Betacoronaviruses pose an ongoing pandemic threat. Antigenic evolution of the SARS-CoV-2 virus has shown that much of the spontaneous antibody response is narrowly focused rather than broadly neutralizing against even SARS-CoV-2 variants, let alone future threats. One way to overcome this is by focusing the antibody response against better-conserved regions of the viral spike protein. This has been demonstrated empirically in prior work, but we posit that systematic design tools will further potentiate antigenic focusing approaches. Here, we present a design approach to predict stable chimeras between SARS-CoV-2 and other coronaviruses, creating synthetic spike proteins that display a desired conserved region, in this case S2, and vary other regions. We leverage AlphaFold to predict chimeric structures and create a new metric for scoring chimera stability based on AlphaFold outputs. We evaluated 114 candidate spike chimeras using this approach. Top chimeras were further evaluated using molecular dynamics simulation as an intermediate validation technique, showing good stability compared to low-scoring controls. Experimental testing of five predicted-stable and two predicted-unstable chimeras confirmed 5/7 predictions, with one intermediate result. This demonstrates the feasibility of the underlying approach, which can be used to design custom immunogens to focus the immune response against a desired viral glycoprotein epitope.

Gmxapi provides an integrated, native Python API for both standard and advanced molecular dynamics simulations in GROMACS. The Python interface permits multiple levels of integration with the core GROMACS libraries, and legacy support is provided via an interface that mimics the command-line syntax, so that all GROMACS commands are fully available. Gmxapi has been officially supported since the GROMACS 2019 release and is enabled by default in current versions of the software. Here we describe gmxapi 0.3 and later. Beyond simply wrapping GROMACS library operations, the API permits several advanced operations that are not feasible using the prior command-line interface. First, the API allows custom user plugin code within the molecular dynamics force calculations, so users can execute custom algorithms without modifying the GROMACS source. Second, the Python interface allows tasks to be dynamically defined, so high-level algorithms for molecular dynamics simulation and analysis can be coordinated with loop and conditional operations. Gmxapi makes GROMACS more accessible to custom Python scripting while also providing support for high-level data-flow simulation algorithms that were previously feasible only in external packages.

Summary: Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles. Availability and implementation: LGPLv2.1 source and instructions are available at https://github.com/kassonlab/gmxapi. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Bacterial resistance to antibiotics, particularly plasmid-encoded resistance to beta lactam drugs, poses an increasing threat to human health. Point mutations to beta-lactamase enzymes can greatly alter the level of resistance conferred, but predicting the effects of such mutations has been challenging due to the large combinatorial space involved and the subtle relationships of distant residues to catalytic function. Therefore we desire an information-theoretic metric to sensitively and robustly detect both local and distant residues that affect substrate conformation and catalytic activity. RESULTS: Here, we report the use of positional mutual information in multiple microsecond-length molecular dynamics (MD) simulations to predict residues linked to catalytic activity of the CTX-M9 beta lactamase. We find that motions of the bound drug are relatively isolated from motions of the protein as a whole, which we interpret in the context of prior theories of catalysis. In order to robustly identify residues that are weakly coupled to drug motions but nonetheless affect catalysis, we utilize an excess mutual information metric. We predict 31 such residues for the cephalosporin antibiotic cefotaxime. Nine of these have previously been tested experimentally, and all decrease both enzyme rate constants and empirical drug resistance. We prospectively validate our method by testing eight high-scoring mutations and eight low-scoring controls in bacteria. Six of eight predicted mutations decrease cefotaxime resistance greater than 2-fold, while only one control shows such an effect. The ability to prospectively predict new variants affecting bacterial drug resistance is of great interest to clinical and epidemiological surveillance. AVAILABILITY AND IMPLEMENTATION: Excess mutual information code is available at https://github.com/kassonlab/positionalmi CONTACT: [email protected].

MOTIVATION: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. RESULTS: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. AVAILABILITY: GROMACS is an open source and free software available from http://www.gromacs.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fusion peptides from influenza hemagglutinin act on membranes to promote membrane fusion, but the mechanism by which they do so remains unknown. Recent theoretical work has suggested that contact of protruding lipid tails may be an important feature of the transition state for membrane fusion. If this is so, then influenza fusion peptides would be expected to promote tail protrusion in proportion to the ability of the corresponding full-length hemagglutinin to drive lipid mixing in fusion assays. We have performed molecular dynamics simulations of influenza fusion peptides in lipid bilayers, comparing the X-31 influenza strain against a series of N-terminal mutants. As hypothesized, the probability of lipid tail protrusion correlates well with the lipid mixing rate induced by each mutant. This supports the conclusion that tail protrusion is important to the transition state for fusion. Furthermore, it suggests that tail protrusion can be used to examine how fusion peptides might interact with membranes to promote fusion. Previous models for native influenza fusion peptide structure in membranes include a kinked helix, a straight helix, and a helical hairpin. Our simulations visit each of these conformations. Thus, the free energy differences between each are likely low enough that specifics of the membrane environment and peptide construct may be sufficient to modulate the equilibrium between them. However, the kinked helix promotes lipid tail protrusion in our simulations much more strongly than the other two structures. We therefore predict that the kinked helix is the most fusogenic of these three conformations.

Biomolecular simulation is a core application on supercomputers, but it is exceptionally difficult to achieve the strong scaling necessary to reach biologically relevant timescales. Here, we present a new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus. This framework combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring. Copernicus enables execution as single parallel jobs with automatic resource allocation. Even for a small protein such as villin (9,864 atoms), Copernicus exhibits near-linear strong scaling from 1 to 5,376 AMD cores. Starting from extended chains we observe structures 0.6 Å from the native state within 30h, and achieve sufficient sampling to predict the native state without a priori knowledge after 80--90h. To match Copernicus' efficiency, a classical simulation would have to exceed 50 microseconds per day, currently infeasible even with custom hardware designed for simulations.

Membrane fusion is essential to both cellular vesicle trafficking and infection by enveloped viruses. While the fusion protein assemblies that catalyze fusion are readily identifiable, the specific activities of the proteins involved and nature of the membrane changes they induce remain unknown. Here, we use many atomic-resolution simulations of vesicle fusion to examine the molecular mechanisms for fusion in detail. We employ committor analysis for these million-atom vesicle fusion simulations to identify a transition state for fusion stalk formation. In our simulations, this transition state occurs when the bulk properties of each lipid bilayer remain in a lamellar state but a few hydrophobic tails bulge into the hydrophilic interface layer and make contact to nucleate a stalk. Additional simulations of influenza fusion peptides in lipid bilayers show that the peptides promote similar local protrusion of lipid tails. Comparing these two sets of simulations, we obtain a common set of structural changes between the transition state for stalk formation and the local environment of peptides known to catalyze fusion. Our results thus suggest that the specific molecular properties of individual lipids are highly important to vesicle fusion and yield an explicit structural model that could help explain the mechanism of catalysis by fusion proteins.

MOTIVATION: Membrane fusion constitutes a key stage in cellular processes such as synaptic neurotransmission and infection by enveloped viruses. Current experimental assays for fusion have thus far been unable to resolve early fusion events in fine structural detail. We have previously used molecular dynamics simulations to develop mechanistic models of fusion by small lipid vesicles. Here, we introduce a novel structural measurement of vesicle topology and fusion geometry: persistent voids. RESULTS: Persistent voids calculations enable systematic measurement of structural changes in vesicle fusion by assessing fusion stalk widths. They also constitute a generally applicable technique for assessing lipid topological change. We use persistent voids to compute dynamic relationships between hemifusion neck widening and formation of a full fusion pore in our simulation data. We predict that a tightly coordinated process of hemifusion neck expansion and pore formation is responsible for the rapid vesicle fusion mechanism, while isolated enlargement of the hemifusion diaphragm leads to the formation of a metastable hemifused intermediate. These findings suggest that rapid fusion between small vesicles proceeds via a small hemifusion diaphragm rather than a fully expanded one. AVAILABILITY: Software available upon request pending public release. SUPPLEMENTARY INFORMATION: Supplementary data are available on Bioinformatics online.

Membrane fusion is critical to biological processes such as viral infection, endocrine hormone secretion, and neurotransmission, yet the precise mechanistic details of the fusion process remain unknown. Current experimental and computational model systems approximate the complex physiological membrane environment for fusion using one or a few protein and lipid species. Here, we report results of a computational model system for fusion in which the ratio of lipid components was systematically varied, using thousands of simulations of up to a microsecond in length to predict the effects of lipid composition on both fusion kinetics and mechanism. In our simulations, increased phosphatidylcholine content in vesicles causes increased activation energies for formation of the initial stalk-like intermediate for fusion and of hemifusion intermediates, in accordance with previous continuum-mechanics theoretical treatments. We also use our large simulation dataset to quantitatively compare the mechanism by which vesicles fuse at different lipid compositions, showing a significant difference in fusion kinetics and mechanism at different compositions simulated. As physiological membranes have different compositions in the inner and outer leaflets, we examine the effect of such asymmetry, as well as the effect of membrane curvature on fusion. These predicted effects of lipid composition on fusion mechanism both underscore the way in which experimental model system construction may affect the observed mechanism of fusion and illustrate a potential mechanism for cellular regulation of the fusion process by altering membrane composition.

MOTIVATION: Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-learning-based method to differentiate signal from artifact in membrane protein localization data by deriving positional information via surface fitting and combining this with fluorescence-intensity-based data to generate input for a support vector machine. RESULTS: We have employed this classifier to analyze signaling protein localization in T-cell activation. Our classifier displayed increased performance over previously available techniques, exhibiting both flexibility and adaptability: training on heterogeneous data yielded a general classifier with good overall performance; training on more specific data yielded an extremely high-performance specific classifier. We also demonstrate accurate automated learning utilizing additional experimental data.

The relocalization of plasma membrane proteins is critical for establishing cellular polarity and regulating cell signaling. Three-dimensional fluorescence video microscopy allows the dynamic visualization of proteins in living cells. We have developed a robust and automated method to employ fluorescence data acquired in this manner for quantitative analysis of membrane protein movements across the cell surface. Our method utilizes level-set-based surface reconstruction followed by a maximum likelihood surface registration algorithm for rigid-body alignment of noisy images. A surface-walking technique yields distance maps for the cell surface, which are then used to measure changes in protein surface distribution over time. Applying this method to signaling in T lymphocytes, we have used it to monitor receptor movements and have validated these results against previously reported single-particle tracking data.