Factors That Control the Force Needed to Unfold a Membrane Protein in Silico Depend on the Mode of Denaturation.

Single-molecule force spectroscopy methods, such as AFM and magnetic tweezers, have proved extremely beneficial in elucidating folding pathways for soluble and membrane proteins. To identify factors that determine the force rupture levels in force-induced membrane protein unfolding, we applied our near-atomic-level Upside molecular dynamics package to study the vertical and lateral pulling of bacteriorhodopsin (bR) and GlpG, respectively. With our algorithm, we were able to selectively alter the magnitudes of individual interaction terms and identify that, for vertical pulling, hydrogen bond strength had the strongest effect, whereas other non-bonded protein and membrane-protein interactions had only moderate influences, except for the extraction of the last helix where the membrane-protein interactions had a stronger influence. The up-down topology of the transmembrane helices caused helices to be pulled out as pairs. The rate-limiting rupture event often was the loss of H-bonds and the ejection of the first helix, which then propagated tension to the second helix, which rapidly exited the bilayer. The pulling of the charged linkers across the membrane had minimal influence, as did changing the bilayer thickness. For the lateral pulling of GlpG, the rate-limiting rupture corresponded to the separation of the helices within the membrane, with the H-bonds generally being broken only afterward. Beyond providing a detailed picture of the rupture events, our study emphasizes that the pulling mode greatly affects the factors that determine the forces needed to unfold a membrane protein.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes.

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

Lipid bilayer induces contraction of the denatured state ensemble of a helical-bundle membrane protein.

Defining the denatured state ensemble (DSE) and disordered proteins is essential to understanding folding, chaperone action, degradation, and translocation. As compared with water-soluble proteins, the DSE of membrane proteins is much less characterized. Here, we measure the DSE of the helical membrane protein GlpG of Escherichia coli (E. coli) in native-like lipid bilayers. The DSE was obtained using our steric trapping method, which couples denaturation of doubly biotinylated GlpG to binding of two streptavidin molecules. The helices and loops are probed using limited proteolysis and mass spectrometry, while the dimensions are determined using our paramagnetic biotin derivative and double electron-electron resonance spectroscopy. These data, along with our Upside simulations, identify the DSE as being highly dynamic, involving the topology changes and unfolding of some of the transmembrane (TM) helices. The DSE is expanded relative to the native state but only to 15 to 75% of the fully expanded condition. The degree of expansion depends on the local protein packing and the lipid composition. E. coli's lipid bilayer promotes the association of TM helices in the DSE and, probably in general, facilitates interhelical interactions. This tendency may be the outcome of a general lipophobic effect of proteins within the cell membranes.

Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours.

An ongoing challenge in protein chemistry is to identify the underlying interaction energies that capture protein dynamics. The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed force fields are typically well-parameterized, obtaining a significant fraction of possible accuracy. We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of error than the level of detail in the force field. To address parameterization of coarse-grained force fields, we use the contrastive divergence technique from machine learning to train from simulations of 450 proteins. In our procedure, the computational efficiency of the model enables high accuracy through the precise tuning of the Boltzmann ensemble. This method is applied to our recently developed Upside model, where the free energy for side chains is rapidly calculated at every time-step, allowing for a smooth energy landscape without steric rattling of the side chains. After this contrastive divergence training, the model is able to de novo fold proteins up to 100 residues on a single core in days. This improved Upside model provides a starting point both for investigation of folding dynamics and as an inexpensive Bayesian prior for protein physics that can be integrated with additional experimental or bioinformatic data.

Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics.

To address the large gap between time scales that can be easily reached by molecular simulations and those required to understand protein dynamics, we present a rapid self-consistent approximation of the side chain free energy at every integration step. In analogy with the adiabatic Born-Oppenheimer approximation for electronic structure, the protein backbone dynamics are simulated as preceding according to the dictates of the free energy of an instantaneously-equilibrated side chain potential. The side chain free energy is computed on the fly, allowing the protein backbone dynamics to traverse a greatly smoothed energetic landscape. This computation results in extremely rapid equilibration and sampling of the Boltzmann distribution. Our method, termed Upside, employs a reduced model involving the three backbone atoms, along with the carbonyl oxygen and amide proton, and a single (oriented) side chain bead having multiple locations reflecting the conformational diversity of the side chain's rotameric states. We also introduce a novel, maximum-likelihood method to parameterize the side chain interactions using protein structures. We demonstrate state-of-the-art accuracy for predicting χ1 rotamer states while consuming only milliseconds of CPU time. Our method enables rapidly equilibrating coarse-grained simulations that can nonetheless contain significant molecular detail. We also show that the resulting free energies of the side chains are sufficiently accurate for de novo folding of some proteins.

A simple approach to spectrally resolved fluorescence and bright field microscopy over select regions of interest.

A standard wide field inverted microscope was converted to a spatially selective spectrally resolved microscope through the addition of a polarizing beam splitter, a pair of polarizers, an amplitude-mode liquid crystal-spatial light modulator, and a USB spectrometer. The instrument is capable of simultaneously imaging and acquiring spectra over user defined regions of interest. The microscope can also be operated in a bright-field mode to acquire absorption spectra of micron scale objects. The utility of the instrument is demonstrated on three different samples. First, the instrument is used to resolve three differently labeled fluorescent beads in vitro. Second, the instrument is used to recover time dependent bleaching dynamics that have distinct spectral changes in the cyanobacteria, Synechococcus leopoliensis UTEX 625. Lastly, the technique is used to acquire the absorption spectra of CH3NH3PbBr3 perovskites and measure differences between nanocrystal films and micron scale crystals.

HERV-K-specific T cells eliminate diverse HIV-1/2 and SIV primary isolates.

The genetic diversity of HIV-1 represents a major challenge in vaccine development. In this study, we establish a rationale for eliminating HIV-1-infected cells by targeting cellular immune responses against stable human endogenous retroviral (HERV) antigens. HERV DNA sequences in the human genome represent the remnants of ancient infectious retroviruses. We show that the infection of CD4+ T cells with HIV-1 resulted in transcription of the HML-2 lineage of HERV type K [HERV-K(HML-2)] and the expression of Gag and Env proteins. HERV-K(HML-2)-specific CD8+ T cells obtained from HIV-1-infected human subjects responded to HIV-1-infected cells in a Vif-dependent manner in vitro. Consistent with the proposed mode of action, a HERV-K(HML-2)-specific CD8+ T cell clone exhibited comprehensive elimination of cells infected with a panel of globally diverse HIV-1, HIV-2, and SIV isolates in vitro. We identified a second T cell response that exhibited cross-reactivity between homologous HIV-1-Pol and HERV-K(HML-2)-Pol determinants, raising the possibility that homology between HIV-1 and HERVs plays a role in shaping, and perhaps enhancing, the T cell response to HIV-1. This justifies the consideration of HERV-K(HML-2)-specific and cross-reactive T cell responses in the natural control of HIV-1 infection and for exploring HERV-K(HML-2)-targeted HIV-1 vaccines and immunotherapeutics.