The Evolutionary Rates of HCV Estimated with Subtype 1a and 1b Sequences over the ORF Length and in Different Genomic Regions

<div><p>Background</p><p>Considerable progress has been made in the HCV evolutionary analysis, since the software BEAST was released. However, prior information, especially the prior evolutionary rate, which plays a critical role in BEAST analysis, is always difficult to ascertain due to various uncertainties. Providing a proper prior HCV evolutionary rate is thus of great importance.</p><p>Methods/Results</p><p>176 full-length sequences of HCV subtype 1a and 144 of 1b were assembled by taking into consideration the balance of the sampling dates and the even dispersion in phylogenetic trees. According to the HCV genomic organization and biological functions, each dataset was partitioned into nine genomic regions and two routinely amplified regions. A uniform prior rate was applied to the BEAST analysis for each region and also the entire ORF. All the obtained posterior rates for 1a are of a magnitude of 10⁻³ substitutions/site/year and in a bell-shaped distribution. Significantly lower rates were estimated for 1b and some of the rate distribution curves resulted in a one-sided truncation, particularly under the exponential model. This indicates that some of the rates for subtype 1b are less accurate, so they were adjusted by including more sequences to improve the temporal structure.</p><p>Conclusion</p><p>Among the various HCV subtypes and genomic regions, the evolutionary patterns are dissimilar. Therefore, an applied estimation of the HCV epidemic history requires the proper selection of the rate priors, which should match the actual dataset so that they can fit for the subtype, the genomic region and even the length. By referencing the findings here, future evolutionary analysis of the HCV subtype 1a and 1b datasets may become more accurate and hence prove useful for tracing their patterns.</p></div

The median evolutionary rates and the tMRCAs estimated in the nine genomic regions and over the entire ORF of the subtype 1a and 1b datasets.

<p>Panels A, B, and C show the median evolutionary rates. Panels D, E, and F show the median tMRCAs. The blue columns represent the estimates for 1a. The red columns represent the estimates for 1b. The dash lines indicate the estimates for the entire ORF.</p

The violin plots of the posterior evolutionary rate estimated using the uniform (0, 0.01) rate prior in the nine genomic regions and over the entire ORF of the subtype 1a (A panel) and 1b (B panel) datasets.

<p>Combined with the GTR+I+Γ substitution model and Bayesian skyline coalesent model, the MCMC procedures were run under three clock models, exoponetial, lognormal, and strict, respectively, using BEAST. The vertical axis measures the substitution rate multiplied by 10⁻³ (substitution/site/year). The horizontal axis indicates the nine genomic regions and the entire ORF. The left three panels show the results for the 1a dataset. The right three panels show the results for the 1b dataset. In each panel, two violins are separated in a small case on the right, which indicate the rates estimated for the routinely amplified partial Core-E1 (P-C/E1) and partial NS5B (P-NS5B) regions.</p

The evolutionary rates and tMRCAs estimated for the 1a and 1b datasets in nine genomic regions and over ORF by root-to-tip regression.

¶<p>In the calendar year. <b>*</b> The evolutionary rate is negative and tMRCA locates in the future.</p

Root-to-tip regression to estimate the tMRCAs and clock rates.

<p>A simple linear regression of the root-to-tip genentic distances against the sampling dates was performed using the Path-o-gen software. The root was determined by maximizing the coefficent of determinant R². The vertical axis measures the genetic distances between the samples and the root while the horizontal axis scales the sampling dates (year). For subtype 1a (A), the mean evolutionary rate (the slope of regression line) is 9.05E-4 substitution/site/year and the tMRCA (the X-intercept) is located at 1941. For subtype 1b (B), the mean evolutionary rate is 4.82E-4 and the tMRCA is located at 1808.</p

Exploring movement and energy in human P-glycoprotein conformational rearrangement

<p>Human P-glycoprotein (P-gp), a kind of ATP-Binding Cassette transporter, can export a diverse variety of anti-cancer drugs out of the tumor cell. Its overexpression is one of the main reasons for the multidrug resistance (MDR) of tumor cells. It has been confirmed that during the substrate transport process, P-gp experiences a large-scale structural rearrangement from the inward- to outward-facing states. However, the mechanism of how the nucleotide-binding domains (NBDs) control the transmembrane domains (TMDs) to open towards the periplasm in the outward-facing state has not yet been fully characterized. Herein, targeted molecular dynamics simulations were performed to explore the conformational rearrangement of human P-gp. The results show that the allosteric process proceeds in a coupled way, and first the transition is driven by the NBDs, and then transmitted to the cytoplasmic parts of TMDs, finally to the periplasmic parts. The trajectories show that besides the translational motions, the NBDs undergo a rotation movement, which mainly occurs in <i>xy</i> plane and ensures the formation of the correct ATP-binding pockets. The analyses on the interaction energies between the six structure segments (cICLs) from the TMDs and NBDs reveal that their subtle energy differences play an important role in causing the periplasmic parts of the transmembrane helices to separate from each other in the established directions and in appropriate amplitudes. This conclusion can explain the two experimental phenomena about human P-gp in some extent. These studies have provided a detailed exploration into human P-gp rearrangement process and given an energy insight into the TMD reorientation during P-gp transition.</p

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNMMD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design

Identification of functionally key residues in maltose transporter with an elastic network model-based thermodynamic method

<p>Periplasmic binding protein-dependent maltose transport system (MBP-MalFGK₂) of <i>Escherichia coli</i>, an important member of the Adenosine triphosphate-binding cassette transporter superfamily, is in charge of the transportation of maltoses across cellular membrane. Studies have shown that this transport processes are activated by the binding of maltose and are accompanied by large-scale cooperative movements between different domains which are mediated by a network of important residues related to signal transduction and allosteric regulation. In this paper, the functionally crucial residues and long-range allosteric pathway of the regulation of the system by substrate were identified by utilising a coarse-grained thermodynamic method proposed by our group. The residues whose perturbations markedly change the binding free energy between maltoses and MBP-MalFGK₂ were considered to be key residues. In result, the key residues in 62 clusters distributed in different subdomains were identified successfully, and the results from our calculation are highly consistent with experimental and theoretical observations. Furthermore, we explored the long-range cooperation within the transporter. These studies will help us better understand the physical mechanism of the effects of the maltose on MBP-MalFGK₂ by long-range allosteric modulation.</p> <p></p

Structures of N43D mutant (blue) and N43D/S138A double mutant (white).

<p>Structures of N43D mutant (blue) and N43D/S138A double mutant (white).</p

Per-residue changes of inhibitor C34 in van der Waals (kcal/mol).

<p>Per-residue changes of inhibitor C34 in van der Waals (kcal/mol).</p