Automated Author ProfilePabis, Anna
Pabis, Anna
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.8 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
<b>Abstract</b><br/>Genetic variation among orthologous proteins can cause cryptic phenotypic properties that only manifest in changing environments. Such variation may impact the evolvability of proteins, but the underlying molecular basis remains unclear. Here, we performed comparative directed evolution of four orthologous metallo-β-lactamases toward a new function and found that different starting genotypes evolved to distinct evolutionary outcomes. Despite a low initial fitness, one ortholog reached a significantly higher fitness plateau than its counterparts, via increasing catalytic activity. By contrast, the ortholog with the highest initial activity evolved to a less-optimal and phenotypically distinct outcome through changes in expression, oligomerization and activity. We show how cryptic molecular properties and conformational variation of active site residues in the initial genotypes cause epistasis, that could lead to distinct evolutionary outcomes. Our work highlights th e importance of understanding the molecular details that connect genetic variation to protein function to improve the prediction of protein evolution.
Authors
- Baier, Florian ;
- Hong, Nansook ;
- Yang, Gloria ;
- Pabis, Anna ;
- Miton, Charlotte M. ;
- Barrozo, Alexandre ;
- Carr, Paul D. ;
- Kamerlin, Shina C. L. ;
- Jackson, Colin J. ;
- Tokuriki, Nobuhiko
No description available
Authors
- Pabis, Anna