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

Abstract

IntroductionIn 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, constructing mutation-based synthetic promoter libraries that span multiple orders of magnitude of promoter strength is receiving increasing attention. A number of machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters.MethodsIn 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. EVMP also provides optional data augmentation, which generates multiple copies of the data by selecting different base promoters for the same synthetic promoter.ResultsIn Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30% (MAE), while the SOTA(state-of-the-art) record was improved by 15.25% (MAE) and 4.03% (R2). Data augmentation based on multiple base promoters further improved the model performance by 17.95% (MAE) and 7.25% (R2) compared with non-EVMP SOTA record.DiscussionIn further study, extended vision (or k-mer) 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. The source code is publicly available on GitHub: https://github.com/Tiny-Snow/EVMP.

Evolution is All You Need in Promoter Design and Optimization

“…For example, when applied to predict the strength of trc promoters, our model achieved an R-squared (R2) value of 0.77, signifying a substantial improvement over Zhao et al's method (0.53) [29] and six EVMP-based algorithms (0.65 for the best one) [30]. This underscores the considerable superiority of our model structure.…”

“…Their method scored an R2 of 0.53. We also evaluated six EVMP-based algorithms developed in [30] and obtained R2 values ranging from 0.62 to 0.65. This demonstrates that our model achieved an overall improvement of 18% compared to the previously best- showcasing that our model could utilize general information from Task1 to make more specific predictions such as Task2.…”

Plant Synthetic Promoters

“…The further growth of libraries above 10 8 elements requires the application of synthetic DNA fragments not related to known DNA genome sequences [46]. However, the analysis of such large libraries usually employs ML or DL to identify relations between DNA sequence properties and promoter activity [46,50,[241][242][243]251,262,263]. To improve these ML and DL approaches, they are trained on synthetic, random DNA fragments to test a larger sequence space; models trained on such synthetic data can predict genomic activity better than those solely trained on genome DNA [46,251].…”

“…The EVMP uses better mutation information through the equivalent transformation of synthetic promoters into base promoters which are input into BaseEncoder and corresponding k-mer mutations, which are entered into VarEncoder. Therefore, the EVMP can significantly improve the prediction accuracy of promoter strength [243]. Advanced ML algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and DNA structural properties.…”

Plant Synthetic Promoters

“…The EVMP uses be er mutation information through the equivalent transformation of synthetic promoters into base promoters and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder, respectively. Therefore, the EVMP can significantly improve the prediction accuracy of promoter strength [243].…”