Compensated pathogenic deviations

Abstract

Deleterious or ‘disease-associated’ mutations are mutations that lead to disease with high phenotype penetrance: they are inherited in a simple Mendelian manner, or, in the case of cancer, accumulate in somatic cells leading directly to disease. However, in some cases, the amino acid that is substituted resulting in disease is the wild-type native residue in the functionally equivalent protein in another species. Such examples are known as ‘compensated pathogenic deviations’ (CPDs) because, somewhere in the second species, there must be compensatory mutations that allow the protein to function normally despite having a residue which would cause disease in the first species. Depending on the nature of the mutations, compensation can occur in the same protein, or in a different protein with which it interacts. In principle, compensation can be achieved by a single mutation (most probably structurally close to the CPD), or by the cumulative effect of several mutations. Although it is clear that these effects occur in proteins, compensatory mutations are also important in RNA potentially having an impact on disease. As a much simpler molecule, RNA provides an interesting model for understanding mechanisms of compensatory effects, both by looking at naturally occurring RNA molecules and as a means of computational simulation. This review surveys the rather limited literature that has explored these effects. Understanding the nature of CPDs is important in understanding traversal along fitness landscape valleys in evolution. It could also have applications in treating diseases that result from such mutations.

Defective recognition of LC3B by mutant SQSTM1/p62 implicates impairment of autophagy as a pathogenic mechanism in ALS-FTLD

“…However it is not unusual for a deleterious missense mutation in one species to be found as the wild-type residue in orthologs of other species without effecting on the fitness of the latter; this can occur as a result of the phenomenon termed ‘Compensated Pathogenic Deviations'. 29 Our functional studies are therefore a reminder of the caveats of relying on prediction programmes alone to establish the pathogenicity of variants of unknown clinical significance. It will be of particular importance in the future to extend our studies to other ALS-associated LIR mutations of SQSTM1 similarly suggested to be benign.…”

Enhancing missense variant pathogenicity prediction with protein language models using VariPred

“…In the study of 3Cnet, the artificial pathogenic-like variants were generated simply by considering the amino acid frequency and the number of gaps. However, a good conservation scoring scheme depends on multiple components, of which the most important include amino acid frequency, residue similarity (biophysical properties), sequence similarity (considering sequence redundancy and MSA depth), the number of gaps in the MSA, and the concept of ‘compensated pathogenic mutations’ (CPDs), which refers to mutations occurring in different species that are tolerated because of compensating mutations 31 . Indeed, it may be possible to exploit the LLR output from ESM-1b to suggest compensatory mutations and use the final VariPred output to evaluate their effect.…”

VariPred: Enhancing Pathogenicity Prediction of Missense Variants Using Protein Language Models

“…In the study of 3Cnet, the artificial pathogenic-like variants were generated simply by considering the amino acid frequency and the number of gaps. However, a good conservation scoring scheme depends on multiple components, of which the most important include amino acid frequency, residue similarity (biophysical properties), sequence similarity (considering sequence redundancy and MSA depth), the number of gaps in the MSA, and the concept of ‘compensated pathogenic mutations’ (CPDs), which refers to mutations occurring in different species that are tolerated because of compensating mutations [39]. Therefore, in the future, it may be worth investigating whether such a combination of data augmentation and synthetic data strategies can further improve the performance of VariPred.…”