Exploring protein fitness landscapes by directed evolution

By Philip A. Romero, Frances H. Arnold. 2009

1,139 citations recorded by the source corpus.

Directed evolution circumvents our profound ignorance of how a protein's sequence encodes its function by using iterative rounds of random mutation and artificial selection to discover new and useful proteins. Proteins can be tunedto adapt to new functions or environments by simple adaptive walks involving small numbers of mutations. Directed evolution studies have shown how rapidly some proteins can evolve under strong selection pressures and, because the entire 'fossil record' of evolutionary intermediates is available for detailed study, they have provided new insight into the relationship between sequence and function. Directed evolution has also shown how mutations that are functionally neutral can set the stage for further adaptation.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “…iteratively querying a model of the mutational ﬁtness landscape could help efﬁciently guide the introduction of mutations to enhance protein function (Romero & Arnold, 2009), inform protein design using a combination of activating mutants (Hu et al., 2018), and make rational substitutions to…”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “Directed evolution is a protein engineering technique that circumvents this intractability in a greedy way [12–14] without the availability of protein structure.”
Unified rational protein engineering with sequence-only deep representation learning (2019, methodology): “To confirm our cluster assignments, we picked a small set of 3 Cytochrome Oxidase (1,2,3) families from OXBench, 8 proteins total.”
Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization (2017, methodology): “We chose to generate a training set using chimeras produced by SCHEMA recombination, which was previously demonstrated to be useful for producing large sets (libraries) of diverse, functional chimeric sequences from homologous parent proteins [27].”
Function-guided protein design by deep manifold sampling (2021, background): “However, sequence space is large, discrete, and sparsely functional [2], where only a small fraction of sequences may fold into stable structural conformations.”
A model of substitution trajectories in sequence space and long-term protein evolution. (2015, background): “…potential epistatic interactions among substitutions that occurred recently (Weinreich et al. 2006; Bridgham et al. 2009; Lozovsky et al. 2009; Romero and Arnold 2009; Lunzer et al. 2010; Khan et al. 2011; Zhang et al. 2012; Covert et al. 2013); second, studies quantifying the degree of…”
Machine-learning-guided directed evolution for protein engineering (2018, background): “Moreover, directed evolution requires at least one minimally functional parent and a locally smooth sequence–function landscape 8 .”
Machine learning in protein engineering (2018, background): “Moreover, directed evolution requires at least one minimally-functional parent and a locally-smooth ﬁtness landscape for stepwise optimization (Romero & Arnold, 2009).”