Computational Structural Biology, Department of Life Science Informatics B-IT, Life & Medical Sciences (LIMES) Institute, University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany.
J Chem Inf Model. 2012 Oct 22;52(10):2657-69. doi: 10.1021/ci3000453. Epub 2012 Oct 4.
At the beginning of each molecular dynamics membrane simulation stands the generation of a suitable starting structure which includes the working steps of aligning membrane and protein and seamlessly accommodating the protein in the membrane. Here we introduce two efficient and complementary methods based on pre-equilibrated membrane patches, automating these steps. Using a voxel-based cast of the coarse-grained protein, LAMBADA computes a hydrophilicity profile-derived scoring function based on which the optimal rotation and translation operations are determined to align protein and membrane. Employing an entirely geometrical approach, LAMBADA is independent from any precalculated data and aligns even large membrane proteins within minutes on a regular workstation. LAMBADA is the first tool performing the entire alignment process automatically while providing the user with the explicit 3D coordinates of the aligned protein and membrane. The second tool is an extension of the InflateGRO method addressing the shortcomings of its predecessor in a fully automated workflow. Determining the exact number of overlapping lipids based on the area occupied by the protein and restricting expansion, compression and energy minimization steps to a subset of relevant lipids through automatically calculated and system-optimized operation parameters, InflateGRO2 yields optimal lipid packing and reduces lipid vacuum exposure to a minimum preserving as much of the equilibrated membrane structure as possible. Applicable to atomistic and coarse grain structures in MARTINI format, InflateGRO2 offers high accuracy, fast performance, and increased application flexibility permitting the easy preparation of systems exhibiting heterogeneous lipid composition as well as embedding proteins into multiple membranes. Both tools can be used separately, in combination with other methods, or in tandem permitting a fully automated workflow while retaining a maximum level of usage control and flexibility. To assess the performance of both methods, we carried out test runs using 22 membrane proteins of different size and transmembrane structure.
在每个分子动力学膜模拟的开始都需要生成一个合适的起始结构,其中包括对齐膜和蛋白质以及无缝地将蛋白质容纳在膜中的步骤。在这里,我们介绍两种基于预平衡膜补丁的高效且互补的方法,这些方法可以自动完成这些步骤。使用基于粗粒度蛋白质的体素铸型,LAMBADA 计算基于亲水性轮廓的评分函数,基于该评分函数确定最佳旋转和平移操作,以对齐蛋白质和膜。采用完全几何方法,LAMBADA 不依赖任何预先计算的数据,甚至可以在常规工作站上在几分钟内对齐大型膜蛋白。LAMBADA 是第一个执行整个对齐过程的工具,同时为用户提供对齐的蛋白质和膜的显式 3D 坐标。第二个工具是 InflateGRO 方法的扩展,在完全自动化的工作流程中解决了其前身的缺点。根据蛋白质所占据的面积确定重叠脂质的确切数量,并通过自动计算和系统优化的操作参数将扩展、压缩和能量最小化步骤限制在相关脂质的子集内,InflateGRO2 可以生成最佳的脂质包装,并将脂质真空暴露减少到最低限度,同时尽可能多地保留平衡膜结构。适用于 MARTINI 格式的原子和粗粒度结构,InflateGRO2 提供高精度、快速性能和更高的应用灵活性,允许轻松制备具有异质脂质组成的系统以及将蛋白质嵌入多个膜中。这两种工具都可以单独使用,也可以与其他方法结合使用,或者串联使用,从而实现完全自动化的工作流程,同时保留最大程度的使用控制和灵活性。为了评估这两种方法的性能,我们使用不同大小和跨膜结构的 22 种膜蛋白进行了测试运行。