Novartis Institutes for Biomedical Research, Wimblehurst Road, Horsham, West Sussex RH12 5AB, UK.
J Chem Inf Model. 2011 Oct 24;51(10):2666-79. doi: 10.1021/ci200168b. Epub 2011 Sep 30.
The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chemistry to explore a broader chemical space and thus present additional opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics. We report a new automated approach to diverse pocket selection, PocketAnalyzer(PCA), which applies principal component analysis and clustering to the output of a grid-based pocket detection algorithm. Since the approach works directly with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of atomic positional coordinates. The approach is technically straightforward and allows simultaneous analysis of mutants, isoforms, and protein structures derived from multiple sources with different residue numbering schemes. The PocketAnalyzer(PCA) approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allows identification of novel computationally derived binding-site conformations that are yet to be observed crystallographically. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby demonstrating the utility of PocketAnalyzer(PCA) for rationalizing and improving the understanding of the molecular basis of protein-ligand interaction and bioactivity. A Python program implementing the PocketAnalyzer(PCA) approach is available for download under an open-source license ( http://sourceforge.net/projects/papca/ or http://cpclab.uni-duesseldorf.de/downloads ).
新型结合部位构象的鉴定可极大地推动基于结构的配体设计项目的进展。不同的口袋形状促使药物化学探索更广泛的化学空间，从而为克服关键的药物发现问题（如效力、选择性、毒性和药代动力学）提供更多机会。我们报告了一种新的自动多样化口袋选择方法，即 PocketAnalyzer（PCA），它将主成分分析和聚类应用于基于网格口袋检测算法的输出。由于该方法直接使用口袋形状描述符，因此它避免了一些基于代理形状描述符的方法所存在的问题，例如一组原子位置坐标。该方法技术上简单直接，允许同时分析来自多个来源的突变体、同工型和蛋白质结构，这些来源具有不同的残基编号方案。通过为醛糖还原酶和病毒神经氨酸酶编译多样化的口袋形状集，说明了 PocketAnalyzer（PCA）方法。在这两种情况下，都可以确定尚未通过晶体学观察到的新型计算衍生的结合部位构象。事实上，随后鉴定出了能够利用这些新型结合部位构象的已知抑制剂，从而证明了 PocketAnalyzer（PCA）对于合理化和改善对蛋白质-配体相互作用和生物活性的分子基础的理解是有用的。一个实现了 PocketAnalyzer（PCA）方法的 Python 程序可在开放源代码许可证下下载（http://sourceforge.net/projects/papca/或http://cpclab.uni-duesseldorf.de/downloads）。
