Amir Shahmoradi

Predicting Evolutionary Site Variability from Structure in Viral Proteins: Buriedness, Packing, Flexibility, and Design

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on nine non-homologous viral protein structures and from variation in homologous variants of those proteins, where they were available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1–0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than the more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

Short versus long gamma-ray bursts: a comprehensive study of energetics and prompt gamma-ray correlations

We present the results of a comprehensive study of the luminosity function, energetics, prompt gamma-ray correlations, and classification methodology of short-hard and long-soft GRBs (SGRBs and LGRBs), based on observational data in the largest catalog of GRBs available to this date: BATSE catalog of 2702 GRBs. We find that: 1. The least-biased classification method of GRBs into short and long, solely based on prompt-emission properties, appears to be the ratio of the observed spectral peak energy to the observed duration (

Numerical simulation of the impact of polymer rheology on polymer injectivity using a multilevel local grid refinement method

R=E_p/T_{90}

Intermediate divergence levels maximize the strength of structure-sequence correlations in enzymes and viral proteins.

) with the dividing line at

Dissecting the roles of local packing density and longer‐range effects in protein sequence evolution

R\simeq50[keV~s^{-1}]

Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for pancreatic cancer.

. 2. Once data is carefully corrected for the effects of the detection threshold of gamma-ray instruments, the population distribution of SGRBs and LGRBs can be individually well described as multivariate log-normal distribution in the

A Multivariate Fit Luminosity Function and World Model for Long Gamma-Ray Bursts

4

Dissecting the relationship between protein structure and sequence variation

--dimensional space of the isotropic peak gamma-ray luminosity, total isotropic gamma-ray emission, the intrinsic spectral peak energy, and the intrinsic duration. 3. Relatively large fractions of SGRBs and LGRBs with moderate-to-low spectral peak energies have been missed by BATSE detectors. 4. Relatively strong and highly significant intrinsic hardness--brightness and duration--brightness correlations likely exist in both populations of SGRBs and LGRBs, once data is corrected for selection effects. The strengths of these correlations are very similar in both populations, implying similar mechanisms at work in both GRB classes, leading to the emergence of these prompt gamma-ray correlations.

Gamma-Ray bursts: Energetics and Prompt Correlations

Polymer injectivity is an important factor for evaluating the project economics of chemical flood, which is highly related to the polymer viscosity. Because the flow rate varies rapidly near injectors and significantly changes the polymer viscosity due to the non-Newtonian rheological behavior, the polymer viscosity near the wellbore is difficult to estimate accurately with the practical gridblock size in reservoir simulation. To reduce the impact of polymer rheology upon chemical EOR simulations, we used an efficient multilevel local grid refinement (LGR) method that provides a higher resolution of the flows in the near-wellbore region. An efficient numerical scheme was proposed to accurately solve the pressure equation and concentration equations on the multilevel grid for both homogeneous and heterogeneous reservoir cases. The block list and connections of the multilevel grid are generated via an efficient and extensible algorithm. Field case simulation results indicate that the proposed LGR is consistent with the analytical injectivity model and achieves the closest results to the full grid refinement, which considerably improves the accuracy of solutions compared with the original grid. In addition, the method was validated by comparing it with the LGR module of CMG_STARS. Besides polymer injectivity calculations, the LGR method is applicable for other problems in need of near-wellbore treatment, such as fractures near wells.

A Cosmological Discriminator Designed to Avoid Selection Bias

Structural properties such as solvent accessibility and contact number predict site‐specific sequence variability in many proteins. However, the strength and significance of these structure–sequence relationships vary widely among different proteins, with absolute correlation strengths ranging from 0 to 0.8. In particular, two recent works have made contradictory observations. Yeh et al. (Mol. Biol. Evol. 31:135–139, 2014) found that both relative solvent accessibility (RSA) and weighted contact number (WCN) are good predictors of sitewise evolutionary rate in enzymes, with WCN clearly out‐performing RSA. Shahmoradi et al. (J. Mol. Evol. 79:130–142, 2014) considered these same predictors (as well as others) in viral proteins and found much weaker correlations and no clear advantage of WCN over RSA. Because these two studies had substantial methodological differences, however, a direct comparison of their results is not possible. Here, we reanalyze the datasets of the two studies with one uniform analysis pipeline, and we find that many apparent discrepancies between the two analyses can be attributed to the extent of sequence divergence in individual alignments. Specifically, the alignments of the enzyme dataset are much more diverged than those of the virus dataset, and proteins with higher divergence exhibit, on average, stronger structure–sequence correlations. However, the highest structure–sequence correlations are observed at intermediate divergence levels, where both highly conserved and highly variable sites are present in the same alignment.