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Xing Che on Muck Rack

Xing Che

As seen in: Nature, BioRxiv
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Articles by Xing Che

Leveraging high-throughput proteomics and AI-based protein folding to accelerate VAV1 molecular glue discovery

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By Hanfeng Lin, Xin Yu, Haiyang Zheng, Ran Cheng, Min Zhang, Xiaoli Qi, Yen-Yu Yang, Shengmin Zhou, Rui Qi, Ly Le, Andrea Bortolato, Semen O. Yesylevskyy, Alan Nafiiev, Xing Che, Jin Wang
| Nature Verified
Abstract This study reports a series of CRBN molecular glues that selectively induce proteasomal degradation of VAV1, a hematopoietic-specific signaling protein and key target in hematological malignancies and autoimmune diseases. Using unbiased global proteomics, we identify phenyl-glutarimide derivatives (e.g., NGT-201-12) as effective VAV1 degraders that engage its C-terminal SH3 domain (SH3-2).
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Beyond the G-Loop: CRBN Molecular Glues Potently Target VAV1 via a Novel SH3 RT-Loop Degron

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By Hanfeng Lin, Xin Yu, Haiyang Zheng, Xing Che
| BioRxiv
Abstract This study reports the discovery and characterization of novel CRBN molecular glues that selectively induce the proteasomal degradation of the hematopoietic-specific signaling protein VAV1, a key target in hematological malignancies and autoimmune diseases. Utilizing unbiased global proteomics, we identified phenyl-glutarimide derivatives NGT-201-12, as effective VAV1 degraders, with its C-terminal SH3 domain (SH32) being crucial for this interaction.
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FKSFold: Improving AlphaFold3-Type Predictions of Molecular Glue-Induced Ternary Complexes with Feynman-Kac-Steered Diffusion

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By Jian Shen, Shengmin Zhou, Xing Che, YDS Pharmatech
| BioRxiv
Abstract This paper discloses technical details of an early approach we explored for molecular glue induced ternary complex structure prediction using Feynman-Kac (FK) steering in the diffusion modules of AlphaFold3-type models.
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FragLlama: Next-fragment prediction for molecular design

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By Jian Shen, Shengmin Zhou, YDS Pharmatech, Xing Che
| BioRxiv
Abstract The emergence of ChatGPT has drawn significant attention to Large Language Models (LLMs) due to their impressive performance. While LLMs primarily focus on next token/word prediction, we apply this principle to molecular design by reframing the task as predicting the next token/fragment. We present FragLlama, a large language model trained for molecular design, featuring custom tokens that represent molecular fragments and functional groups.
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