物理对齐深度学习实现等离子体纳米腔中动态单分子DNA低聚物的SERS解析与测序

中文摘要 AI 辅助

单分子表面增强拉曼光谱（SM-SERS）以超高灵敏度捕捉动态分子行为，但其生物聚合物分析受限于强光谱异质性、瞬热点采样及背景干扰。本文开发了一种物理对齐深度学习框架，整合对比注意力多实例学习（CAMIL）、三通道多核CNN分类器及轨迹级转换引导序列重构，以解码等离子体纳米腔中单分子DNA低聚物的动态行为。本研究中，CAMIL从对比正负DNA轨迹袋中挖掘富含链嵌入核苷酸及二核苷酸特征的有效光谱，生成单分子DNA片段光谱态库；该库用于训练12类CNN，将查询时间分辨DNA SM-SERS帧分配至高置信度DNA片段态，实现对状态组成、驻留长度、熵、切换频率及转换矩阵的轨迹级分析。转换矩阵进一步转化为状态转换边证据用于候选序列评分，可从受限随机采样动力学中推断非对称DNA序列。该框架将SM-SERS异质性转化为定量分析信息，推进了动态单分子解码研究。

英文摘要

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) captures dynamic molecular behavior with ultrahigh sensitivity, but its biopolymer analysis is hindered by strong spectral heterogeneity, transient hotspot sampling, and background interference. Here, we develop a physics-aligned deep learning framework integrating contrastive attention-based multiple-instance learning (CAMIL), a tri-channel multi-kernel CNN classifier, and trajectory-level transition-guided sequence reconstruction to decode single-molecule DNA oligomer dynamics in a plasmonic nanocavity. In this work, CAMIL mines informative spectra enriched with chain-embedded nucleotide and dinucleotide signatures from contrastive positive and negative DNA trajectory bags, generating a single-molecule DNA-segment spectral-states library. This library trains a 12-class CNN to assign query time-resolved DNA SM-SERS frames to high-confidence DNA-segment states, enabling trajectory-level analysis of state composition, dwell length, entropy, switching frequency, and transition matrix. The transition matrix is further converted into state-transition-edge evidence for candidate sequence scoring, enabling asymmetric DNA sequences inference from confined stochastic sampling dynamics. This framework transforms SM-SERS heterogeneity into quantitative analytical information, advancing dynamic single-molecule decoding.

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28 pages, 4 figures, 1 table