Peptide sequencing-the process of identifying amino acid sequences from mass spectrometry data-is a fundamental task in proteomics. Non-Autoregressive Transformers (NATs) have proven highly effective for this task, outperforming traditional methods. Unlike autoregressive models, which generate tokens sequentially, NATs predict all positions simultaneously, leveraging bidirectional context through unmasked self-attention. However, existing NAT approaches often rely on Connectionist Temporal Classification (CTC) loss, which presents significant optimization challenges due to CTC's complexity and increases the risk of training failures. To address these issues, we propose an improved non-autoregressive peptide sequencing model that incorporates a structured protein sequence curriculum learning strategy. This approach adjusts protein's learning difficulty based on the model's estimated protein generational capabilities through a sampling process, progressively learning peptide generation from simple to complex sequences. Additionally, we introduce a self-refining inference-time module that iteratively enhances predictions using learned NAT token embeddings, improving sequence accuracy at a fine-grained level. Our curriculum learning strategy reduces NAT training failures frequency by more than 90% based on sampled training over various data distributions. Evaluations on nine benchmark species demonstrate that our approach outperforms all previous methods across multiple metrics and species.

翻译：肽段测序——即从质谱数据中鉴定氨基酸序列的过程——是蛋白质组学的一项基础任务。非自回归Transformer（NAT）模型已被证明对此任务极为有效，其性能优于传统方法。与按顺序生成标记的自回归模型不同，NAT模型通过无掩码自注意力机制利用双向上下文信息，同时预测所有位置。然而，现有的NAT方法通常依赖于连接时序分类（CTC）损失函数，该损失函数因其复杂性带来了显著的优化挑战，并增加了训练失败的风险。为解决这些问题，我们提出了一种改进的非自回归肽段测序模型，该模型结合了结构化的蛋白质序列课程学习策略。该方法通过采样过程，基于模型估计的蛋白质生成能力动态调整蛋白质的学习难度，从而逐步从简单到复杂的序列学习肽段生成。此外，我们引入了一个自精炼的推理时模块，该模块利用已学习的NAT标记嵌入迭代优化预测结果，在细粒度层面提升序列准确性。基于不同数据分布的采样训练，我们的课程学习策略将NAT训练失败频率降低了90%以上。在九个基准物种上的评估表明，我们的方法在多项指标和多个物种上均优于所有先前的方法。