Correction to: A statistical framework for analyzing deep mutational scanning data

Rubin, Alan F.; Gelman, Hannah; Lucas, Nathan J.; Bajjalieh, Sandra M.; Papenfuss, Anthony T.; Speed, Terence P.; Fowler, Douglas M.

doi:10.1186/s13059-018-1391-7

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each sequence the frequencies were calculated for all sorts, and the log ratio of the binding frequency to the expression frequency was used for analysis. For SSM sorts, the ratio of mutant counts from a binding sort to the expression sort was compared to that of the wild type sequence, and the log difference was defined as an enrichment factor 46 .…”

Section: Deep Sequencing and Analysismentioning

confidence: 99%

Small-molecule binding and sensing with a designed protein family

Lee,

Pellock,

Norn

et al. 2023

Preprint

View full text Add to dashboard Cite

Despite transformative advances in protein design with deep learning, the design of small-molecule–binding proteins and sensors for arbitrary ligands remains a grand challenge. Here we combine deep learning and physics-based methods to generate a family of proteins with diverse and designable pocket geometries, which we employ to computationally design binders for six chemically and structurally distinct small-molecule targets. Biophysical characterization of the designed binders revealed nanomolar to low micromolar binding affinities and atomic-level design accuracy. The bound ligands are exposed at one edge of the binding pocket, enabling thede novodesign of chemically induced dimerization (CID) systems; we take advantage of this to create a biosensor with nanomolar sensitivity for cortisol. Our approach provides a general method to design proteins that bind and sense small molecules for a wide range of analytical, environmental, and biomedical applications.

show abstract

Section: Deep Sequencing and Analysismentioning

confidence: 99%

Small-molecule binding and sensing with a designed protein family

Lee,

Pellock,

Norn

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations

Mehlhoff

Ostermeier

2023

Molecular Biology and Evolution

View full text Add to dashboard Cite

Mutations can have deleterious fitness effects when they decrease protein specific activity or decrease active protein abundance. Mutations will also be deleterious when they cause misfolding or misinteractions that are toxic to the cell (i.e., independent of whether the mutations affect specific activity and abundance). The extent to which protein evolution is shaped by these and other collateral fitness effects is unclear in part because little is known of their frequency and magnitude. Using deep mutational scanning (DMS), we previously found at least 42% of missense mutations in the TEM-1 β-lactamase antibiotic resistance gene cause deleterious collateral fitness effects. Here, we used DMS to comprehensively determine the collateral fitness effects of missense mutations in three genes encoding the antibiotic resistance proteins New Delhi metallo-β-lactamase (NDM-1), chloramphenicol acetyltransferase I (CAT-I), and 2”-aminoglycoside nucleotidyltransferase (AadB). AadB (20%), CAT-I (0.9%), and NDM-1 (0.2%) were less susceptible to deleterious collateral fitness effects than TEM-1 (42%) indicating that genes have different propensities for these effects. As was observed with TEM-1, all the studied deleterious aadB mutants increased aggregation. However, aggregation did not correlate with collateral fitness effects for many of the deleterious mutants of CAT-I and NDM-1. Select deleterious mutants caused unexpected phenotypes to emerge. The introduction of internal start codons in CAT-1 caused loss of the episome and a mutation in aadB made its cognate antibiotic essential for growth. Our study illustrates how the complexity of the cell provides a rich environment for collateral fitness effects and new phenotypes to emerge.

show abstract

Correction to: A statistical framework for analyzing deep mutational scanning data

Cited by 2 publications

References 2 publications

Small-molecule binding and sensing with a designed protein family

Small-molecule binding and sensing with a designed protein family

Genes Vary Greatly in Their Propensity for Collateral Fitness Effects of Mutations

Contact Info

Product

Resources

About