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Research Article
Protein engineering in the big data era: harnessing near-redundant structural data
Samuel Coulbourn Flores
Stockholm University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3869-8147
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This work is licensed under a CC BY 4.0 License. Read Full License
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- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Motivation: Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each.
Results: . We improve increase precision and Positive Predictive Value, and decrease Root Mean Square Error and higher Positive Predictive Value, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.
Availability: Public web server at biodesign.scilifelab.se
Keywords
protein-protein interactions, homology, affinity, complexes, biologic design, mutation, protein engineering
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This is a preprint. It has not completed peer review.
Research Article
Protein engineering in the big data era: harnessing near-redundant structural data
Samuel Coulbourn Flores
Stockholm University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3869-8147
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 2
Posted 18 Feb, 2021
No community comments so far
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Motivation: Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each.
Results: . We improve increase precision and Positive Predictive Value, and decrease Root Mean Square Error and higher Positive Predictive Value, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.
Availability: Public web server at biodesign.scilifelab.se
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.