Exploring protein fitness landscapes by directed evolution
TLDR
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.Abstract:
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 tuned to 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.read more
Citations
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A transposase strategy for creating libraries of circularly permuted proteins
TL;DR: Construction of a Thermotoga neapolitana adenylate kinase (AK) library using PERMUTE revealed that this approach produces vectors that express circularly permuted proteins with distinct sequence diversity from existing methods.
Posted ContentDOI
Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences
Alexander Rives,Siddharth Goyal,Joshua Meier,Demi Guo,Myle Ott,C. Lawrence Zitnick,Jerry Ma,Rob Fergus,Rob Fergus +8 more
TL;DR: This work uses unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state- of- the-art features for long-range contact prediction.
Journal ArticleDOI
Directed Evolution: Bringing New Chemistry to Life.
TL;DR: The evolution of nature's enzymes can lead to the discovery of new reactivity, transformations not known in biology, and reactivity inaccessible by small‐molecule catalysts.
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley,Grigory Khimulya,Surojit Biswas,Surojit Biswas,Mohammed AlQuraishi,George M. Church,George M. Church +6 more
TL;DR: Deep learning is applied to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded and broadly applicable to unseen regions of sequence space.
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Machine-learning-guided directed evolution for protein engineering.
TL;DR: The steps required to build machine-learning sequence–function models and to use those models to guide engineering are introduced and the underlying principles of this engineering paradigm are illustrated with the help of case studies.
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