Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae297.
Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development.
We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test datasets. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs.
The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP.
由于肽的特异性和功效，它们是治疗各种疾病的有前途的药物。然而，基于肽的药物的开发常常受到肽潜在毒性的阻碍，这对其临床应用构成了重大障碍。评估肽毒性的传统实验方法既耗时又昂贵，使开发过程效率低下。因此，迫切需要专门设计的计算工具来准确快速地预测肽毒性，从而有助于识别安全的肽候选物用于药物开发。
我们在这里提供了一种新的计算方法 CAPTP，它利用卷积和自注意力的力量来增强从氨基酸序列预测肽毒性的能力。CAPTP 表现出色，在交叉验证设置和独立测试数据集上的马修斯相关系数（Matthews correlation coefficient）约为 0.82。这一性能超过了现有的最先进的肽毒性预测器。重要的是，CAPTP 即使在处理数据不平衡时也能保持其稳健性和通用性。CAPTP 的进一步分析表明，某些序列模式，特别是在肽的头部和中央区域，对于确定其毒性至关重要。这一见解可以为更安全的肽药物设计提供重要的信息和指导。
CAPTP 的源代码可在 https://github.com/jiaoshihu/CAPTP 上免费获取。
