EVMP: Enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework

Motivation In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays many machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters. Results In order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k -mer mutations, which are input into BaseEncoder and VarEncoder respectively. In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30%, while the SOTA(state-of-the-art) record was improved by 15.25%. EVMP also provides optional data augmentation based on multiple base promoters, which further improved the model performance by 17.95% compared with non-EVMP SOTA record. In further study, extended vision is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. Availability and implementation The source code is publicly available on github: https://github.com/Tiny-Snow/EVMP . Contact [email protected] Supplementary information Appendix is available at bioRxiv online.