Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design

Ma, M.; Bu, W.; Liu, G.; Liu, Y.; Liu, S.; Zhao, Z.; Yao, S.; Hua, Q.; Zhang, Y.; Zhong, C.; Huang, H.; Deng, P.; Jin, P.; Yin, Q.; Cao, C.; Liu, H.; Xu, M.; He, Y.; Qin, T.; Chen, Z.

doi:10.64898/2026.07.10.737719

bioRxiv · 10.64898/2026.07.10.737719

Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design

Ma, M.·Bu, W.·Liu, G.·Liu, Y.·Liu, S.·Zhao, Z.·Yao, S.·Hua, Q.·Zhang, Y.·Zhong, C.·Huang, H.·Deng, P.·Jin, P.·Yin, Q.·Cao, C.·Liu, H.·Xu, M.·He, Y.·Qin, T.·Chen, Z.

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Abstract

Designing synthetic cis-regulatory elements (CREs) with cell-type-specific activity remains challenging, and optimization is usually treated as a black box, obscuring how regulatory grammar emerges and why design trajectories fail. Here, we present GO-CRE (Guided Optimization of Cis-Regulatory Elements), which combines efficient sequence generation, predictor-guided reinforcement learning, and trajectory-level interpretation. GO-CRE reconstructs iterative sequence changes in a shared sequence-grammar landscape and identifies coordinated update programs corresponding to search, commitment, and optimization. Productive trajectories in HepG2 and K562 progressively acquired cell-type-associated grammar, whereas SK-N-SH trajectories remained confined to local basins. In HepG2, trajectory analysis revealed a low-complexity polyG trap; introducing a polyG penalty redirected optimization toward HNF/FOXA-associated features. Final designs retained sequence diversity while converging on cell-type-associated motif patterns. Lentiviral MPRA validated cell-type-specific activity in K562 and HepG2 and showed higher average activity of HepG2 designs than endogenous CREs. Together, these findings establish sequence-grammar trajectory reconstruction as a basis for interpretable and tunable synthetic CRE design.

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Ma, M., Bu, W., Liu, G., Liu, Y., Liu, S., Zhao, Z., Yao, S., Hua, Q., Zhang, Y., Zhong, C., Huang, H., Deng, P., Jin, P., Yin, Q., Cao, C., Liu, H., Xu, M., He, Y., Qin, T., Chen, Z.. 2026-07-16. Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design. https://doi.org/10.64898/2026.07.10.737719

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