Relative specificity as an important consideration in the big data era

Abstract

Technological breakthroughs such as high-throughput methods, genomics, single-cell studies, and machine learning have fundamentally transformed research and ushered in the big data era of biology. Nevertheless, current data collections, analyses, and modeling frequently overlook relative specificity, a crucial property of molecular interactions in biochemical systems. Relative specificity describes how, for example, an enzyme reacts with its many substrates at different rates, and how this discriminatory action alone is sufficient to modulate the substrates and downstream events. As a corollary, it is not only important to comprehensively identify an enzyme’s substrates, but also critical to quantitatively determine how the enzyme interacts with the substrates and to evaluate how it shapes subsequent biological outcomes. Genomics and high-throughput techniques have greatly facilitated the studies of relative specificity in the 21st century, and its functional significance has been demonstrated in complex biochemical systems including transcription, translation, protein kinases, RNA-binding proteins, and animal microRNAs (miRNAs), although it remains ignored in most work. Here we analyze recent findings in big data and relative specificity studies and explain how the incorporation of relative specificity concept might enhance our mechanistic understanding of gene functions, biological phenomena, and human diseases.