fabienplisson.bsky.social
BioDesign, Machine Learning, Drug Discovery | Rosenkranz Award 2021 | Dad | Polyglot | Capybarist | plissonf.github.io
Founding ingeniebio.com
ORCID 0000-0003-224
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Derguini, F.; Plisson, F.; Massiot, G. 𝘗𝘳𝘦𝘱𝘢𝘳𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘵𝘢𝘨𝘪𝘵𝘪𝘯𝘪𝘯 𝘊 𝘢𝘯𝘥 𝘍 𝘥𝘦𝘳𝘪𝘷𝘢𝘵𝘪𝘷𝘦𝘴 𝘢𝘴 𝘢𝘯𝘵𝘪-𝘤𝘢𝘯𝘤𝘦𝘳 𝘢𝘨𝘦𝘯𝘵𝘴. FR 2941697 A1 20100806 2010.
Europe PMC europepmc.org/article/PAT/...
Google Patents
patents.google.com/patent/FR294...
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As an engineer at heart, I wondered how we would scale up if we found a non-toxic, bioactive analogue. That led us to 𝗯𝗶𝗼𝗰𝗮𝘁𝗮𝗹𝘆𝘀𝗶𝘀 (think immobilised enzymes) - screening lipases and esterases to reach, in 1 step, a key intermediate: Taginitol C. This work was patented.
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Over three years, we synthesised dozens of Taginitin C analogues, eventually streamlining the synthesis from 10 to 6 steps 𝘷𝘪𝘢 judicious protection/deprotection strategies.
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The molecule was notoriously sensitive to acids, bases, nucleophiles, and UV light. Resources were limited, and we were performing multi-step syntheses on just 𝟱 𝗺𝗴 𝘀𝗰𝗮𝗹𝗲𝘀, in 𝘁𝗶𝗻𝘆 𝗿𝗼𝘂𝗻𝗱-𝗯𝗼𝘁𝘁𝗼𝗺 𝗳𝗹𝗮𝘀𝗸𝘀, working in darkened fume hoods.
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Together with various teammates (MSc students) we worked on the hit-lead optimisation of Tagitinin C - a toxic germanocrolide inhibiting the ubiquitin-proteasome pathway.
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Eighteen years ago, I joined one of my first drug development R&D programs at the 𝘐𝘯𝘴𝘵𝘪𝘵𝘶𝘵 𝘥𝘦𝘴 𝘚𝘤𝘪𝘦𝘯𝘤𝘦𝘴 𝘦𝘵 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘪𝘦𝘴 𝘥𝘶 𝘔𝘦𝘥𝘪𝘤𝘢𝘮𝘦𝘯𝘵 𝘥𝘦 𝘛𝘰𝘶𝘭𝘰𝘶𝘴𝘦 - a public-private partnership between the @cnrs.fr and Pierre Fabre Laboratories.
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Beilun Wang and team recently reported the development of antimicrobial peptides combining an ensemble of DL predictors with EvoGradient, modifying candidate sequences strategically from evolutionary information, improving their antimicrobial activity and selectivity.
www.nature.com/articles/s41...
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We previously used that strategy to set guidelines for discovering and designing non-hemolytic peptides:
www.nature.com/articles/s41...
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The process mimics the hypothesis-driven process of a medicinal chemist, especially with Deep Mutational Scanning. It also merits explaining the results from your predictive model(s) by comparing mutants.
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Another strategy starts from sequences identified with the best predictive scores and applies a generative algorithm to create analogues (via random mutagenesis or directed evolution), followed by predictions.
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A common strategy for identifying sequences with the desired characteristics is to combine a generative algorithm with multiple models predicting properties and functions. This narrows the search in the vast peptide fitness space.
www.sciencedirect.com/science/arti...
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Generative models have created peptide sequences that share antimicrobial characteristics (e.g., amphipathic character, charged residues, helical constraint).
link.springer.com/protocol/10....
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These models are routinely used to discover novel AMPs from biodiverse extant and extinct species. Check out the great work done by @delafuentelab.bsky.social team with their APEX models.
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If you want to leverage AI+ML tools to engineer your peptides, proteins or enzymes but don't know how.
Visit ingeniebio.com and let's talk.
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This cool study by Adrian Bunzel and co-workers puts great effort into capitalising on tools #RosettaDesign, #ESMFold, and #ProteinMPNN with evolutionary information to develop AI.zymes.
www.biorxiv.org/content/10.1...
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You add these guys as well @delafuentelab.bsky.social @noeliaferruz.bsky.social @cjjackson.bsky.social
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Hi Kevin, please add me to the next BioML pack. Thanks.
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If you want to support my voyage, you can still do so here: www.chuffed.org/project/help...
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No, I haven't. I'll have a look. Thanks.
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#TheBigBlue
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I just noticed you were in NY. A bit far. But yes, I have been freediving and spearfishing actively from 11yo to 35yo.
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Yep, I still have my gear, although I have not used it for a while. Happy to go for some fun.
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At @IngenieBio, we can help you and your team leverage the latest emerging #ProteinDesign tools or custom #AI models to meet your needs. Explore more at#IngenieBio (3/3)
ingeniebio.com
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It might be surprising, but not all drug discovery programs target proteins. Applying #MachineLearning models to phenotypic screenings for function, activity or property can add value to your #peptidedesign and #proteinengineering campaigns. (2/3
doi.org/10.1038/d41586…
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These challenges require tailored #AI solutions. I founded @IngenieBio to help you integrate #ArtificialIntelligence algorithms suited to your needs and accelerate your peptide research programs. Visit3/3)
ingeniebio.com