Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.
Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints.
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借助 3D 蛋白质-配体相互作用指纹增强当前评分功能的性能
DOI：
10.1186/s12859-017-1750-5
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发表时间：
2017-07-18
影响因子：
3
通讯作者：
Wang R
中科院分区：
文献类型：
--
作者：
Liu J;Su M;Liu Z;Li J;Li Y;Wang R
背景在基于结构的药物设计中，结合亲和力预测仍然是当前评分功能的一个具有挑战性的目标。目标偏向得分函数的发展为解决这一问题提供了新的可能性，但这种方法也伴随着某些技术困难。我们之前报告了知识引导评分(KGS)方法作为一种替代方法(BMC BioInformation，2010，11,193-208)。其核心思想是基于已知的参考复合体的结合数据来计算给定蛋白质-配体复合体的结合亲和力，从而有效地减少结合亲和力预测的误差。KGS2结合四种评分功能(X-Score、ChemPLP、ASP和GoldScore)在五种药物靶点(HIV-1蛋白酶、碳酸氢酶2、β-分泌酶1、β-胰酶和检查点激酶1)上进行评估。在原位评分试验中，大多数病例在应用KGS2后都有明显的改善。此外，在所有情况下，KGS2的性能都好于KGS。在更具挑战性的分子对接实验中，KGS2的应用在某些情况下也改善了构效关系。结论KGS2可以作为现有评分功能的一个方便的附加组件，而不需要对其进行重新设计，其应用并不局限于某些特定的靶蛋白作为定制的评分功能。作为一种内插方法，随着对蛋白质-配体复杂结构和结合亲和力数据的了解的增加，其原理上的精度可以进一步提高。我们期待KGS2将成为一个实用的工具，以提高现有评分函数在结合亲和力预测中的性能。KGS2软件可在联系作者后获得。
BackgroundIn structure-based drug design, binding affinity prediction remains as a challenging goal for current scoring functions. Development of target-biased scoring functions provides a new possibility for tackling this problem, but this approach is also associated with certain technical difficulties. We previously reported the Knowledge-Guided Scoring (KGS) method as an alternative approach (BMC Bioinformatics, 2010, 11, 193–208). The key idea is to compute the binding affinity of a given protein-ligand complex based on the known binding data of an appropriate reference complex, so the error in binding affinity prediction can be reduced effectively.ResultsIn this study, we have developed an upgraded version, i.e. KGS2, by employing 3D protein-ligand interaction fingerprints in reference selection. KGS2 was evaluated in combination with four scoring functions (X-Score, ChemPLP, ASP, and GoldScore) on five drug targets (HIV-1 protease, carbonic anhydrase 2, beta-secretase 1, beta-trypsin, and checkpoint kinase 1). In the in situ scoring test, considerable improvements were observed in most cases after application of KGS2. Besides, the performance of KGS2 was always better than KGS in all cases. In the more challenging molecular docking test, application of KGS2 also led to improved structure-activity relationship in some cases.ConclusionsKGS2 can be applied as a convenient “add-on” to current scoring functions without the need to re-engineer them, and its application is not limited to certain target proteins as customized scoring functions. As an interpolation method, its accuracy in principle can be improved further with the increasing knowledge of protein-ligand complex structures and binding affinity data. We expect that KGS2 will become a practical tool for enhancing the performance of current scoring functions in binding affinity prediction. The KGS2 software is available upon contacting the authors.
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影响因子：
5.6
作者：
Damm-Ganamet KL;Smith RD;Dunbar JB Jr;Stuckey JA;Carlson HA
通讯作者：
Carlson HA
影响因子：
5.6
作者：
Dunbar JB Jr;Smith RD;Damm-Ganamet KL;Ahmed A;Esposito EX;Delproposto J;Chinnaswamy K;Kang YN;Kubish G;Gestwicki JE;Stuckey JA;Carlson HA
通讯作者：
Carlson HA
影响因子：
5.6
作者：
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通讯作者：
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影响因子：
22.7
作者：
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通讯作者：
KERBOSCH, J
影响因子：
5.8
作者：
Huang, Ying;Niu, Beifang;Li, Weizhong
通讯作者：
Li, Weizhong