Caetano, D. | Search bioRxiv

bioRxiv2021

Adsorption of Lysozyme Into a Charged Confining Pore

Several applications arise from the confinement of proteins on surfaces since their stability and biological activity are enhanced. It is also known that the way a protein adsorbs on the surface is important for its biological function since its active sites should not be obstructed. In this study, the adsorption properties of hen egg-white Lysozyme, HEWL, into a negatively charged silica pore is examined employing a coarse-grained model and constant-pH Monte Carlo simulations. The role of electrostatic interactions is taken into account when including the Debye-HAijckel potentials into the C structure-based model. We evaluate the effects of pH, salt concentration, and pore radius on the protein preferential orientation and spatial distribution of its residues regarding the pore surface. By mapping the residues that stay closer to the pore surface, we find the increase of pH leads to orientational changes of the adsorbed protein when the solution pH gets closer to the HEWL isoelectric point. At these conditions, the pKa shift of these important residues caused by the adsorption into the charged confining surface results in a HEWL charge distribution that stabilizes the adsorption in the observed protein orientation. We compare our observations to the results of pKa shift for HEWL available in the literature and to some experimental data.

biophysics↗

bioRxiv2018

Hidden state models improve the adequacy of state-dependent diversification approaches using empirical trees, including biogeographical models

The state-dependent speciation and extinction models (SSE) have recently been criticized due to their high rates of \"false positive\" results and many researchers have advocated avoiding SSE models in favor of other \"non-parametric\" or \"semi-parametric\" approaches. The hidden Markov modeling (HMM) approach provides a partial solution to the issues of model adequacy detected with SSE models. The inclusion of \"hidden states\" can account for rate heterogeneity observed in empirical phylogenies and allows detection of true signals of state-dependent diversification or diversification shifts independent of the trait of interest. However, the adoption of HMM into other classes of SSE models has been hampered by the interpretational challenges of what exactly a \"hidden state\" represents, which we clarify herein. We show that HMM models in combination with a model-averaging approach naturally account for hidden traits when examining the meaningful impact of a suspected \"driver\" of diversification. We also extend the HMM to the geographic state-dependent speciation and extinction (GeoSSE) model. We test the efficacy of our \"GeoHiSSE\" extension with both simulations and an empirical data set. On the whole, we show that hidden states are a general framework that can generally distinguish heterogeneous effects of diversification attributed to a focal character.

evolutionary biology↗