Belda Ignasi, Madurga Sergio, Tarragó Teresa, Llorà Xavier, Giralt Ernest
Institut de Recerca Biomèdica, Parc Científic de Barcelona, Universitat de Barcelona, Josep Samitier, Barcelona, Spain.
Mol Divers. 2007 Feb;11(1):7-21. doi: 10.1007/s11030-006-9053-1. Epub 2006 Dec 13.
The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space.
肽设计中可实现的令人惊叹的结构多样性程度，引发了对能够评估众多候选结构的计算资源的需求。在我们的具体案例中，我们将肽设计问题转化为一个优化问题，并结合对接使用进化计算（EC）来进行组合搜索。然而，在具有不同最优解的巨大搜索空间中使用EC可能会带来某些缺点。例如，EC容易将搜索集中在首次找到的良好区域。这是一个问题，不仅是因为会自动且不理想地排除潜在的良好搜索空间区域，还因为找到的解决方案可能在化学合成上极其困难，甚至可能是错误的对接阳性结果。为了避免拒绝潜在的良好解决方案并最大化搜索的分子多样性，我们实施了进化多模态搜索技术，以及多模态算法所需的分子多样性度量，以测量搜索空间各个区域之间的差异。
