Rastelli Giulio, Degliesposti Gianluca, Del Rio Alberto, Sgobba Miriam
Dipartimento di Scienze Farmaceutiche, Università di Modena e Reggio Emilia, Via Campi 183, 41100 Modena, Italy.
Chem Biol Drug Des. 2009 Mar;73(3):283-6. doi: 10.1111/j.1747-0285.2009.00780.x.
Binding estimation after refinement (BEAR) is a novel automated computational procedure suitable for correcting and overcoming limitations of docking procedures such as poor scoring function and the generation of unreasonable ligand conformations. BEAR makes use of molecular dynamics simulation followed by MM-PBSA and MM-GBSA binding free energy estimates as tools to refine and rescore the structures obtained from docking virtual screenings. As binding estimation after refinement relies on molecular dynamics, the entire procedure can be tailored to the needs of the end-user in terms of computational time and the desired accuracy of the results. In a validation test, binding estimation after refinement and rescoring resulted in a significant enrichment of known ligands among top scoring compounds compared with the original docking results. Binding estimation after refinement has direct and straightforward application in virtual screening for correcting both false-positive and false-negative hits, and should facilitate more reliable selection of biologically active molecules from compound databases.
精炼后结合估计（BEAR）是一种新颖的自动化计算程序，适用于校正和克服对接程序的局限性，如评分函数不佳以及生成不合理的配体构象。BEAR利用分子动力学模拟，随后进行MM-PBSA和MM-GBSA结合自由能估计，作为优化和重新评分从对接虚拟筛选中获得的结构的工具。由于精炼后结合估计依赖于分子动力学，整个程序可以根据最终用户在计算时间和所需结果准确性方面的需求进行定制。在一项验证测试中，与原始对接结果相比，精炼和重新评分后的结合估计导致在得分最高的化合物中已知配体显著富集。精炼后结合估计在虚拟筛选中可直接且简单地应用于校正假阳性和假阴性命中结果，并且应有助于从化合物数据库中更可靠地选择生物活性分子。
