Combined Approach to Engineer a Highly Active Mutant of Processive Chitinase Hydrolyzing Crystalline Chitin

Product inhibition slow down the moving velocity of processive chitinase and sliding-intermediate state blocks re-binding of product

Processive movement is the key reaction for crystalline polymer degradation by enzyme. Product release is an important phenomenon in resetting the moving cycle, but how it affects chitinase kinetics was unknown. Therefore, we investigated the effect of diacetyl chitobiose (C2) on the biochemical activity and movement of chitinase A from Serratia marcescens (SmChiA). The apparent inhibition constant of C2 on crystalline chitin degradation of SmChiA was 159 μM. The binding position of C2 obtained by X-ray crystallography was at subsite +1, +2 and Trp275 interact with C2 at subsite +1. This binding state is consistent with the competitive inhibition obtained by biochemical analysis. The apparent inhibition constant of C2 on the moving velocity of high-speed (HS) AFM observations was 330 μM, which is close to the biochemical results, indicating that the main factor in crystalline chitin degradation is also the decrease in degradation activity due to inhibition of processive movement. The Trp275 is a key residue for making a sliding intermediate complex. SmChiA W275A showed weaker activity and affinity than WT against crystalline chitin because it is less processive than WT. In addition, biochemical apparent inhibition constant for C2 of SmChiA W275A was 45.6 μM. W275A mutant showed stronger C2 inhibition than WT even though the C2 binding affinity is weaker than WT. This result indicated that Trp275 is important for the interaction at subsite +1, but also important for making sliding intermediate complex and physically block the rebinding of C2 on the catalytic site for crystalline chitin degradation.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Label-free monitoring of crystalline chitin hydrolysis by chitinase based on Raman spectroscopy

We demonstrate a method for label-free monitoring of hydrolytic activity of crystalline-chitin-degrading enzyme, chitinase, by means of Raman spectroscopy. We found that crystalline chitin exhibited a characteristic Raman peak at 2995 cm-1, which did not appear in the reaction product, N,N'-diacetylchitobiose. We used this Raman peak as a marker of crystalline chitin degradation to monitor the hydrolytic activity of chitinase. When the crystalline chitin suspension and chitinase were mixed together, the peak intensity of crystalline chitin at 2995 cm-1 was linearly decreased depending on incubation time. The decrease in peak intensity was inversely correlated with the increase in the amount of released N,N'-diacetylchitobiose, which was measured by conventional colorimetric assay with alkaline ferricyanide. Our result, presented here, provides a new method for simple, in situ, and label-free monitoring of enzymatic activity of chitinase.