Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA.
Structure. 2012 Jun 6;20(6):987-97. doi: 10.1016/j.str.2012.03.009. Epub 2012 May 3.
Proteins perform functions through interacting with other molecules. However, structural details for most of the protein-ligand interactions are unknown. We present a comparative approach (COFACTOR) to recognize functional sites of protein-ligand interactions using low-resolution protein structural models, based on a global-to-local sequence and structural comparison algorithm. COFACTOR was tested on 501 proteins, which harbor 582 natural and drug-like ligand molecules. Starting from I-TASSER structure predictions, the method successfully identifies ligand-binding pocket locations for 65% of apo receptors with an average distance error 2 Å. The average precision of binding-residue assignments is 46% and 137% higher than that by FINDSITE and ConCavity. In CASP9, COFACTOR achieved a binding-site prediction precision 72% and Matthews correlation coefficient 0.69 for 31 blind test proteins, which was significantly higher than all other participating methods. These data demonstrate the power of structure-based approaches to protein-ligand interaction predictions applicable for genome-wide structural and functional annotations.
蛋白质通过与其他分子相互作用来执行功能。然而，大多数蛋白质-配体相互作用的结构细节尚不清楚。我们提出了一种基于全局到局部序列和结构比较算法的比较方法（COFACTOR），用于识别蛋白质-配体相互作用的功能位点，使用低分辨率的蛋白质结构模型。COFACTOR 在 501 个蛋白质上进行了测试，这些蛋白质含有 582 个天然和类药配体分子。从 I-TASSER 结构预测开始，该方法成功地确定了 65%的apo 受体的配体结合口袋位置，平均距离误差为 2Å。结合残基分配的平均精度比 FINDSITE 和 ConCavity 分别高 46%和 137%。在 CASP9 中，COFACTOR 对 31 个盲测蛋白质的结合位点预测精度为 72%，马修斯相关系数为 0.69，明显高于所有其他参与方法。这些数据表明，基于结构的方法在蛋白质-配体相互作用预测方面具有强大的作用，适用于全基因组的结构和功能注释。
