conditioned on disease phenotypes across top-level Human Phenotype Ontology categories. This approach shows promise for phenotype-specific estimation of variant effe...

Check out our new paper introducing V2P — a method that predicts both variant pathogenicity and disease phenotype across 23 HPO categories. With @itanlab.bsky.social, David Stein, and many other great collaborators
www.nature.com/articles/s41...
www.v2p.ai

4 months ago 7 1 0 0

Congrats, @itanlab.bsky.social and David! So happy to see it published

4 months ago 3 0 0 0

Very comprehensive work on the modulation of mu opioid receptor

6 months ago 1 0 0 0
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How AI is taking over every step of drug discovery Academic scientists and pharmaceutical companies alike are embracing artificial intelligence, even as questions linger about its value

Check out this great @cenmag.bsky.social piece by @aayushipratap.bsky.social on how AI is transforming drug discovery.

Our work at Mt. Sinai’s AI Small Molecule Drug Discovery Center is featured among others driving this fast-moving field.

🔗 tinyurl.com/sabpvrtp

#AI #DrugDiscovery

6 months ago 3 0 0 0
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Structural insights into brain thyroid hormone transport via MCT8 and OATP1C1 Structures of the solute carrier transporters MCT8 and OATP1C1 reveal mechanisms of thyroid hormone transport into the brain and identify a conserved extracellular allosteric site in OATP1C1.

Interesting read: Structural insights into brain thyroid hormone transport via MCT8 and OATP1C1 www.cell.com/cell/abstrac...

6 months ago 6 0 0 0
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Undruggable No More: AI Hits Disordered Proteins, Unlocks Therapy Targets David Baker’s lab has successfully designed binders to disordered proteins, expanding therapeutic access to over 50% of the human proteome.

David Baker's Nobel Prize-winning lab has now designed binders to "undruggable" disordered proteins, unlocking therapeutic access to over 50% of the human proteome!

‪@uwproteindesign.bsky.social‬

Read more at GEN:
tinyurl.com/4fw6wvkm

9 months ago 3 1 0 0
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Democratizing Artificial Intelligence in Pre-Clinical Drug Discovery While AI-driven approaches tout increased speed and lower costs, commercial interests compromise scientific collaboration.

Excited to see our new AI Small Molecule Drug Discovery Center #MountSinai featured in GEN!
🧬🧠 Read more: genengnews.com/topics/artif... Thank you @faylinphd.bsky.social
#AI #DrugDiscovery #Genetics

9 months ago 5 1 0 0
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Proposed pH-dependent mechanism of the mitochondrial pyruvate carrier. In the outward-open state, positively charged K49 and H86 bind pyruvate, initiating conformational changes to the inward-open state. The high matrix pH deprotonates H86, allowing pyruvate to leave.
www.science.org/doi/10.1126/...

1 year ago 11 5 0 0
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Structural basis for selective inhibition of human GABA transporter GAT3 The astrocytic γ-aminobutyric acid (GABA) transporter, GAT3, is essential for terminating GABAergic signalling in the central nervous system. Selective inhibition of GAT3 offers a potential strategy f...

We are excited to present the first #cryoEM structures of human GABA transporter 3 – GAT3 – bound to an in-house selective inhibitor, SR-THAP, to substrate GABA, and in the substrate-free state!
@biorxivpreprint.bsky.social
www.biorxiv.org/content/10.1...

1 year ago 20 3 3 1
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Are you aiming for leadership roles in pharmaceutical, biotech, high tech, or medical device companies? Consider applying to the revamped MSBS program @GradSchoolSinai @IcahnMountSinai, featuring 1 or 2-year tracks and non-thesis options tailored for industry professionals

1 year ago 2 2 0 0
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Safeguarding the future of biomedical science in the United States NIH’s abrupt decision to cap indirect cost reimbursement at 15% threatens the critical infrastructure supporting groundbreaking biomedical research in the United States. This policy jeopardizes Americ...

Safeguarding the future of biomedical science in the United States: Cell www.cell.com/cell/fulltex...

1 year ago 1 0 0 0
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At @GradSchoolSinai @IcahnMountSinai, we are excited to train the next generation of #datascience and #AI #professionals for #PrecisionMedicine @MountSinaiNYC
and beyond. Apply here: icahn.mssm.edu/education/ad...

1 year ago 1 1 0 0
Overview of the computational framework employed in this study to capture generalizable dynamics in kinases and how perturbations such as ligand binding/mutations modulate conformational dynamics. The framework requires a protein sequence as input and uses the MSA subsampling approach to generate initial structural guesses (AF ensemble) using AlphaFold. These structures were then used t seed to launch independent MD simulations and finally combined together using the Markov state model (MSM) to capture conformational dynamics in kinases. In some cases, this standard workflow may not fully capture the breadth of conformational heterogeneity. To address such scenarios, we used an adaptiv sampling framework which starts with launching short MD simulations (10 ns) from an AF generated conformational ensemble. The high-dimensional data from these simulations are used to train a time-lagged autoencoder (TAE), which identifies slowly varying structural features. Clustering in the TAE’ latent space yields a “physics-refined” ensemble, and representative cluster centers are selected as starting points for further MD simulations. This expanded ensemble is then integrated into the MSM, providing a more comprehensive view of kinase conformational heterogeneity in both apo and holo states. Capturing these kinase dynamics facilitates the study of conformational allostery and reveals druggable cryptic pockets in protein–protein complexes, potentially accelerating the development of targeted therapies.

Overview of the computational framework employed in this study to capture generalizable dynamics in kinases and how perturbations such as ligand binding/mutations modulate conformational dynamics. The framework requires a protein sequence as input and uses the MSA subsampling approach to generate initial structural guesses (AF ensemble) using AlphaFold. These structures were then used t seed to launch independent MD simulations and finally combined together using the Markov state model (MSM) to capture conformational dynamics in kinases. In some cases, this standard workflow may not fully capture the breadth of conformational heterogeneity. To address such scenarios, we used an adaptiv sampling framework which starts with launching short MD simulations (10 ns) from an AF generated conformational ensemble. The high-dimensional data from these simulations are used to train a time-lagged autoencoder (TAE), which identifies slowly varying structural features. Clustering in the TAE’ latent space yields a “physics-refined” ensemble, and representative cluster centers are selected as starting points for further MD simulations. This expanded ensemble is then integrated into the MSM, providing a more comprehensive view of kinase conformational heterogeneity in both apo and holo states. Capturing these kinase dynamics facilitates the study of conformational allostery and reveals druggable cryptic pockets in protein–protein complexes, potentially accelerating the development of targeted therapies.

Changes in the equilibrium dynamics of kinases can be observed as mutations and ligands are introduced. Authors describe & apply a workflow that combines AF2+MSA subsampling, molecular dynamics simulations, and an autoencoder for dimensionality reduction
www.biorxiv.org/content/10.1...

1 year ago 22 4 0 0
Bioscience Funding Confusion Threatens U.S. Innovation ‘With the current turmoil, China could surpass the U.S. in the near future,’ Nobel Prize winner David Baker warns.

Another outstanding story of the real-world impact of the short-sighted cuts to NIH and other federal support for the best scientific research infrastructure in the world.

www.wsj.com/articles/bio...

1 year ago 3 2 0 0
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🚀 #AlphaFold Database update

AlphaFold DB now integrates The Encyclopedia of Domains (TED) – a resource designed to systematically identify & classify structural domains within AlphaFold-predicted protein structures.

www.ebi.ac.uk/about/news/u...

@pdbeurope.bsky.social

1 year ago 118 44 1 2
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Francis Collins, the NIH Director for 12 years, led the Human Genome Project and other NIH efforts for 32 years, resigned today. Key words from his resignation letter
www.nytimes.com/2025/03/01/u...

1 year ago 3133 1385 61 94

This is a fantastic meeting for the Cryo-EM community in the NYC area. This year it will take place on the East Coast of Manhattan. Please come and join us!

1 year ago 4 6 0 0
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Opinion | The Pharmaceutical Industry Heads Into Elon Musk’s Wood Chipper (Gift Article) Who needs N.I.H. grants? A lot of red-state universities, for one.

This is an excellent article that accurately describes the situation with NIH funding. Great work @zey.bsky.social !

www.nytimes.com/2025/02/11/o...

1 year ago 11 2 0 1
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Good morning snowy NYC

1 year ago 6 0 0 1
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Quantitation and Error Measurements in Dose–Response Curves

If you work in drug discovery and you read one thing this weekend, make it this exceptional J Med Chem editorial. Hits on almost every blessed thing I’ve tweetorialized over the last several years.

Quantitation and Error Measurements in Dose–Response Curves

1 year ago 88 19 4 0
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Want to become a professor? Here’s how hiring criteria differ by country Huge analysis identifies regional variations in the criteria that institutions use to move researchers up the ranks.

Want to become a professor? A huge analysis looks at how criteria assessing researchers for promotion vary widely around the world, and finds some surprises 🧪 www.nature.com/articles/d41...

1 year ago 19 3 0 1
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