Rishwanth Raghu
@rishwanth_raghu
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CS PhD student @Princeton. Machine learning for structural biology.
Joined July 2022
We’ll be presenting CryoBoltz ❄️⚡️ at #NeurIPS this week! Check out the new experiments in our updated paper. We’re also releasing our code for the community to try out on their data 🚀 Paper: https://t.co/rPMtaPLUR7 Code:
github.com
CryoBoltz code for protein structure prediction with cryo-EM guidance. NeurIPS 2025. - ml-struct-bio/cryoboltz
Excited to present CryoBoltz ❄️⚡, a multiscale guidance approach for steering AlphaFold3/Boltz-1 to sample structures that are consistent with experimental cryo-EM density maps. 🧵1/7 https://t.co/rPMtaPMsGF Joint work with @axlevy0 @GordonWetzstein & @ZhongingAlong!
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Rishwanth Raghu kicks off our BioE Lunch and Learn 🍕 series for the semester! Rish is a graduate student in @ZhongingAlong��s lab and presenting “Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data”
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For a detailed description of our approach and analysis, check out our preprint! Predicting conformational dynamics is a key challenge for structural biology. CryoBoltz is our attempt to unlock the full potential of structure prediction models using experimental data. 🚀 7/7
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We also demonstrate our method on real cryo-EM data of P-glycoprotein. CryoBoltz models 4 conformational states of this transporter given experimental maps, whose resolutions are too low (> 4Å) for model building algorithms. 6/7
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We demonstrate CryoBoltz on antibodies, whose CDR loops are challenging targets for structure prediction models due to a lack of evolutionary signal. We show that guidance with a synthetic map enables accurate modeling of the highly variable CDR H3 loop. 5/7
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CryoBoltz can sample both the outward and inward state of the STP10 transporter given a corresponding synthetic density map, whereas unguided Boltz-1 and AlphaFold3 only sample the outward or inward state, respectively. 4/7
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To fit both the low-resolution shape and high-resolution details of a cryo-EM map, we propose a guidance mechanism that combines global, point cloud-based constraints with local, density-based constraints, enabling sampling of conformations supported by the experimental data. 3/7
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Protein structure prediction models can now map sequence to structure with high accuracy, but are often biased towards a single low-energy conformational state. Can we guide them to sample multiple functionally relevant states using cryo-EM measurements? 2/7
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Excited to present CryoBoltz ❄️⚡, a multiscale guidance approach for steering AlphaFold3/Boltz-1 to sample structures that are consistent with experimental cryo-EM density maps. 🧵1/7 https://t.co/rPMtaPMsGF Joint work with @axlevy0 @GordonWetzstein & @ZhongingAlong!
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🚨New paper at #NeurIPS2024! We present Hydra, a mixture of neural fields 🐉🐉🐉 for ab initio cryo-EM reconstruction of complex mixtures. Hydra is our attempt at pushing the boundaries for even more extreme forms of heterogeneity in cryo-EM 🤯. This was joint work with an
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