The sequence in the 5′ untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce “GRU-rewiring,” a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5′ UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs.

已知 5' 非翻译区 (UTR) 中的序列会影响 mRNA 翻译速率。然而，潜在的监管语法仍然难以捉摸。在这里，我们提出了 MTtrans，一种多任务翻译率预测器，能够从各种实验技术的数据集中学习常见的序列模式。核心前提是共同的主题更有可能真正参与翻译控制。MTtrans 在准确性和捕获跨物种可转移基序的能力方面均优于现有方法，突显了其在识别进化保守序列基序方面的优势。我们独立的荧光激活细胞分选结合深度测序 (FACS-seq) 实验验证了 MTtrans 识别的大多数基序的影响。此外，我们还引入了“GRU 重新布线”，这是一种解释循环单元隐藏状态的技术。门控循环单元 (GRU) 重新布线使我们能够识别调控元件富集的位置并检查 5' UTR 突变的局部影响。MTtrans 是破译翻译调控基序的强大工具。