Lamzin V S, Wilson K S
European Molecular Biology Laboratory, Hamburg, Germany.
Acta Crystallogr D Biol Crystallogr. 1993 Jan 1;49(Pt 1):129-47. doi: 10.1107/S0907444992008886.
An automated refinement procedure (ARP) for protein models is proposed, and its convergence properties discussed. It is comparable to the iterative least-squares minimization/difference Fourier synthesis approach for small molecules. ARP has been successfully applied to three proteins, and for two of them resulted in models very similar to those obtained by conventional least-squares refinement and rebuilding with FRODO. In real time ARP is about ten times faster than conventional refinement. In its present form ARP requires high (2.0 A or better) resolution data, which should be of high quality and a starting protein model having about 75% of the atoms in roughly the correct position. For the third protein at 2.4 A resolution, ARP was significantly less powerful but nevertheless gave definite improvement, in the density map at least.
提出了一种用于蛋白质模型的自动优化程序（ARP），并讨论了其收敛特性。它类似于小分子的迭代最小二乘最小化/差值傅里叶合成方法。ARP已成功应用于三种蛋白质，其中两种得到的模型与通过传统最小二乘优化以及使用FRODO重建得到的模型非常相似。在实时性方面，ARP比传统优化快约十倍。以其目前的形式，ARP需要高分辨率（2.0 Å或更好）的数据，数据应具有高质量，并且起始蛋白质模型中约75%的原子位置大致正确。对于分辨率为2.4 Å的第三种蛋白质，ARP的效果明显较差，但至少在密度图上有一定改善。
