ESMSec:使用蛋白质语言模型和注意力模型预测人类体液中分泌的蛋白质
Yan Wang1, Huiting Sun1, Nan Sheng1
1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
International journal of molecular sciences
|June 27, 2024
概括
使用ESMSec深度学习框架预测人体液中分泌的蛋白质显示出高准确度. 这种方法增强了疾病生物标志物发现和早期诊断的潜力.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 人体液体中分泌的蛋白质作为疾病诊断和风险预测的关键生物标志物.
- 将自然语言处理 (NLP) 变压器模型集成到蛋白质组学中,导致了用于蛋白质序列表示的先进蛋白质语言模型 (PLM).
研究的目的:
- 引入ESMSec,这是一个新的深度学习框架,用于预测人体液中分泌的三种类型的蛋白质.
- 利用ESM2模型和注意力架构进行增强的蛋白质序列特征提取和分类.
主要方法:
- 使用ESM2模型将蛋白质序列编码到1000 × 480的特征矩阵中.
- 采用多头注意力机制与完全连接的神经网络用于分泌蛋白质的二元分类.
- 验证血,脑脊液 (CSF) 和精液数据集的框架.
主要成果:
- 在ESMSec中,平均准确度很高:血为0.8486,脑脊髓为0.8358,精液为0.8325.
- 该模型在预测分泌的蛋白质方面超过了现有的最先进的 (SOTA) 方法.
- 证明了ESM (进化规模建模) 在提高预测性能方面的有效性.
结论:
- 根据ESMSec框架,选人体液中分泌的蛋白质具有很大的潜力.
- 这一进步可以通过生物标志物识别帮助早期检测和评估各种疾病的风险.
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