[Protein engineering: from directed evolution to computational design]. | Semantic Scholar (2024)

The progress of structure-function relationships of industrial enzymes and applications are reviewed, future developments are addressed, and rational design and de novo design become possible.

This review emphasizes the advancements in FDMs for the biosynthesis of hydroxylated aromatic compounds and presents a summary of three strategies aimed at enhancing their catalytic efficiency: developing efficient enzyme mutants through protein engineering, enhancing the supply and rapid circulation of critical cofactors, and facilitating cofactors delivery.

This study forecasts that the latest variant of SARS-CoV-2 may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days, and investigates the effectiveness of the “ML-Guided Design Correctly Predicts Combinatorial Effects Strategy” compared to the “ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy”.

Computational "inside-out" design can lead to the production of catalytically active and selective proteins, but their kinetic performances fall short of natural enzymes when combined with directed evolution, molecular dynamics simulations, and crowd-sourced structure-prediction approaches.

A directed evolution approach is described that augments recombination-based directed evolution by incorporating a strategy for statistical analysis of protein sequence activity relationships (ProSAR), which facilitates mutation-oriented enzyme optimization by permitting the capture of additional information contained in the sequence-activity data.

This review focuses on recent developments originating from various groups, including optimization of molecular biological methods for gene mutagenesis and the design of efficient strategies for their application, resulting in notable reduction of the screening effort.

A computational library design protocol that can increase enzyme stability by 20-35°C with little experimental screening, typically fewer than 200 variants, is described.

A new synthetic biology approach to protein engineering is described that avoids limitations by combining high throughput gene synthesis with machine learning-based design algorithms that only require the testing of a small number of variants.

It is demonstrated that computational design of a library with chemically diverse stabilizing mutations allows the engineering of drastically stabilized and fully functional variants of the mesostable enzyme limonene epoxide hydrolase.

The first fully automated design and experimental validation of a novel sequence for an entire protein is described, and a BLAST search shows that the designed sequence, full sequence design 1 (FSD-1), has very low identity to any known protein sequence.

Using new algorithms that rely on hashing techniques to construct active sites for multistep reactions, retro-aldolases that use four different catalytic motifs to catalyze the breaking of a carbon-carbon bond in a nonnatural substrate are designed.

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