TL;DR
The project aimed to enhance the catalytic properties of formolase, whilst critically assessing the predictive power and limitations of the computational tools employed, and critical of the challenges faced and biases introduced in computational workflow development.
This review examines enzyme engineering from classical methods to AI-assisted biocatalyst development, highlighting key advances, challenges, and emerging trends in autonomous laboratories, sustainable biocatalysis, and computational protein design.
UnZipro is presented, an efficient, scalable, and generalizable framework for zero-shot, in silico protein evolution, that integrates a compact, pre-trained inverse folding model with meta-learning to derive family-specific fitness landscapes.
It is shown that prospective structure selection, rather than structure generation, represents the primary bottleneck in ensemble-based VS, highlighting an urgent need for novel structural descriptors to identify high-performing conformations.
How solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering is discussed.
It is demonstrated that modern EC predictors largely fail to distinguish catalytically incompetent variants from functional enzymes, and it is proposed that integrating structure-aware negative examples into both training and benchmarking is critical for developing functionally robust models in computational enzymology...
The chemistry department of the BIPM and the National Physics Laboratory (UK) have collaborated to investigate potential candidate materials for such comparisons, including the measurement of the purity of a de novo peptide, C3triskelion, capable of self-assembling into artificial virus-like capsids exhibiting strong a...
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