Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Jiaqi Wei, Zijie Qiu, Sheng Xu, Nanqing Dong
| Summary:
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.
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