Pfizer Research Technology Center, Cambridge, Massachusetts 02139, Pfizer Global Research and Development, Ramsgate Road, Kent CT139NJ, Sandwich, United Kingdom.
J Chem Inf Model. 2010 Jan;50(1):155-69. doi: 10.1021/ci9003317.
A new computational algorithm for protein binding sites characterization and comparison has been developed, which uses a common reference framework of the projected ligand-space four-point pharmacophore fingerprints, includes cavity shape, and can be used with diverse proteins as no structural alignment is required. Protein binding sites are first described using GRID molecular interaction fields (GRID-MIFs), and the FLAP (fingerprints for ligands and proteins) method is then used to encode and compare this information. The discriminating power of the algorithm and its applicability for large-scale protein analysis was validated by analyzing various scenarios: clustering of kinase protein families in a relevant manner, predicting ligand activity across related targets, and protein-protein virtual screening. In all cases the results showed the effectiveness of the GRID-FLAP method and its potential use in applications such as identifying selectivity targets and tools/hits for new targets via the identification of other proteins with pharmacophorically similar binding sites.
已经开发出一种用于蛋白质结合位点特征描述和比较的新计算算法，该算法使用投影配体空间四点药效团指纹的通用参考框架，包括腔形状，并且可以与不同的蛋白质一起使用，因为不需要结构对齐。首先使用 GRID 分子相互作用场 (GRID-MIFs) 来描述蛋白质结合位点，然后使用 FLAP（配体和蛋白质的指纹）方法对该信息进行编码和比较。通过分析各种情况验证了该算法的区分能力及其在大规模蛋白质分析中的适用性：以相关方式对激酶蛋白家族进行聚类、预测相关靶标上的配体活性，以及进行蛋白质-蛋白质虚拟筛选。在所有情况下，结果都表明了 GRID-FLAP 方法的有效性及其在识别选择性靶标和通过识别具有类似药效团结合位点的其他蛋白质来寻找新靶标工具/命中的潜在用途。
