Advancements in biocatalysis: From computational to metabolic engineering

Through several waves of technological research and un-matched innovation strategies, bio-catalysis has been widely used at the industrial level. Because of the value of enzymes, methods for producing value-added compounds and industrially-relevant fine chemicals through biological methods have been developed. A broad spectrum of numerous biochemical pathways is catalyzed by enzymes, including enzymes that have not been identified. However, low catalytic efficacy, low stability, inhibition by non-cognate substrates, and intolerance to the harsh reaction conditions required for some chemical processes are considered as major limitations in applied bio-catalysis. Thus, the development of green catalysts with multi-catalytic features along with higher efficacy and induced stability are important for bio-catalysis. Implementation of computational science with metabolic engineering, synthetic biology, and machine learning routes offers novel alternatives for engineering novel catalysts. Here, we describe the role of synthetic biology and metabolic engineering in catalysis. Machine learning algorithms for catalysis and the choice of an algorithm for predicting protein-ligand interactions are discussed. The importance of molecular docking in predicting binding and catalytic functions is reviewed. Finally, we describe future challenges and perspectives.