Panda Gayatri, Mishra Neha, Sharma Disha, Kutum Rintu, Bhoyar Rahul C, Jain Abhinav, Imran Mohamed, Senthilvel Vigneshwar, Divakar Mohit Kumar, Mishra Anushree, Garg Parth, Banerjee Priyanka, Sivasubbu Sridhar, Scaria Vinod, Ray Arjun
Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
Front Pharmacol. 2022 Jul 5;13:858345. doi: 10.3389/fphar.2022.858345. eCollection 2022.
India confines more than 17% of the world's population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.
印度容纳了世界上超过17%的人口，其基因构成多样，存在多种具有临床相关性的罕见突变，这些突变属于许多亚组，在全球测序数据集（如包含有限印度族裔样本的千人基因组数据（1KG））中未得到充分重视。此类数据库对制药和药物开发行业至关重要，因为多样性在识别药物不良反应的遗传易感性方面起着关键作用。利用最近发布的、最大的经过整理的印度基因组数据库（IndiGen）和千人基因组数据中存在的变异信息，对属于激酶编码基因（第二大药物靶点组）的变异进行了定性和比较序列及结构研究。序列水平分析基于非同义单核苷酸变异（nsSNVs）和氨基酸交换频率确定了不同人群之间的异同，而对IndiGen变异进行了与UniProtKB Humsavar数据中报告的致病变异的比较结构分析。研究了这些变异对蛋白质结构特征（如结构稳定性、溶剂可及性、疏水性和氢键网络）的影响。对这些含有印度变异的蛋白质进行已知药物的虚拟筛选，揭示了由于印度人群中存在的变异而在结合强度上产生的关键差异。总之，本研究对理解世界第二大人口中存在的常见变异及其在序列、结构和药物基因组学格局中的影响进行了全面调查。本文报道的初步研究支持对印度人群特有的药物不良反应进行筛选和检测，这有助于开发针对印度人群药物相关不良事件的临床前和上市后筛选技术。
