PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations
Version 1
: Received: 1 June 2023 / Approved: 2 June 2023 / Online: 2 June 2023 (11:58:21 CEST)
How to cite:
Pandey, P.; Pandey, S.; Rimal, P.; Ancona, N.; Alexov, E. Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations. Preprints2023, 2023060199. https://doi.org/10.20944/preprints202306.0199.v1
Pandey, P.; Pandey, S.; Rimal, P.; Ancona, N.; Alexov, E. Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations. Preprints 2023, 2023060199. https://doi.org/10.20944/preprints202306.0199.v1
Pandey, P.; Pandey, S.; Rimal, P.; Ancona, N.; Alexov, E. Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations. Preprints2023, 2023060199. https://doi.org/10.20944/preprints202306.0199.v1
APA Style
Pandey, P., Pandey, S., Rimal, P., Ancona, N., & Alexov, E. (2023). Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations. Preprints. https://doi.org/10.20944/preprints202306.0199.v1
Chicago/Turabian Style
Pandey, P., Nicolas Ancona and Emil Alexov. 2023 "Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations" Preprints. https://doi.org/10.20944/preprints202306.0199.v1
Abstract
Development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods are either methods with adjustable parameters or machine learning algorithms, both requiring a database of experimentally measured folding and binding free energy changes. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residue on the above-mentioned thermodynamics characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variant (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases, may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases and the distribution of the free energy changes are not the same. Furthermore, the Pearson correlation coefficients (PCCs) obtained on cases involving SNVs are less impressive than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. All methods are found to underestimate the energy changes by roughly a factor of 2.
Keywords
mutations; folding free energy change; binding free energy change; single nucleotide variant
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
