Kamble Asmita, Srinivasan Sumana, Singh Harinder
Department of Biological Sciences, Sunandan Divatia School of Science, NMIMS Deemed to be University, Vile Parle (W), Mumbai, 400056, India.
Biosystems Engineering Lab, Department of Chemical Engineering, IIT Bombay, Powai, Mumbai, 400076, India.
Mol Biotechnol. 2019 Jan;61(1):53-59. doi: 10.1007/s12033-018-0132-1.
Enzymes are essential biological macromolecules, which catalyse chemical reactions and have impacted the human civilization tremendously. The importance of enzymes as biocatalyst was realized more than a century ago by eminent scientists like Kuhne, Buchner, Payen, Sumner, and the last three decades has seen exponential growth in enzyme industry, mainly due to the revolution in tools and techniques in molecular biology, biochemistry and production. This has resulted in high demand of enzymes in various applications like food, agriculture, chemicals, pharmaceuticals, cosmetics, environment and research sector. The cut-throat competition also pushes the enzyme industry to constantly discover newer and better enzymes regularly. The conventional methods to discover enzymes are generally costly, time consuming and have low success rate. Exploring the exponentially growing biological databases with the help of various computational tools can increase the discovering process, with less resource consumption and higher success rate. Present review discusses this approach, known as in-silico bioprospecting, which broadly involves computational searching of gene/protein databases to find novel enzymes.
酶是必不可少的生物大分子,它们催化化学反应,对人类文明产生了巨大影响。早在一个多世纪前,库恩、布赫纳、佩恩、萨姆纳等杰出科学家就认识到了酶作为生物催化剂的重要性。在过去三十年里,酶产业呈指数级增长,这主要归功于分子生物学、生物化学和生产领域工具和技术的变革。这导致酶在食品、农业、化工、制药、化妆品、环境和研究等各个应用领域的需求大增。激烈的竞争也促使酶产业不断定期发现更新、更好的酶。传统的酶发现方法通常成本高昂、耗时且成功率低。借助各种计算工具探索呈指数级增长的生物数据库,可以加快发现进程,减少资源消耗并提高成功率。本综述讨论了这种称为计算机辅助生物勘探的方法,该方法主要包括通过计算搜索基因/蛋白质数据库来寻找新型酶。