Deep sequencing methods for protein engineering and design

Abstract

The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances.

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

“…Dourado and Flores have built a ΔΔ G dataset based on both homology models and fitted models generated by fitting into low‐resolution Cryo‐EM density map. Deep sequencing is a high‐throughput experimental method that can detect mutations, and provides estimates of experimental binding affinity based on the enrichment data . The Critical Assessment of Prediction of Interactions (CAPRI), a community‐wide experiment on the comparative evaluation of protein–protein docking for structure prediction, has run a ΔΔ G prediction challenge based on experimental data from deep sequencing .…”

“…Deep sequencing is a high-throughput experimental method that can detect mutations, and provides estimates of experimental binding affinity based on the enrichment data. [37][38][39] The Critical Assessment of Prediction of Interactions (CAPRI), a community-wide experiment on the comparative evaluation of protein-protein docking for structure prediction, has run a ΔΔG prediction challenge based on experimental data from deep sequencing. 40 Deep sequencing was also exploited for two specific complexes, type I dockerin-cohesin 36 and TCR-pepMHC, 41 to study the impact of mutations and train ΔΔG predictors.…”

Engineered control of enzyme structural dynamics and function

“…High throughput mutational analyses may allow for more comprehensive interrogation and validation of these amino acid interaction networks. For example, the Matthews groups conducted a comprehensive mutational analysis of three InGPS enzymes (i.e., >5000 mutations) expressed in yeast cells in which the endogenous IGPS gene was deleted, following the deep mutational scan approach developed by the Bolon group .…”

ProtaBank: A repository for protein design and engineering data

“…This rapid generation of large sets of mutational data has enabled comprehensive mappings between protein sequence and function for properties such as stability, binding affinity, and catalytic activity . Deep mutational scanning approaches have been used to study protein fitness landscapes, discover new functional sites, and engineer proteins with new and improved properties . Many groups are now using these techniques to generate large amounts of protein engineering (PE) data—a trend that is expected to grow in the future.…”

“…[5][6][7] Deep mutational scanning approaches have been used to study protein fitness landscapes, discover new functional sites, and engineer proteins with new and improved properties. 8,9 Many groups are now using these techniques to generate large amounts of protein engineering (PE) data-a trend that is expected to grow in the future.…”