Biology subjects

Bryer, A. J.

Publications and source records attributed to Bryer, A. J..

3 recordsLinked to original sources

bioRxiv2023

Elasticity of the HIV-1 Core Facilitates Nuclear Entry and Infection

HIV-1 infection requires passage of the viral core through the nuclear pore of the cell, a process that depends on functions of the viral capsid 1,2. Recent studies have shown that HIV- 1 cores enter the nucleus prior to capsid disassembly 3-5. Interactions with the nuclear pore complex are necessary but not sufficient for nuclear entry, and the mechanism by which the viral core traverses the comparably sized nuclear pore is unknown. Here we show that the HIV-1 core is highly elastic and that this property is linked to nuclear entry and infectivity. Using atomic force microscopy-based approaches, we found that purified wild type cores rapidly returned to their normal conical morphology following a severe compression. Results from independently performed molecular dynamic simulations of the mature HIV-1 capsid also revealed its elastic property. Analysis of four HIV-1 capsid mutants that exhibit impaired nuclear entry revealed that the mutant viral cores are brittle. Suppressors of the mutants restored elasticity and rescued infectivity and nuclear entry. Elasticity was also reduced by treatment of cores with the capsid-targeting compound PF74 and the antiviral drug lenacapavir. Our results indicate that capsid elasticity is a fundamental property of the HIV-1 core that enables its passage through the nuclear pore complex, thereby facilitating infection. These results provide new insights into the mechanisms of HIV-1 nuclear entry and the antiviral mechanisms of HIV-1 capsid inhibitors.

biophysics↗

bioRxiv2022

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling has positioned itself as an indispensable tool for decades, particularly for biomolecular simulations where atomic systems encompass hundreds of millions of atoms. While distinct flavors of coarse grain modeling exist, those occupying the coarse end of the spectrum are typically knowledge based, relying on a priori information to parameterize models, thus hindering general predictive capability. Here, we present an algorithmic and transferable approach known as shape based coarse graining (SBCG) which employs unsupervised machine learning via competitive Hebbian adaptation to construct coarse molecules that perfectly represent atomistic topologies. We show how SBCG provides ample control over model granularity, and we provide a quantitative metric for selection thereof. Parameter optimization, inclusion of small molecule species, as well as simulation configuration are discussed in detail. Our method and its implementation is made available as part of the CGBuilder plugin, present in the widely-used visual molecular dynamics (VMD) and nanoscale molecular dynamics (NAMD) software suites. We demonstrate applications of our method with a variety of systems from the inositol hexaphosphate-bound, full-scale HIV-1 capsid to heteromultimeric cofilin-2-bound actin filaments. Overall, we show that SBCG provides a simple yet robust approach to coarse graining that requires minimal user input and lacks any ad hoc interactions between protein domains. Furthermore, because the Hamiltonian employed in SBCG is CHARMM compatible, SBCG takes full advantage of the latest GPU-accelerated NAMD3 yielding molecular sampling of over a microsecond per day for systems that span micrometers.

biophysics↗

bioRxiv2019

High-performance analysis of biomolecular containers to measure small-molecule transport, transbilayer lipid diffusion, and protein cavities

Compartmentalization is a central theme in biology. Cells are composed of numerous membrane-enclosed structures, evolved to facilitate specific biochemical processes; viruses act as containers of genetic material, optimized to drive infection. Molecular dynamics simulations provide a mechanism to study biomolecular containers and the influence they exert on their environments; however, trajectory analysis software generally lacks knowledge of container interior versus exterior. Further, many relevant container analyses involve large-scale particle tracking endeavors, which may become computationally prohibitive with increasing system size. Here, a novel method based on 3-D ray casting is presented, which rapidly classifies the space surrounding biomolecular containers of arbitrary shape, enabling fast determination of the identities and counts of particles (e.g., solvent molecules) found inside and outside. The method is broadly applicable to the study of containers and enables high-performance characterization of properties such as solvent density, small-molecule transport, transbilayer lipid diffusion, and topology of protein cavities. The method is implemented in VMD, a widely used simulation analysis tool that supports personal computers, clouds, and parallel supercomputers, including ORNLs Summit and Titan and NCSAs Blue Waters, where the method can be employed to efficiently analyze trajectories encompassing millions of particles. The ability to rapidly characterize the spatial relationships of particles relative to a biomolecular container over many trajectory frames, irrespective of large particle counts, enables analysis of containers on a scale that was previously unfeasible, at a level of accuracy that was previously unattainable.\n\nAuthor summaryThe cell is the basic unit of life. Within the container of the cell, the many chemical reactions and biological processes essential to life are carried out simultaneously. Human and other eukaryotic cells include a variety of sub-containers, namely organelles, that provide separation between reactions and processes, and engender the chemical environments conducive to them. In order to understand how the cell works, researchers must study the functions of these containers. Molecular dynamics simulations can reveal important information about how biomolecular containers behave and control their enclosed environments, but the latter can be particularly challenging and expensive to measure. The challenge arises because simulation analysis software lacks awareness of the concepts of container \"inside\" and \"outside.\" The expense arises because tracking the many solvent molecules that make up a containers environment requires significant computing power. We have developed a method that allows the simulation analysis software VMD to automatically detect the interior versus exterior of a container and quickly identify the solvent molecules found in each location. This versatile new feature enables researchers to characterize essential container properties using a relatively inexpensive calculation. Further, the method performs efficiently on supercomputers, allowing researchers to study massive container systems that include millions of particles.

biophysics↗