Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators

Vivian B. Brandenburg, Franz Narberhaus, Axel Mosig

Abstract: Author summary Intrinsic transcriptional terminators are essential regulators in determining the 3’ end of transcripts in bacteria. The underlying mechanism involves RNA secondary structure, where nucleotides fold into a specific hairpin motif. Identifying terminator sequences in bacterial genomes has conventionally been approached with well-established energy models for structural motifs. However, the folding mechanism of transcription terminators is understood only partially, limiting the success of energy-model based identification. Neural networks have been proposed to overcome these limitations. However, their adoption for predicting and identifying RNA secondary structure has been a double edged sword: Neural networks promise to learn features that are not represented by the energy models, while they are black boxes that lack explicit modeling assumptions and may fail to account for features that are well understandable based on decades-old energy models. Here, we introduce a pre-training approach for neural networks that uses energy-model based inverse folding of structural motifs. As we demonstrate, this approach “brings back the energy model” to identify transcriptional terminators and overcomes the limitations of previous energy-model based predictions. Our approach works for diverse types of neural networks, and is suitable for the identification of structural motifs of many other RNA molecules beyond transcriptional terminators.