Structural-Based Modeling in Protein Engineering. A Must Do

Abstract

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

A Plurizyme with Transaminase and Hydrolase Activity Catalyzes Cascade Reactions

“…The protocol used to design an artificial hydrolase site has been explained previously, [18, 19] including two review articles [29, 30] . Briefly, the process begins with scanning the transaminase surface to identify noncatalytic ester binding sites using global Protein Energy Landscape Exploration (PELE) exploration [31] .…”

“…The protocol used to design an artificial hydrolase site has been explained previously, [18,19] including two review articles. [29,30] Briefly, the process begins with scanning the transaminase surface to identify noncatalytic ester binding sites using global Protein Energy Landscape Exploration (PELE) exploration. [31] Next, we perform local explorations of active site variants to introduce a well-positioned catalytic triad while also considering the existence of oxyanion holes that are required for efficient ester hydrolysis.…”

Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering

“…Structure-based models for predicting residue coevolutionary effects are far less abundant than sequence-based models but can nonetheless yield important predictions. 92 We want to highlight recent approaches based on structural data and molecular dynamics simulation in addition to pure sequence data. In 2016, Goldenzweig et al developed PROSS, a computational strategy aiming to predict mutations that will not result in misfolds or aggregates, thus increasing functional yields and parameters.…”

“…Structure-based models for predicting residue coevolutionary effects are far less abundant than sequence-based models but can nonetheless yield important predictions . We want to highlight recent approaches based on structural data and molecular dynamics simulation in addition to pure sequence data.…”

Computational Advances in Protein Engineering and Enzyme Design

“…As interest in machine learning for enzyme design continues to increase, Osadchy and Kolodny highlight how deep learning tools can be used to help protein engineers find good sequences that will lead to a desired property of the enzyme, and Priyakumar and co-workers present “SCONES”, a self-consistent neural network for protein stability prediction upon mutation . Following a year of substantial advances in protein structure prediction , and de novo protein design, Guallar and co-workers emphasize the critical importance of structure-based modeling in protein design, also presenting a new technique coupling Rosetta , and the Guallar group’s Protein Energy Landscape Exploration (PELE) software, , that is capable of greatly accelerating and streamlining the computational design process. From a mechanistic perspective, Sugita and co-workers introduce new hybrid quantum mechanical/molecular mechanical (QM/MM) tools in the GENESIS program, which allows for minimum-energy pathways and free-energy profiles of enzymatic reactions in a parallelized and highly computationally efficient way, making it feasible to explore larger numbers of enzyme variants using DFT-based QM/MM calculations.…”