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

In metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. Promoter traits including excessive sequence length and restricted vocabulary size, are considered to impede the effect of natural language models on tasks involving genetic sequence. We proposed EVMP (Extended Vision Mutant Priority framework), which enhances various machine learning models. The synthetic promoter input to EVMP is split into base promoter and k-mer mutations, which are encoded by BaseEncoder and VarEncoder respectively. We used EVMP on various machine learning models for promoter strength prediction in Trc synthetic promoter library. The MAE (mean absolute error) of LSTM was reduced from 0.50 to 0.18, the MAE of the Transformer was reduced from 0.27 to 0.177, and the MAE of other models was also slightly reduced by EVMP. By virtual dataset expansion based on multiple different base promoters, EVMP utilizes ensemble learning and conclusively lowers MAE. Further investigation demonstrated that EVMP can improve performance of machine learning models by lowering the over-smoothing phenomenon caused by the sequence similarity of synthetic promoters. Additionally, only 56% of the original amount of synthetic promoter dataset is needed to obtain an identical previous result after applying EVMP, which reduces the cost of creating synthetic promoter libraries. Our experiments demonstrated that EVMP is versatile and robust for building synthetic sequence machine learning models. The source code is available at https://github.com/Tiny-Snow/EVMP.