Kumar Sivakumar Prasanth, Jasrai Yogesh T, Mehta Vijay P, Pandya Himanshu A
a Department of Bioinformatics, Applied Botany Centre (ABC) , Gujarat University , Ahmedabad 380009 , Gujarat , India.
J Biomol Struct Dyn. 2015;33(4):706-22. doi: 10.1080/07391102.2014.908142. Epub 2014 Apr 15.
Quantitative pharmacophore hypothesis combines the 3D spatial arrangement of pharmacophore features with biological activities of the ligand data-set and predicts the activities of geometrically and/or pharmacophoric similar ligands. Most pharmacophore discovery programs face difficulties in conformational flexibility, molecular alignment, pharmacophore features sampling, and feature selection to score models if the data-set constitutes diverse ligands. Towards this focus, we describe a ligand-based computational procedure to introduce flexibility in aligning the small molecules and generating a pharmacophore hypothesis without geometrical constraints to define pharmacophore space, enriched with chemical features necessary to elucidate common pharmacophore hypotheses (CPHs). Maximal common substructure (MCS)-based alignment method was adopted to guide the alignment of carbon molecules, deciphered the MCS atom connectivity to cluster molecules in bins and subsequently, calculated the pharmacophore similarity matrix with the bin-specific reference molecules. After alignment, the carbon molecules were enriched with original atoms in their respective positions and conventional pharmacophore features were perceived. Distance-based pharmacophoric descriptors were enumerated by computing the interdistance between perceived features and MCS-aligned 'centroid' position. The descriptor set and biological activities were used to develop support vector machine models to predict the activities of the external test set. Finally, fitness score was estimated based on pharmacophore similarity with its bin-specific reference molecules to recognize the best and poor alignments and, also with each reference molecule to predict outliers of the quantitative hypothesis model. We applied this procedure to a diverse data-set of 40 HIV-1 integrase inhibitors and discussed its effectiveness with the reported CPH model.
定量药效团假说将药效团特征的三维空间排列与配体数据集的生物活性相结合，并预测几何和/或药效团相似配体的活性。如果数据集包含多种不同的配体，大多数药效团发现程序在构象灵活性、分子比对、药效团特征采样以及用于对模型评分的特征选择方面都会面临困难。针对这一重点，我们描述了一种基于配体的计算程序，该程序在对齐小分子和生成药效团假说时引入灵活性，无需几何约束来定义药效团空间，该空间富含阐明共同药效团假说（CPH）所需的化学特征。采用基于最大公共子结构（MCS）的比对方法来指导碳分子的比对，解读MCS原子连接性以将分子聚类到不同的组中，随后，计算与组特异性参考分子的药效团相似性矩阵。比对后，碳分子在各自位置上富含原始原子，并识别出传统的药效团特征。通过计算感知到的特征与MCS对齐的“质心”位置之间的相互距离，列举基于距离的药效团描述符。描述符集和生物活性用于开发支持向量机模型，以预测外部测试集的活性。最后，基于与组特异性参考分子的药效团相似性估计适应度得分，以识别最佳和较差的比对，并且还与每个参考分子进行比较，以预测定量假说模型的异常值。我们将此程序应用于40种HIV-1整合酶抑制剂的多样化数据集，并与报道的CPH模型讨论了其有效性。
