Laboratory for Biomolecular Modeling, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland.
Swiss Institute of Bioinformatics (SIB), Lausanne, CH-1015, Switzerland.
Sci Rep. 2017 Mar 22;7(1):235. doi: 10.1038/s41598-017-00266-w.
Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.
当使用整合建模方法时，预测大型分子组装体的结构仍然是结构生物学中的一项具有挑战性的任务。其中一个主要问题源于用于预测天然复合物结构的异质实验数据的处理。我们提出了一种新方法，首次应用于一组对称复合物，该方法基于进化计算，可以独立处理每个可用的实验输入，从而避免了在优化过程中平衡分配给聚合适应度函数的权重分量的需要。
