Ose, N. J. | Search bioRxiv

bioRxiv2023

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

biophysics↗

bioRxiv2021

Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor belong to any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for such mutations. In this study, we present an analysis of 144 human enzymes containing 591 pathogenic missense variants, in which allosteric dynamic coupling of mutated positions with known active sites provides insights into a primary biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of {beta}-Glucocerebrosidase (GCase), which contains 94 Gaucher disease-associated missense variants located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling due to the presence of missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease. Author SummaryGenetic diseases occur when mutations to a particular gene cause a gain/loss in function of the related protein. Although several methods based on conservation and protein biochemistry exist to predict which genetic mutations may impact function, many disease causing changes remain unexplained by these metrics. In this study, we propose an explanation for these genetic changes may cause disease. In order to function, important regions of a protein must be able to exhibit collective motion. Through computer simulations, we observed that changing even a single amino acid within a protein can change the protein motion. Notably, disease causing genetic changes tend to alter the motion of regions which are critically important to protein function, even the mutations are far from these critical regions. In addition, we examined the degree that two amino acids within a protein may "couple" to one another, meaning the degree to which motion in one amino acid will affect the other. We found that amino acids which are highly coupled to the active site of a protein are more likely to result in disease if mutated, thereby offering a new tool for predicting genetic disease which incorporates internal protein dynamics.

biophysics↗