Contribution of Selection for Protein Folding Stability in Shaping the Patterns of Polymorphisms in Coding Regions

The patterns of polymorphisms in genomes are imprints of the evolutionary forces at play in nature. In particular, polymorphisms have been extensively used to infer the fitness effects of mutations and their dynamics of fixation. However, the role and contribution of molecular biophysics to these observations remain unclear. Here, we couple robust findings from protein biophysics, enzymatic flux theory, the selection against the cytotoxic effects of protein misfolding, and explicit population dynamics simulations in the polyclonal regime. First, we recapitulate results on the dynamics of clonal interference and on the shape of the DFE, thus providing them with a molecular and mechanistic foundation. Second, we predict that if evolution is indeed under the dynamic equilibrium of mutation–selection balance, the fraction of stabilizing and destabilizing mutations is almost equal among single-nucleotide polymorphisms segregating at high allele frequencies. This prediction is proven true for polymorphisms in the human coding region. Overall, our results show how selection for protein folding stability predominantly shapes the patterns of polymorphisms in coding regions

Contribution of Selection for Protein Folding Stability in Shaping the Patterns of Polymorphisms in Coding Regions

The patterns of polymorphisms in genomes are imprints of the evolutionary forces at play in nature. In particular, polymorphisms have been extensively used to infer the fitness effects of mutations and their dynamics of fixation. However, the role and contribution of molecular biophysics to these observations remain unclear. Here, we couple robust findings from protein biophysics, enzymatic flux theory, the selection against the cytotoxic effects of protein misfolding, and explicit population dynamics simulations in the polyclonal regime. First, we recapitulate results on the dynamics of clonal interference and on the shape of the DFE, thus providing them with a molecular and mechanistic foundation. Second, we predict that if evolution is indeed under the dynamic equilibrium of mutation–selection balance, the fraction of stabilizing and destabilizing mutations is almost equal among single-nucleotide polymorphisms segregating at high allele frequencies. This prediction is proven true for polymorphisms in the human coding region. Overall, our results show how selection for protein folding stability predominantly shapes the patterns of polymorphisms in coding regions

Highly Abundant Proteins Favor More Stable 3D Structures in Yeast

AbstractTo understand the variation of protein sequences in nature, we need to reckon with evolutionary constraints that are biophysical, cellular, and ecological. Here, we show that under the global selection against protein misfolding, there exists a scaling among protein folding stability, protein cellular abundance, and effective population size. The specific scaling implies that the several-orders-of-magnitude range of protein abundances in the cell should leave imprints on extant protein structures, a prediction that is supported by our structural analysis of the yeast proteome

The Influence of Selection for Protein Stability on dN/dS Estimations

Understanding the relative contributions of various evolutionary processes—purifying selection, neutral drift, and adaptation—is fundamental to evolutionary biology. A common metric to distinguish these processes is the ratio of nonsynonymous to synonymous substitutions (i.e., dN/dS) interpreted from the neutral theory as a null model. However, from biophysical considerations, mutations have non-negligible effects on the biophysical properties of proteins such as folding stability. In this work, we investigated how stability affects the rate of protein evolution in phylogenetic trees by using simulations that combine explicit protein sequences with associated stability changes. We first simulated myoglobin evolution in phylogenetic trees with a biophysically realistic approach that accounts for 3D structural information and estimates of changes in stability upon mutation. We then compared evolutionary rates inferred directly from simulation to those estimated using maximum-likelihood (ML) methods. We found that the dN/dS estimated by ML methods (ωML) is highly predictive of the per gene dN/dS inferred from the simulated phylogenetic trees. This agreement is strong in the regime of high stability where protein evolution is neutral. At low folding stabilities and under mutation-selection balance, we observe deviations from neutrality (per gene dN/dS > 1 and dN/dS 1. Altogether, we show how protein biophysics affects the dN/dS estimations and its subsequent interpretation. These results are important for improving the current approaches for detecting positive selection

Multiscale approaches for studying energy transduction in dynein

Cytoplasmic dynein is an important motor that drives all minus-end directed movement along microtubules. Dynein is a complex motor whose processive motion is driven by ATP-hydrolysis. Dynein's run length has been measured to be several millimetres with typical velocities in the order of a few nanometres per second. Therefore, the average time between steps is a fraction of a second. When this time scale is compared with typical time scales for protein side chain and backbone movements (~10−9 s and ~10−5 s, respectively), it becomes clear that a multi-timescale modelling approach is required to understand energy transduction in this protein. Here, we review recent efforts to use computational and mathematical modelling to understand various aspects of dynein's chemomechanical cycle. First, we describe a structural model of dynein's motor unit showing a heptameric organization of the motor subunits. Second, we describe our molecular dynamics simulations of the motor unit that are used to investigate the dynamics of the various motor domains. Third, we present a kinetic model of the coordination between the two dynein heads. Lastly, we investigate the various potential geometries of the dimer during its hydrolytic and stepping cycle

Computational Studies Reveal Phosphorylation-dependent Changes in the Unstructured R Domain of CFTR

The Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a cAMP dependent chloride channel that is mutated in cystic fibrosis, an inherited disease of high morbidity and mortality. The phosphorylation of its ∼200 amino acid R domain by protein kinase A is obligatory for channel gating under normal conditions. The R domain contains more than ten PKA phosphorylation sites. No individual site is essential but phosphorylation of increasing numbers of sites enables progressively greater channel activity. In spite of numerous studies of the role of the R domain in CFTR regulation, its mechanism of action remains largely unknown. This is because neither its structure nor its interactions with other parts of CFTR have been completely elucidated. Studies have shown that the R domain lacks well-defined secondary structural elements and is an intrinsically disordered region of the channel protein. Here, we have analyzed the disorder pattern and employed computational methods to explore low energy conformations of the R domain. Specific disorder and secondary structure patterns detected suggest the presence of Molecular Recognition Elements (MoREs) that may mediate phosphorylation regulated intra- and inter-domain interactions. Simulations were performed to generate an ensemble of accessible R domain conformations. Although the calculated structures may represent more compact conformers than occur in vivo, their secondary structure propensities are consistent with predictions and published experimental data. Equilibrium simulations of a mimic of a phosphorylated R domain showed that it exhibited an increased radius of gyration. In one possible interpretation of these findings, by changing its size, the globally unstructured R domain may act as an entropic spring to perturb the packing of membrane-spanning sequences that constitute the ion permeability pathway and thereby activate channel gating

Structural Basis for μ-Opioid Receptor Binding and Activation

Opioids that stimulate the μ-opioid receptor (MOR1) are the most frequently prescribed and effective analgesics. Here we present a structural model of MOR1. Molecular dynamics simulations show a ligand-dependent increase in the conformational flexibility of the third intracellular loop that couples with the G-protein complex. These simulations likewise identified residues that form frequent contacts with ligands. We validated the binding residues using site-directed mutagenesis coupled with radioligand binding and functional assays. The model was used to blindly screen a library of ~1.2 million compounds. From the thirty-four compounds predicted to be strong binders, the top three candidates were examined using biochemical assays. One compound showed high efficacy and potency. Post hoc testing revealed this compound to be nalmefene, a potent clinically used antagonist, thus further validating the model. In summary, the MOR1 model provides a tool for elucidating the structural mechanism of ligand-initiated cell signaling and screening for novel analgesics

Protein Biophysics Explains Why Highly Abundant Proteins Evolve Slowly

The consistent observation across all kingdoms of life that highly abundant proteins evolve slowly demonstrates that cellular abundance is a key determinant of protein evolutionary rate. However, other empirical findings, such as the broad distribution of evolutionary rates, suggest that additional variables determine the rate of protein evolution. Here, we report that under the global selection against the cytotoxic effects of misfolded proteins, folding stability (ΔG), simultaneous with abundance, is a causal variable of evolutionary rate. Using both theoretical analysis and multiscale simulations, we demonstrate that the anticorrelation between the premutation ΔG and the arising mutational effect (ΔΔG), purely biophysical in origin, is a necessary requirement for abundance–evolutionary rate covariation. Additionally, we predict and demonstrate in bacteria that the strength of abundance–evolutionary rate correlation depends on the divergence time separating reference genomes. Altogether, these results highlight the intrinsic role of protein biophysics in the emerging universal patterns of molecular evolution