Kanakaveti Vishnupriya, Sakthivel Ramasamy, Rayala S K, Gromiha M Michael
Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Molecular Oncology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Chem Biol Drug Des. 2017 Aug;90(2):308-316. doi: 10.1111/cbdd.12952. Epub 2017 Mar 1.
Evasion of apoptosis owing to aberrant expression of Bcl-2 (B-cell lymphoma-2) anti-apoptotic proteins is a promising hallmark of cancer. These proteins are associated with resistance to chemotherapy and radiation. Currently available QSAR models are limited to a set of inhibitors corresponding to a particular chemical scaffold, and unified models are required to identify the differential specificity of diverse compounds toward inhibiting these targets. In this study, we predicted the factors driving differential activity and specificity implementing multiplexed QSAR analysis for a dataset of 1,649 reported inhibitors of Bcl-2 (B-cell lymphoma-2) and Bcl-xL (B-cell lymphoma-extra large). We developed QSAR models for seven diverse scaffolds and critically analyzed the chemical space with coupling factors. The correlation values of QSAR models for Bcl-2 and Bcl-xL range from 0.95 to 0.985. The MAE and sMAPE of the models were in the range of 0.052-5.4 nm and 0.41%-10%, respectively, signifying model robustness. The crucial descriptors and moieties accounting for the activity were benchmarked against experimentally determined binding patterns. The comprehensive analysis made in the study explores latent features of the chemical space in a broad perspective. Further, we have developed a user-friendly Web server for predicting a specific/dual inhibitor of Bcl-2 and Bcl-xL [http://www.iitm.ac.in/bioinfo/APPLE/].
由于抗凋亡蛋白Bcl-2(B细胞淋巴瘤-2)的异常表达而导致的细胞凋亡逃避是一种很有前景的癌症标志。这些蛋白与化疗和放疗抗性相关。目前可用的定量构效关系(QSAR)模型仅限于对应特定化学支架的一组抑制剂,因此需要统一模型来识别不同化合物对这些靶点抑制作用的差异特异性。在本研究中,我们通过对1649种已报道的Bcl-2(B细胞淋巴瘤-2)和Bcl-xL(B细胞淋巴瘤-特大)抑制剂数据集进行多重QSAR分析,预测了驱动差异活性和特异性的因素。我们为七种不同的支架开发了QSAR模型,并通过耦合因子对化学空间进行了严格分析。Bcl-2和Bcl-xL的QSAR模型的相关值范围为0.95至0.985。模型的平均绝对误差(MAE)和对称平均绝对百分比误差(sMAPE)分别在0.052 - 5.4纳米和0.41% - 10%的范围内,表明模型具有稳健性。将解释活性的关键描述符和基团与实验确定的结合模式进行了基准比较。该研究中进行的综合分析从广泛的角度探索了化学空间的潜在特征。此外,我们开发了一个用户友好的网络服务器,用于预测Bcl-2和Bcl-xL的特异性/双重抑制剂[http://www.iitm.ac.in/bioinfo/APPLE/]。