Yong Wang
@yongwangKU
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A computational biophysicist, integrative structural modeller, currently working on membrane transporters using computer simulations.
Hangzhou, CN
Joined October 2016
RT @LindorffLarsen: ⏰ Two weeks left to register for our Benzon Symposium on protein structure prediction and design 🧶🍝🧬🖥️ . Meeting will….
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RT @HaidaiHu: Thrilled to share that our Zorya paper is now published online in Nature! It was an incredible team effort @__jphilipp @Salmo….
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RT @FrankNoeBerlin: Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equili….
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RT @LeoTZ03: Protenix: Protein + X | ByteDance .- A trainable PyTorch reproduction of AlphaFold 3 .GitHub:
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RT @LindorffLarsen: Sören, Giulio & I wrote a short review on the use of machine learning methods to study disordered proteins, with a focu….
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RT @TarakChem: Interested in AI-ML & Enhanced Sampling methods & their applications in Chemistry, Materials, and Biology? Then join our res….
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RT @LindorffLarsen: Do you like CALVADOS, but also proteins with folded domains?. If yes, check out Fan’s paper on the CALVADOS 3 model . S….
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RT @LindorffLarsen: Very nice article by @dmgerhard on . “The Dynamic Lives of Intrinsically Disordered Proteins”. with quotes from @G_T_He….
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RT @JustinLemkulVT: And I have a second one - updated tutorials for.@GMX_TWEET version 2024! These are new and distinct from the other set….
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RT @LindorffLarsen: Our work on computational design of intrinsically disordered proteins is now out in Science Advances. We asked whether….
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RT @Heidarsson_lab: Exciting news! I am grateful and thrilled to join the Section for Biomolecular Sciences #UCPH. We use single-molecule t….
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6/ Lastly, the model was developed on top of the fantastic Chroma model (, and this work was carried out by #JingtianXu, a highly talented undergraduate student in my lab who is now pursuing his PhD at @Tsinghua_Uni. I've learned a great deal from him :).
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RT @rafeequemavoor: 3D illustration workshop for researchers. Registration started for the next batch. Link: Kin….
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RT @CompBioPhys: 🤓Interested in #ionchannels? We have #jobopportunities #PhD students and #Postdocs in our lab @mpi_nat in the #deGroot gro….
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5/ This research opens up new avenues for the application of AI in structural biology, particularly in the study of multi-state membrane proteins. We’re excited about the potential implications for future research!.
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4/ The results? Our model-generated structures resemble the recently solved cryo-EM structures of hSPCA1 ( which were not included in the training dataset, confirming the effectiveness of our diffusion model.
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3/ By integrating shot molecular dynamics simulations with deep learning, we were able to augment our structural dataset and ensure the physical plausibility of our AI-generated protein structures.
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2/ The innovative use of state classifiers and membrane constraints in our model allows precise control over the generation gradient, ensuring that the predicted structures are both state-specific and biologically relevant.
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1/ Understanding protein dynamics is essential for revealing their biological functions. Our study introduces a forward conditional-backward diffusion process, tailored to generate accurate multi-state conformations of P-type ATPases.
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🔍 Curious about how #AI can generate membrane protein structures at specific states? Our recent paper explores a diffusion model to predict the multi-state conformations of #PtypeATPases.
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