Protein dynamics provide mechanistic insights about epistasis among common missense polymorphisms.

Sequencing of the protein coding genome has revealed many different missense mutations of human proteins and different population frequencies of corresponding haplotypes, which consist of different sets of those mutations. Here, we present evidence for pairwise intramolecular epistasis (i.e., nonadditive interactions) between many such mutations through an analysis of protein dynamics. We suggest that functional compensation for conserving protein dynamics is a likely evolutionary mechanism that maintains high-frequency mutations that are individually nonneutral but epistatically compensating within proteins. This analysis is the first of its type to look at human proteins with specific high population frequency mutations and examine the relationship between mutations that make up that observed high-frequency protein haplotype. Importantly, protein dynamics revealed a separation between high and low frequency haplotypes within a target protein cytochrome P450 2A7, with the high-frequency haplotypes showing behavior closer to the wild-type protein. Common protein haplotypes containing two mutations display dynamic compensation in which one mutation can correct for the dynamic effects of the other. We also utilize a dynamics-based metric, EpiScore, that evaluates the epistatic interactions and allows us to see dynamic compensation within many other proteins.

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 fall in 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 pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of ß-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are 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 caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.

The selfish herd: Noise effects in Local Crowded Horizon and Voronoi models.

Randomness or noise in the motion of herding animals is more important than commonly assumed. We show through simulations that noise promotes the compactness of a herd by facilitating the breakup of small clusters in favor of a more compact whole, reducing the mean median distances to center (MMDC) for the herd. Adding noise to movement models can also decrease risk from predators emerging locally by reducing Domains of Danger (DODs); we specifically show that adding noise to the Voronoi (V) movement model gives favorable DOD results comparable to that of the Local Crowded Horizon (LCH) model. In addition, we show that adding noise to these models can significantly reduce risk from predators emerging outside the herding area through head-to-head competition in mixed herds, with effects that can be larger than the choice of model itself. Our results are consistent with recent observational studies in several different animal populations that suggest random motion plays a significant role in the movement of individuals within these groups. Because of its significance, noise must be considered whenever model effectiveness is discussed or comparisons between movement models are made.

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.