Biology subjects

Gilchrist, M.

Publications and source records attributed to Gilchrist, M..

2 recordsLinked to original sources

bioRxiv2017

Measuring Selection Across HIV Gag: Combining Physico-Chemistry and Population Genetics

We present physico-chemical based model grounded in population genetics. Our model predicts the stationary probability of observing an amino acid residue at a given site. Its predictions are based on the physico-chemical properties of the inferred optimal residue at that site and the sensitivity of the proteins functionality to deviation from the physico-chemical optimum at that site. We contextualize our physico-chemical model by comparing our model fit and parameters it to the more general, but less biologically meaningful entropy based metric: site sensitivity or 1/E. We show mathematically that our physico-chemical model is a more restricted form of the entropy model and how 1/E is proportional to the log-likelihood of a parameter-wise saturated model. Next, we fit both our physico-chemical and entropy models to sequences for subtype Cs Gag poly-protein in the LANL HIV database. Comparing our models site sensitivity parameters G' to 1/E we find they are highly correlated. We also compare the ability of G', 1/E, and other indirect measures of HIV fitness to empirical in vitro and in vivo measures. We find G' does a slightly better job predicting empirical fitness measures of in vivo viral escape time and in vitro spreading rates. While our predictive gain is modest, our model can be modified to test more complex or alternative biological hypotheses. More generally, because of its explicit biological formulation, our model can be easily extended to test for stabilizing vs. diversifying selection. We conjecture that our model could also be extended include epistasis in a more realistic manner than Ising models, while requiring many fewer parameters than Potts models.

evolutionary biology↗

bioRxiv2017

Population Genetics Based Phylogenetics Under Stabilizing Selection for an Optimal Amino Acid Sequence: A Nested Modeling Approach

We present a new phylogenetic approach SelAC (Selection on Amino acids and Codons), whose substitution rates are based on a nested model linking protein expression to population genetics. Unlike simpler codon models which assume a single substitution matrix for all sites, our model more realistically represents the evolution of protein coding DNA under the assumption of consistent, stabilizing selection using cost-benefit approach. This cost-benefit approach allows us generate a set of 20 optimal amino acid specific matrix families using just a handful of parameters and naturally links the strength of stabilizing selection to protein synthesis levels, which we can estimate. Using a yeast dataset of 100 orthologs for 6 taxa, we find SelAC fits the data much better than popular models by 104-105 AICc units. Our results indicate there is great potential for more accurate inference of phylogenetic trees and branch lengths from already existing data through the use of nested, mechanistic models. Additional parameters estimated by SelAC indicate that a large amount of non-phylogenetic, but biologically meaningful, information can be inferred from exisiting data. For example, SelAC prediction of gene specific protein synthesis rates correlates well with both empirical (r=0.33-0.48) and other theoretical predictions (r=0.45-0.64) for multiple yeast species. SelAC also provides estimates of the optimal amino acid at each site. Finally, because SelAC is a nested approach based on clearly stated biological assumptions, future modifications, such as including shifts in the optimal amino acid sequence within or across lineages, are possible.

evolutionary biology↗