<br>FCMSTrans：通过在 Transformer 框架内利用多尺度卷积和深度特征组合来准确预测与疾病相关的 nsSNP,Journal of Chemical Information and Modeling

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FCMSTrans：通过在 Transformer 框架内利用多尺度卷积和深度特征组合来准确预测与疾病相关的 nsSNP
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-02-13 , DOI: 10.1021/acs.jcim.3c02025
Ming Zhang ₁ , Chao Gong ₁ , Fang Ge ₂ , Dong-Jun Yu ₃

Affiliation

非同义单核苷酸多态性 (nsSNP) 涉及 6000 多种疾病，需要准确预测才能加快药物发现和改进疾病诊断。在这项研究中，我们提出了 FCMSTrans，一种新颖的 nsSNP 预测器，它创新性地结合了 Transformer 框架和多尺度模块来进行全面的特征提取。 FCMSTrans的独特之处在于深度特征组合策略。该策略合并了进化规模建模 (ESM) 和 ProtTrans (PT) 特征，提供了对蛋白质生化特性以及位置特异性评分矩阵、二级结构、预测相对溶剂可及性和预测无序 (PSPP) 特征的理解，这些特征是源自四种蛋白质序列和结构导向特征。该特征组合提供了涉及 nsSNP 的分子动力学的全面视图。我们的模型采用了 Transformer 跨多个层的自注意力机制，提取更高层次和抽象的表示。同时，通过多尺度卷积捕获不同级别的特征，丰富了多个梯队的特征抽象。我们与现有方法的比较分析强调了 FCMSTrans 采用的集成特征融合方法所带来的重大改进。基于不同数据集（例如 PredictSNP、MMP 和 PMD）的性能评估进一步证实了这一点，曲线下面积 (AUC) 分别为 0.869、0.819 和 0.693。此外，在第三方数据集上进行的盲测中，FCMSTrans 表现出优于当前最佳预测器 PROVEAN 的鲁棒性和优越性，取得了令人印象深刻的 AUC 分数 0.7838。 FCMSTrans 的 Python 代码可在 https://github.com/gc212/FCMSTrans 获取，供学术使用。

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FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer’s self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

更新日期：2024-02-13

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