Improving catalytic function by ProSAR-driven enzyme evolution

By Richard J. Fox, S. C. Davis, E. Mundorff, L. Newman, V. Gavrilovic, Steven K. Ma, L. Chung, C. Ching, Sarena Tam, S. Muley and 5 more. 2007

515 citations recorded by the source corpus.

Subfields: Molecular Biology

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Citations in the corpus

Directed Polymerase Evolution (2013, background): “Lastly, the most guided of these newer approaches, statistical analysis of protein sequence activity relationships (ProSAR), assumes that the contributions of different mutations are additive (non-epistatic), and generates linear equations based on sequence-activity data of mutants from previous rou”
Protein sequence design with deep generative models (2021, methodology): “Early examples of this approach were developed by Fox [14] and Liao [15] in learning the relationship between enzyme sequence and cyanation or hydrolysis activity, respectively.”
Probing the Mutational Interplay between Primary and Promiscuous Protein Functions: A Computational-Experimental Approach (2012, methodology): “Having more fitting parameters than dependent variable values is a common occurrence in chemometrics, often addressed using partial least-squares [27,28] (PLS), a dimensionality reduction approach akin to principal component analysis.”
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design (2020, methodology): “We may use these models φ (cid:48) as proxies of φ , in order to generate and screen sequences in silico before they are sent to synthesis (Yang et al., 2019; Fox et al., 2007).”
Machine learning in protein engineering (2018, methodology): “The most beneﬁcial mutations can then be ﬁxed, deleterious mutations can be eliminated from the pool of considered mutations, and new mutations can be added to the pool of considered mutations (Fox et al., 2007).”
Directed evolution drives the next generation of biocatalysts. (2009, methodology): “For example, scientists at Codexis have developed an algorithm termed Pro-SAR (protein sequence-activity relationships) in which all mutations are assigned as either beneficial, neutral or deleterious based on the impact they have on the enzyme function that is being screened fo”
Machine-learning-guided directed evolution for protein engineering (2018, methodology): “…sequences, machine-learning methods can use this information to expedite evolution and expand the number of properties that can be optimized by intelligently selecting new variants to screen, thereby reaching higher fitness levels than are possible through directed evolution alone 10–14 (Fig.”