On the linkage of thermodynamics and pathogenicity

Pandey, Preeti; Ghimire, Sanjeev; Wu, Bohua; Alexov, Emil

doi:10.1016/j.sbi.2023.102572

Cited by 4 publications

(11 citation statements)

References 56 publications

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“…Furthermore, the work indicated that the leading algorithms for predicting folding and binding free energy changes caused by mutations perform differently in cases of SNVs and non-SNVs. This is an important observation since there is a strong linkage between the change in folding and binding free energies and the probability of mutation being pathogenic [58]. Overall, PCC is better for non-SNVs, which points out that the methods may not be accurate in ranking SNV cases.…”

Section: Discussionmentioning

confidence: 97%

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Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

Pandey,

Panday,

Rimal

et al. 2023

IJMS

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The development of methods and algorithms to predict the effect of mutations on protein stability, protein–protein interaction, 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 require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (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 is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.

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Section: Discussionmentioning

confidence: 97%

“…This observation should be considered when asserting the effect of SNVs, both benign and pathogenic, on protein stability and macromolecular interactions. Especially since there is a strong linkage between thermodynamics and pathogenicity [58].…”

Section: Discussionmentioning

confidence: 99%

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

Pandey,

Panday,

Rimal

et al. 2023

IJMS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, the work indicated that the leading algorithms for predicting folding and binding free energy changes caused by mutations perform differently in cases of SNVs and non-SNVs. This is an important observation since there is a strong linkage between the change of the folding and binding free energies and the probability of mutation being pathogenic [58]. Overall PCC is better for non-SNVs, which points out that the methods may not be accurate in ranking SNV cases.…”

Section: Discussionmentioning

confidence: 98%

“…This observation should be considered in asserting the effect of SNVs, both benign and pathogenic, on protein stability and macromolecular interactions. Especially since there is a strong linkage between thermodynamics and pathogenicity [58].…”

Section: Discussionmentioning

confidence: 99%

Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations

Pandey,

Rimal

et al. 2023

Preprint

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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.

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“…To conduct our study on the correlation between changes in protein folding free energy and the likelihood of a mutation being pathogenic, we have used the database of monogenic disorders (Pandey et al 2023).…”

Section: Database Of Monogenic Disorders (Mogedo)mentioning

confidence: 99%

Most monogenic disorders are caused by mutations altering protein folding free energy

Pandey,

Alexov

2023

Preprint

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Revealing the molecular effect that pathogenic missense mutations cause on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast, targeted drug development at a low cost. Here, we report a database of monogenic disorders (MOGEDO), which includes 768 proteins, the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in-silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG but not to make protein over-stable since over-stable protein may be dysfunctional. We demonstrate that using ΔΔG and predicted solvent exposure of the mutation site; one can develop a predictive method that distinguishes pathogenic from benign mutation with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), and the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.

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On the linkage of thermodynamics and pathogenicity

Cited by 4 publications

References 56 publications

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

Predicting the Effect of Mutations on Protein Stability and Binding: Assessment of Leading Algorithms Performance and Databases Content with Respect to Types of Mutations

Most monogenic disorders are caused by mutations altering protein folding free energy

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