bioRxivMolecular BiologyPreprint
Drug design and repurposing with a sequence-to-drug paradigm
Lifan CheniD, Zisheng Fan, Jie ChangiD, Ruirui YangiD, Hao GuoiD, Yinghui ZhangiD, Tianbiao Yang, Chenmao Zhou, Zhengyang Chen, Chen Zheng, Xinyue HaoiD, Keke ZhangiD, Rongrong Cui, Yiluan Ding, Naixia ZhangiD, Xiaomin LuoiD, Hualiang JiangiD, Sulin ZhangiD, Mingyue ZhengiD
Abstract
Drug development based on target proteins has been a successful approach in recent decades. A conventional structure-based drug design pipeline is a complex, human-engineered pipeline with multiple independently optimized steps. Advances in end-to-end differentiable learning suggest the potential benefits of similarly reformulating drug design. Here, we proposed a new sequence-to-drug paradigm that discovers drug-like small-molecule modulators directly from protein sequences and validated this concept for the first time in three stages. First, we designed TransformerCPI2.0 as a core tool for the sequence-to-drug paradigm, which exhibited competitive performance with conventional structure-based drug design approaches. Second, we validated the binding knowledge that TransformerCPI2.0 has learned. Third, we applied a sequence-to-drug paradigm to discover new hits for E3 ubiquitin-protein ligases: speckle-type POZ protein (SPOP), ring finger protein 130 (RNF130) which does not have a 3D structure, and repurposed proton pump inhibitors (PPIs) for ADP-ribosylation factor 1 (ARF1). This first proof of concept shows that the sequence-to-drug paradigm is a promising direction for drug development.
- assay: protein sequence
- research focus: drug response / drug discovery
- research focus: sequence-to-function modeling
- research focus: benchmarking
- approach: deep learning
- approach: transformer / foundation model
- Citations
- 1
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