Int&in:基于机器学习的Web服务器,用于在intins中进行主动分站点识别
Mirko Schmitz1,2,3, Jara Ballestin Ballestin1,2,4, Junsheng Liang1,2
1BIOSS and CIBSS Research Signalling Centers, University of Freiburg, Freiburg, Germany.
概括
整蛋白是执行蛋白质拼接的蛋白质. 一个新的机器学习模型预测了整体中活跃和不活跃的分裂站点,有助于合成生物学应用中分割整体的工程.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 整因是蛋白质元素,可催化它们自身的切除和对侧蛋白质序列的结合 (蛋白质拼接).
- 整体存在于连续或分割的形式;分割的整体需要两个片段来关联用于拼接.
- 人工分裂的intins使合成生物学和蛋白质相互作用研究的新应用.
研究的目的:
- 开发一个预测模型来识别活跃和不活跃的Intin分离站点.
- 创建一个用户友好的Web服务器 (Int&in) 用于预测Intin分区网站.
- 为了方便设计具有量身定制属性的新型分离整体.
主要方法:
- 使用后勤回归的机器学习模型的开发.
- 在126个从gp41-1,Npu DnaE和CL中分离的站点上训练模型.
- 使用科学文献中的97个分区站点进行验证.
- 包括蛋白质结构特征和序列保存数据.
主要成果:
- 机器学习模型在预测活跃和不活跃的分割站点 (0.79培训,0.78测试) 中取得了高准确性.
- Int&in 网络服务器提供了对互联网分区网站活动的准确预测.
- 该模型展示了结构和序列信息用于预测整体行为的实用性.
结论:
- Int&in 网络服务器准确地预测了分拆站点之间的信息,克服了启发式识别方法.
- 这种工具将加速用于各种应用的分割整体的设计和工程.
- 这些发现支持在蛋白质工程和合成生物学中使用机器学习.
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