The challenge of predicting distal active site mutations in computational enzyme design

Abstract

Many computational enzyme design approaches have been developed in recent years that focus on a reduced set of key enzymatic features. Initial protocols mostly focused on the chemical steps(s) through transition state stabilization, whereas most recent approaches exploit the enzyme conformational dynamics often crucial for substrate binding, product release, and allosteric regulation. The detailed evaluation of the conformational landscape of many laboratory‐evolved enzymes has revealed dramatic changes on the relative stabilities of the conformational states after mutation, favoring those conformational states key for the novel functionality. Of note is that these mutations are often located all around the enzyme structure, which contrasts with most of the computational design strategies that reduce the problem into active site alterations. Recent computational strategies have been developed that consider enzyme design as a population shift problem, that is, redistribution of the relative stabilities of the conformational states induced by mutations. These strategies focus on reconstructing the conformational landscape of the enzyme, applying correlation‐based tools to elucidate the underlying allosteric network of interactions and identify potential mutation hotspots located at the active site, but most importantly at distal positions for the first time. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Molecular Modeling

Conformational Landscapes of Halohydrin Dehalogenases and Their Accessible Active Site Tunnels

“…Computational techniques and, in particular, molecular dynamics (MD) simulations are particularly useful in elucidating the ensemble of thermally accessible enzyme conformations by integrating Newton's laws of motion [25]. This enables the reconstruction of the enzyme conformation landscape and assess how this is shifted by ligand binding, sequence differences between protein family members, and/or the introduction of mutations in the enzyme active site or at distal positions [7,26]. Recovery of time-dependent dynamical descriptors, such as volume cavities, solvent-accessible surface area, or changes in internal tunnels/channels is also possible by post-processing the highly dimensional MD datasets [4,27].…”

Power of Biocatalysis for Organic Synthesis

“…The high complexity of the biocatalyst, as outlined in the previous section, makes the computational design of new variants highly challenging and has led to the development of a plethora of computational approaches for enzyme design. 33 , 75 …”

“…Whether machine learning will become a key technology in this context will be seen in the future, 207 − 209 but for sure, computational design requires various approaches and methods. 33 , 75 Huge efforts have been spent on designing enzymes, and basic research in the years to come will provide new approaches to create highly efficient, stable biocatalysts that are also easy to produce.…”

Rational Prediction of Distal Activity-Enhancing Mutations in Tryptophan Synthase

“…[14][15][16][17] Rational design emerged as an attractive alternative to decrease the screening efforts to a reduced number of promising enzyme variants based on prior structural knowledge and computational approaches. [18][19][20][21] Given the sophisticated nature of enzyme catalysis, multiple computational strategies and protocols have been developed in recent years for computational enzyme design. 20 The evaluation of the conformational landscape of enzymes along distinct natural and DE evolutionary pathways has evidenced that the introduced mutations progressively tune the conformational ensemble, stabilizing key conformational states for the novel function.…”

“…[18][19][20][21] Given the sophisticated nature of enzyme catalysis, multiple computational strategies and protocols have been developed in recent years for computational enzyme design. 20 The evaluation of the conformational landscape of enzymes along distinct natural and DE evolutionary pathways has evidenced that the introduced mutations progressively tune the conformational ensemble, stabilizing key conformational states for the novel function. 4,6,10,20 Of note is that the mutations introduced with DE are often located distal from the active site pocket, which given the vast sequence space are computationally challenging to predict.…”