S期激酶相关蛋白2(S-phase kinase-associated protein 2,Skp2)与Skp1形成的蛋白质聚合物在调控癌细胞生长周期中发挥着重要作用,而苯并吡喃酮类抑制剂(简称BPC)可有效抑制Skp1-Skp2的形成,但其分子识别机制尚不明确.通过生物信息学统...
详细信息
S期激酶相关蛋白2(S-phase kinase-associated protein 2,Skp2)与Skp1形成的蛋白质聚合物在调控癌细胞生长周期中发挥着重要作用,而苯并吡喃酮类抑制剂(简称BPC)可有效抑制Skp1-Skp2的形成,但其分子识别机制尚不明确.通过生物信息学统计分析已报道的Skp1-Skp2晶体结构,确定模拟体系后,首先用同源模建对其模拟体系缺失的结构进行补全;然后用分子对接方法获得Skp1-Skp2-BPC复合物模型并用于后续分子动力学模拟.计算结果表明:疏水相互作用是促使BPC特异性结合在由Skp2 W109、D110、L117、I120、R138和W139所构成口袋中的主要驱动力,自由能计算值与实验数据吻合较好.Skp2结合BPC后,结合口袋周围的氢键网络有所加强,口袋附近的溶剂化水分子数量明显减少,导致Skp1-Skp2的体系稳定性下降.体系构象成簇与运动性分析显示,Skp1-Skp2在结合BPC抑制剂后,Skp1的运动更加剧烈,这可能是BPC主要的抑制机理.
To speed up three-dimensional （3D） DC resistivity modeling, we present a new multigrid method, the aggregation-based algebraic multigrid method （AGMG）. We first discretize the differential equation of the secondar...
详细信息
To speed up three-dimensional （3D） DC resistivity modeling, we present a new multigrid method, the aggregation-based algebraic multigrid method （AGMG）. We first discretize the differential equation of the secondary potential field with mixed boundary conditions by using a seven-point finite-difference method to obtain a large sparse system of linear equations. Then, we introduce the theory behind the pairwise aggregation algorithms for AGMG and use the conjugate-gradient method with the V-cycle AGMG preconditioner （AGMG-CG） to solve the linear equations. We use typical geoelectrical models to test the proposed AGMG-CG method and compare the results with analytical solutions and the 3DDCXH algorithm for 3D DC modeling （3DDCXH）. In addition, we apply the AGMG-CG method to different grid sizes and geoelectrical models and compare it to different iterative methods, such as ILU-BICGSTAB, ILU-GCR, and SSOR-CG. The AGMG-CG method yields nearly linearly decreasing errors, whereas the number of iterations increases slowly with increasing grid size. The AGMG-CG method is precise and converges fast, and thus can improve the computational efficiency in forward modeling of three-dimensional DC resistivity.
暂无评论