MBAR-enhanced lattice Monte Carlo simulation of the effect of helices on membrane protein aggregation

Abstract

We study the effect of helical structure on the aggregation of proteins using a simplified lattice protein model with an implicit membrane environment. A recently proposed Monte Carlo approach, which exploits the proven statistical optimality of the MBAR estimator in order to improve simulation efficiency, was used. The results show that with both two and four proteins present, the tendency to aggregate is strongly expedited by the presence of amphipathic helix (APH), whereas a transmembrane helix (TMH) slightly disfavours aggregation.When four protein molecules are present, partially aggregated states (dimers and trimers) were more common when the APH was present, compared with the cases where no helices or only the TMH is present.Keywords: lattice Monte Carlo, parallel tempering, multicanonical sampling, protein aggregation, twin-arginine translocation Background and IntroductionProteins are macromolecules that are essential to the functioning of living organisms. The primary structure of a protein consists of a chain of amino acids that is coded by genes. Typically, upon synthesis on ribosomes, the extended chain must fold into a specific three-dimensional structure called the by one or more beads, coarse-grained models that balance accuracy and compu-2 tational efficiency, to atomistic models that are more accurate and detailed but too computationally prohibitive to study the full assembly process of interest.On the other hand, computational approaches can be applied to models of various resolutions, although certain methods may be more suitable to a particular of states from multiple equilibrium simulations using the MBAR estimator, and so there is a wide choice of methods that can be used in place of PT and MUCA as are used here.We emphasize that the TatA aggregation in the Tat mechanism serves as a motivation to the current lattice model, and that our simulation results are not

Special Issue: Predictive multiscale materials modeling

“…Other challenges included modeling across different but coupled time and length scales incorporating multiple physics, developing probabilistic hierarchical and multiscale models, scalable uncertainty quantification, effective coarse graining [6], relative entropy and variational inference methods suitable for correlated data, dynamics and time series [4,7,8], efficient Lattice Monte Carlo, parallel tempering, and multicanonical sampling in molecular simulations [9], and exploring high-dimensional structure relations.…”