EMOCPD：利用氨基酸微环境的高效注意力计算蛋白质设计模型
EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment
AI总结：
针对传统及现有深度学习蛋白质设计方法的缺陷，提出基于氨基酸微环境的高效注意力模型EMOCPD，通过多头注意力和逆残差结构提升设计准确率，在测试集上表现优于现有方法，且更适配低负向氨基酸含量的蛋白质设计。
摘要：
计算蛋白质设计（CPD）指利用计算方法设计蛋白质。传统方法依赖能量函数和启发式算法进行序列设计，效率低下，无法满足生物分子大数据时代的需求，其准确性受限于能量函数和搜索算法。现有深度学习方法受网络学习能力的制约，无法从稀疏的蛋白质结构中提取有效信息，这限制了蛋白质设计的准确性。为解决这些不足，我们开发了利用氨基酸微环境的高效注意力计算蛋白质设计模型（EMOCPD）。该模型旨在通过分析氨基酸周围的三维原子环境，预测蛋白质中每个氨基酸的类别，并基于预测的高概率潜在氨基酸类别优化蛋白质。EMOCPD采用多头注意力机制聚焦稀疏蛋白质微环境中的重要特征，并利用逆残差结构优化网络架构。所提出的EMOCPD在训练集上达到80%以上的准确率，在两个独立测试集上分别达到68.33%和62.32%的准确率，比最优对比方法高出10%以上。在蛋白质设计中，EMOCPD预测的突变体与野生型相比，热稳定性和蛋白质表达量均有显著提升，有效验证了EMOCPD在设计更优蛋白质方面的潜力。此外，EMOCPD的预测会根据20种氨基酸的含量产生正向、负向或极小的影响，据此可将氨基酸分为正向、负向或中性三类。研究结果表明，EMOCPD更适合设计负向氨基酸含量较低的蛋白质。
英文摘要：
Computational protein design (CPD) refers to the use of computational methods to design proteins. Traditional methods relying on energy functions and heuristic algorithms for sequence design are inefficient and do not meet the demands of the big data era in biomolecules, with their accuracy limited by the energy functions and search algorithms. Existing deep learning methods are constrained by the learning capabilities of the networks, failing to extract effective information from sparse protein structures, which limits the accuracy of protein design. To address these shortcomings, we developed an Efficient attention-based Models for Computational Protein Design using amino acid microenvironment (EMOCPD). It aims to predict the category of each amino acid in a protein by analyzing the three-dimensional atomic environment surrounding the amino acids, and optimize the protein based on the predicted high-probability potential amino acid categories. EMOCPD employs a multi-head attention mechanism to focus on important features in the sparse protein microenvironment and utilizes an inverse residual structure to optimize the network architecture. The proposed EMOCPD achieves over 80% accuracy on the training set and 68.33% and 62.32% accuracy on two independent test sets, respectively, surpassing the best comparative methods by over 10%. In protein design, the thermal stability and protein expression of the predicted mutants from EMOCPD show significant improvements compared to the wild type, effectively validating EMOCPD's potential in designing superior proteins. Furthermore, the predictions of EMOCPD are influenced positively, negatively, or have minimal impact based on the content of the 20 amino acids, categorizing amino acids as positive, negative, or neutral. Research findings indicate that EMOCPD is more suitable for designing proteins with lower contents of negative amino acids.
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