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Cross-reactivity00:42

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Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...

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多模式深度学习用于生成潜在的抗登革热.

Huynh Anh Duy1,2, Tarapong Srisongkram3

  • 1Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.

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这项研究引入了一个新的计算框架,通过集成先进的预测和生成模型来发现强效的抗登革热 (ADPs). 这种方法成功地确定了新的ADP候选者,推动了登革热病毒的治疗开发.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药物发现 药物发现
  • 病毒学 病毒学

背景情况:

  • 登革热病毒对全球健康构成重大威胁,需要有效的抗病毒疗法.
  • 关于抗登革热 (ADP) 的有限数据阻碍了计算药物发现工作.
  • 对于针对登革热病毒的新型治疗性来说,有极大需求.

研究的目的:

  • 开发一种多式计算框架,用于预测和识别新型强效抗登革热 (ADP).
  • 为ADP发现利用高性能预测建模和生成学习.
  • 创建一个公开可访问的工具,以帮助开发登革热病毒疗法.

主要方法:

  • 使用双向长期短期记忆 (BiLSTM) 和具有多种序列表示的神经网络堆叠组合的多式组合构建了一个预测模型.
  • 采用Wasserstein生成对抗网络,使用梯度惩罚来生成新的ADP候选人.
  • 利用随机森林回归分析来预测候选的抑制功效 (IC50).

主要成果:

  • 预测模型实现了高性能,均衡准确度,AUC-ROC和AUC-PR超过90%,MCC超过80%.
  • 确定了甘氨酸 (G),氨酸 (F) 和氨酸 (W) 作为影响ADP抑制功能的关键残留物.
  • 发现了33个具有高预测概率的新型ADP序列,并确定了三种具有预测IC50值低于10μM的候选,向登革热病毒包膜蛋白.

结论:

  • 多式模式框架有效地模拟了ADP活动,增强了基于的抗病毒药物的发现管道.
  • 这项研究提供了对登革热病毒有前途的治疗候选者.
  • 一个公开的网络服务器 (https://antidengue-peptide-predictor.streamlit.app) 便于实际应用和进一步研究.