LipidWrapper: an algorithm for generating large-scale membrane models of arbitrary geometry

Exploring the Properties of Curved Lipid Membranes: Comparative Analysis of Atomistic and Coarse-Grained Force Fields

Curvature emerges as a fundamental membrane characteristic crucial for diverse biological processes, including vesicle formation, cell signaling, and membrane trafficking. Increasingly valuable insights into atomistic details governing curvature-dependent membrane properties are provided by computer simulations. Nevertheless, the underlying force field models are conventionally calibrated and tested in relation to experimentally derived parameters of planar bilayers, thereby leaving uncertainties concerning their consistency in reproducing curved lipid systems. In this study we compare the depiction of buckled phosphatidylcholine (POPC) and POPC-cholesterol membranes by four popular force field models. Aside from agreement with respect to general trends in curvature dependence of a number of parameters, we observe a few qualitative differences. Among the most prominent ones is the difference between atomistic and coarse grained force fields in their representation of relative compressibility of the polar headgroup region and hydrophobic lipid core. Through a number of downstream effects, this discrepancy can influence the way in which curvature modulates the behavior of membrane bound proteins depending on the adopted simulation model.

CHARMM-GUI Multicomponent Assembler for modeling and simulation of complex multicomponent systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. In this work, we demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to study complex interactions between small molecules, biomacromolecules, polymers, and nanomaterials.

Three-step docking by WIPI2, ATG16L1, and ATG3 delivers LC3 to the phagophore

The covalent attachment of ubiquitin-like LC3 proteins (microtubule-associated proteins 1A/1B light chain 3) prepares the autophagic membrane for cargo recruitment. We resolve key steps in LC3 lipidation by combining molecular dynamics simulations and experiments in vitro and in cellulo. We show how the E3-like ligaseautophagy-related 12 (ATG12)-ATG5-ATG16L1 in complex with the E2-like conjugase ATG3 docks LC3 onto the membrane in three steps by (i) the phosphatidylinositol 3-phosphate effector protein WD repeat domain phosphoinositide-interacting protein 2 (WIPI2), (ii) helix α2 of ATG16L1, and (iii) a membrane-interacting surface of ATG3. Phosphatidylethanolamine (PE) lipids concentrate in a region around the thioester bond between ATG3 and LC3, highlighting residues with a possible role in the catalytic transfer of LC3 to PE, including two conserved histidines. In a near-complete pathway from the initial membrane recruitment to the LC3 lipidation reaction, the three-step targeting of the ATG12-ATG5-ATG16L1 machinery establishes a high level of regulatory control.

Integrating cellular electron microscopy with multimodal data to explore biology across space and time

Biology spans a continuum of length and time scales. Individual experimental methods only glimpse discrete pieces of this spectrum but can be combined to construct a more holistic view. In this Review, we detail the latest advancements in volume electron microscopy (vEM) and cryo-electron tomography (cryo-ET), which together can visualize biological complexity across scales from the organization of cells in large tissues to the molecular details inside native cellular environments. In addition, we discuss emerging methodologies for integrating three-dimensional electron microscopy (3DEM) imaging with multimodal data, including fluorescence microscopy, mass spectrometry, single-particle analysis, and AI-based structure prediction. This multifaceted approach fills gaps in the biological continuum, providing functional context, spatial organization, molecular identity, and native interactions. We conclude with a perspective on incorporating diverse data into computational simulations that further bridge and extend length scales while integrating the dimension of time.

CHARMM-GUI Multicomponent Assembler for Modeling and Simulation of Complex Multicomponent Systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. We demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to facilitate innovative studies of complex interactions between small (organic and inorganic) molecules, biomacromolecules, polymers, and nanomaterials.

Ubiquitination regulates ER-phagy and remodelling of endoplasmic reticulum

The endoplasmic reticulum (ER) undergoes continuous remodelling via a selective autophagy pathway, known as ER-phagy¹. ER-phagy receptors have a central role in this process², but the regulatory mechanism remains largely unknown. Here we report that ubiquitination of the ER-phagy receptor FAM134B within its reticulon homology domain (RHD) promotes receptor clustering and binding to lipidated LC3B, thereby stimulating ER-phagy. Molecular dynamics (MD) simulations showed how ubiquitination perturbs the RHD structure in model bilayers and enhances membrane curvature induction. Ubiquitin molecules on RHDs mediate interactions between neighbouring RHDs to form dense receptor clusters that facilitate the large-scale remodelling of lipid bilayers. Membrane remodelling was reconstituted in vitro with liposomes and ubiquitinated FAM134B. Using super-resolution microscopy, we discovered FAM134B nanoclusters and microclusters in cells. Quantitative image analysis revealed a ubiquitin-mediated increase in FAM134B oligomerization and cluster size. We found that the E3 ligase AMFR, within multimeric ER-phagy receptor clusters, catalyses FAM134B ubiquitination and regulates the dynamic flux of ER-phagy. Our results show that ubiquitination enhances RHD functions via receptor clustering, facilitates ER-phagy and controls ER remodelling in response to cellular demands.

Understanding Virus Structure and Dynamics through Molecular Simulations

Viral outbreaks remain a serious threat to human and animal populations and motivate the continued development of antiviral drugs and vaccines, which in turn benefits from a detailed understanding of both viral structure and dynamics. While great strides have been made in characterizing these systems experimentally, molecular simulations have proven to be an essential, complementary approach. In this work, we review the contributions of molecular simulations to the understanding of viral structure, functional dynamics, and processes related to the viral life cycle. Approaches ranging from coarse-grained to all-atom representations are discussed, including current efforts at modeling complete viral systems. Overall, this review demonstrates that computational virology plays an essential role in understanding these systems.

CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications

Molecular dynamics simulations of membranes and membrane proteins serve as computational microscopes, revealing coordinated events at the membrane interface. As G protein-coupled receptors, ion channels, transporters, and membrane-bound enzymes are important drug targets, understanding their drug binding and action mechanisms in a realistic membrane becomes critical. Advances in materials science and physical chemistry further demand an atomistic understanding of lipid domains and interactions between materials and membranes. Despite a wide range of membrane simulation studies, generating a complex membrane assembly remains challenging. Here, we review the capability of CHARMM-GUI Membrane Builder in the context of emerging research demands, as well as the application examples from the CHARMM-GUI user community, including membrane biophysics, membrane protein drug-binding and dynamics, protein-lipid interactions, and nano-bio interface. We also provide our perspective on future Membrane Builder development.

Differential dynamics and direct interaction of bound ligands with lipids in multidrug transporter ABCG2

ABCG2 is an ATP-binding cassette (ABC) transporter that extrudes a wide range of xenobiotics and drugs from the cell and contributes to multidrug resistance in cancer cells. Following our recent structural characterization of topotecan-bound ABCG2, here, we present cryo-EM structures of ABCG2 under turnover conditions in complex with a special modulator and slow substrate, tariquidar, in nanodiscs. The structures reveal that similar to topotecan, tariquidar induces two distinct ABCG2 conformations under turnover conditions (turnover-1 and turnover-2). μs-scale molecular dynamics simulations of drug-bound and apo ABCG2 in native-like lipid bilayers, in both topotecan- and tariquidar-bound states, characterize the ligand size as a major determinant of its binding stability. The simulations highlight direct lipid-drug interactions for the smaller topotecan, which exhibits a highly dynamic binding mode. In contrast, the larger tariquidar occupies most of the available volume in the binding pocket, thus leaving little space for lipids to enter the cavity and interact with it. Similarly, when simulating ABCG2 in the apo inward-open state, we also observe spontaneous penetration of phospholipids into the binding cavity. The captured phospholipid diffusion pathway into ABCG2 offers a putative general path to recruit any hydrophobic/amphiphilic substrates directly from the membrane. Our simulations also reveal that ABCG2 rejects cholesterol as a substrate, which is omnipresent in plasma membranes that contain ABCG2. At the same time, cholesterol is found to prohibit the penetration of phospholipids into ABCG2. These molecular findings have direct functional ramifications on ABCG2's function as a transporter.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Breathing and Tilting: Mesoscale Simulations Illuminate Influenza Glycoprotein Vulnerabilities

Influenza virus has resurfaced recently from inactivity during the early stages of the COVID-19 pandemic, raising serious concerns about the nature and magnitude of future epidemics. The main antigenic targets of influenza virus are two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). Whereas the structural and dynamical properties of both glycoproteins have been studied previously, the understanding of their plasticity in the whole-virion context is fragmented. Here, we investigate the dynamics of influenza glycoproteins in a crowded protein environment through mesoscale all-atom molecular dynamics simulations of two evolutionary-linked glycosylated influenza A whole-virion models. Our simulations reveal and kinetically characterize three main molecular motions of influenza glycoproteins: NA head tilting, HA ectodomain tilting, and HA head breathing. The flexibility of HA and NA highlights antigenically relevant conformational states, as well as facilitates the characterization of a novel monoclonal antibody, derived from convalescent human donor, that binds to the underside of the NA head. Our work provides previously unappreciated views on the dynamics of HA and NA, advancing the understanding of their interplay and suggesting possible strategies for the design of future vaccines and antivirals against influenza.

Membrane curvature sensing and stabilization by the autophagic LC3 lipidation machinery

How the highly curved phagophore membrane is stabilized during autophagy initiation is a major open question in autophagosome biogenesis. Here, we use in vitro reconstitution on membrane nanotubes and molecular dynamics simulations to investigate how core autophagy proteins in the LC3 (Microtubule-associated proteins 1A/1B light chain 3) lipidation cascade interact with curved membranes, providing insight into their possible roles in regulating membrane shape during autophagosome biogenesis. ATG12(Autophagy-related 12)-ATG5-ATG16L1 was up to 100-fold enriched on highly curved nanotubes relative to flat membranes. At high surface density, ATG12-ATG5-ATG16L1 binding increased the curvature of the nanotubes. While WIPI2 (WD repeat domain phosphoinositide-interacting protein 2) binding directs membrane recruitment, the amphipathic helix α2 of ATG16L1 is responsible for curvature sensitivity. Molecular dynamics simulations revealed that helix α2 of ATG16L1 inserts shallowly into the membrane, explaining its curvature-sensitive binding to the membrane. These observations show how the binding of the ATG12-ATG5-ATG16L1 complex to the early phagophore rim could stabilize membrane curvature and facilitate autophagosome growth.

Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex

Nuclear pore complexes (NPCs) control biomolecular transport in and out of the nucleus. Disordered nucleoporins in the complex's pore form a permeation barrier, preventing unassisted transport of large biomolecules. Here, we combine coarse-grained simulations of experimentally derived NPC structures with a theoretical model to determine the microscopic mechanism of passive transport. Brute-force simulations of protein transport reveal telegraph-like behavior, where prolonged diffusion on one side of the NPC is interrupted by rapid crossings to the other. We rationalize this behavior using a theoretical model that reproduces the energetics and kinetics of permeation solely from statistics of transient voids within the disordered mesh. As the protein size increases, the mesh transforms from a soft to a hard barrier, enabling orders-of-magnitude reduction in permeation rate for proteins beyond the percolation size threshold. Our model enables exploration of alternative NPC architectures and sets the stage for uncovering molecular mechanisms of facilitated nuclear transport.

Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape

The surface assessment via grid evaluation (SuAVE) software was developed to account for the effect of curvature in the calculations of structural properties of chemical interfaces regardless of the chemical composition, asymmetry, and level of atom coarseness. It employs differential geometry techniques, enabling the representation of chemical surfaces as fully differentiable. In this article, we present novel developments of SuAVE to treat closed surfaces and complex cavity shapes. These developments expand the repertoire of curvature-dependent analyses already available in the previous version of SuAVE (e.g., area per lipid, density profiles, membrane thickness, deuterium-order parameters, volume per lipid, and surface curvature angle) to include new functionalities applicable to soft matter (e.g., sphericity, average radius, principal moment of inertia, and roundness) and crystalline porous materials (e.g., pore diameter, internal void volume, total area, and the total void volume of the unit cell structure). SuAVE can accurately handle chemical systems with high and low atom density as demonstrated for two distinct chemical systems: the lipid A vesicle and a set of selected metal–organic frameworks. The SuAVE software v2.0 is fully parallel and benefits from a compiler that supports OpenMP. SuAVE is freely available from https://github.com/SuAVE-Software/source and https://www.biomatsite.net/.

Breathing and tilting: mesoscale simulations illuminate influenza glycoprotein vulnerabilities

Influenza virus has resurfaced recently from inactivity during the early stages of the COVID-19 pandemic, raising serious concerns about the nature and magnitude of future epidemics. The main antigenic targets of influenza virus are two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). Whereas the structural and dynamical properties of both glycoproteins have been studied previously, the understanding of their plasticity in the whole-virion context is fragmented. Here, we investigate the dynamics of influenza glycoproteins in a crowded protein environment through mesoscale all-atom molecular dynamics simulations of two evolutionary-linked glycosylated influenza A whole-virion models. Our simulations reveal and kinetically characterize three main molecular motions of influenza glycoproteins: NA head tilting, HA ectodomain tilting, and HA head breathing. The flexibility of HA and NA highlights antigenically relevant conformational states, as well as facilitates the characterization of a novel monoclonal antibody, derived from human convalescent plasma, that binds to the underside of the NA head. Our work provides previously unappreciated views on the dynamics of HA and NA, advancing the understanding of their interplay and suggesting possible strategies for the design of future vaccines and antivirals against influenza.

Molecular Dynamics Simulations of Curved Lipid Membranes

Eukaryotic cells contain membranes with various curvatures, from the near-plane plasma membrane to the highly curved membranes of organelles, vesicles, and membrane protrusions. These curvatures are generated and sustained by curvature-inducing proteins, peptides, and lipids, and describing these mechanisms is an important scientific challenge. In addition to that, some molecules can sense membrane curvature and thereby be trafficked to specific locations. The description of curvature sensing is another fundamental challenge. Curved lipid membranes and their interplay with membrane-associated proteins can be investigated with molecular dynamics (MD) simulations. Various methods for simulating curved membranes with MD are discussed here, including tools for setting up simulation of vesicles and methods for sustaining membrane curvature. The latter are divided into methods that exploit scaffolding virtual beads, methods that use curvature-inducing molecules, and methods applying virtual forces. The variety of simulation tools allow researcher to closely match the conditions of experimental studies of membrane curvatures.

The ugly, bad, and good stories of large-scale biomolecular simulations

Molecular modeling of large biomolecular assemblies exemplifies a disruptive area holding both promises and contentions. Propelled by peta and exascale computing, several simulation methodologies have now matured into user-friendly tools that are successfully employed for modeling viruses, membranous nano-constructs, and key pieces of the genetic machinery. We present three unifying biophysical themes that emanate from some of the most recent multi-million atom simulation endeavors. Despite connecting molecular changes with phenotypic outcomes, the quality measures of these simulations remain questionable. We discuss the existing and upcoming strategies for constructing representative ensembles of large systems, how new computing technologies will boost this area, and make a point that integrative modeling guided by experimental data is the future of biomolecular computations.

Assembly and Analysis of Cell-Scale Membrane Envelopes

The march toward exascale computing will enable routine molecular simulation of larger and more complex systems, for example, simulation of entire viral particles, on the scale of approximately billions of atoms─a simulation size commensurate with a small bacterial cell. Anticipating the future hardware capabilities that will enable this type of research and paralleling advances in experimental structural biology, efforts are currently underway to develop software tools, procedures, and workflows for constructing cell-scale structures. Herein, we describe our efforts in developing and implementing an efficient and robust workflow for construction of cell-scale membrane envelopes and embedding membrane proteins into them. A new approach for construction of massive membrane structures that are stable during the simulations is built on implementing a subtractive assembly technique coupled with the development of a structure concatenation tool (fastmerge), which eliminates overlapping elements based on volumetric criteria rather than adding successive molecules to the simulation system. Using this approach, we have constructed two "protocells" consisting of MARTINI coarse-grained beads to represent cellular membranes, one the size of a cellular organelle and another the size of a small bacterial cell. The membrane envelopes constructed here remain whole during the molecular dynamics simulations performed and exhibit water flux only through specific proteins, demonstrating the success of our methodology in creating tight cell-like membrane compartments. Extended simulations of these cell-scale structures highlight the propensity for nonspecific interactions between adjacent membrane proteins leading to the formation of protein microclusters on the cell surface, an insight uniquely enabled by the scale of the simulations. We anticipate that the experiences and best practices presented here will form the basis for the next generation of cell-scale models, which will begin to address the addition of soluble proteins, nucleic acids, and small molecules essential to the function of a cell.

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Integrative structural modelling and visualisation of a cellular organelle

Models of insulin secretory vesicles from pancreatic beta cells have been created using the cellPACK suite of tools to research, curate, construct and visualise the current state of knowledge. The model integrates experimental information from proteomics, structural biology, cryoelectron microscopy and X-ray tomography, and is used to generate models of mature and immature vesicles. A new method was developed to generate a confidence score that reconciles inconsistencies between three available proteomes using expert annotations of cellular localisation. The models are used to simulate soft X-ray tomograms, allowing quantification of features that are observed in experimental tomograms, and in turn, allowing interpretation of X-ray tomograms at the molecular level.

#COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus ob-scure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

ACM REFERENCE FORMAT

Abigail Dommer 1† , Lorenzo Casalino 1† , Fiona Kearns 1† , Mia Rosenfeld 1 , Nicholas Wauer 1 , Surl-Hee Ahn 1 , John Russo, 2 Sofia Oliveira 3 , Clare Morris 1 , AnthonyBogetti 4 , AndaTrifan 5,6 , Alexander Brace 5,7 , TerraSztain 1,8 , Austin Clyde 5,7 , Heng Ma 5 , Chakra Chennubhotla 4 , Hyungro Lee 9 , Matteo Turilli 9 , Syma Khalid 10 , Teresa Tamayo-Mendoza 11 , Matthew Welborn 11 , Anders Christensen 11 , Daniel G. A. Smith 11 , Zhuoran Qiao 12 , Sai Krishna Sirumalla 11 , Michael O'Connor 11 , Frederick Manby 11 , Anima Anandkumar 12,13 , David Hardy 6 , James Phillips 6 , Abraham Stern 13 , Josh Romero 13 , David Clark 13 , Mitchell Dorrell 14 , Tom Maiden 14 , Lei Huang 15 , John McCalpin 15 , Christo- pherWoods 3 , Alan Gray 13 , MattWilliams 3 , Bryan Barker 16 , HarindaRajapaksha 16 , Richard Pitts 16 , Tom Gibbs 13 , John Stone 6 , Daniel Zuckerman 2 *, Adrian Mulholland 3 *, Thomas MillerIII 11,12 *, ShantenuJha 9 *, Arvind Ramanathan 5 *, Lillian Chong 4 *, Rommie Amaro 1 *. 2021. #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy ofDeltaSARS-CoV-2 in a Respiratory Aerosol. In Supercomputing '21: International Conference for High Perfor-mance Computing, Networking, Storage, and Analysis . ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI.

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Large-Scale Molecular Dynamics Simulations of Cellular Compartments

Molecular dynamics or MD simulation is gradually maturing into a tool for constructing in vivo models of living cells in atomistic details. The feasibility of such models is bolstered by integrating the simulations with data from microscopic, tomographic and spectroscopic experiments on exascale supercomputers, facilitated by the use of deep learning technologies. Over time, MD simulation has evolved from tens of thousands of atoms to over 100 million atoms comprising an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium. In this chapter, we present a step-by-step outline for preparing, executing and analyzing such large-scale MD simulations of biological systems that are essential to life processes. All scripts are provided via GitHub.

Fighting viruses with computers, right now

The synergistic conjunction of various technological revolutions with the accumulated knowledge and workflows is rapidly transforming several scientific fields. Particularly, Virology can now feed from accurate physical models, polished computational tools, and massive computational power to readily integrate high-resolution structures into biological representations of unprecedented detail. That preparedness allows for the first time to get crucial information for vaccine and drug design from in-silico experiments against emerging pathogens of worldwide concern at relevant action windows. The present work reviews some of the main milestones leading to these breakthroughs in Computational Virology, providing an outlook for future developments in capacity building and accessibility to computational resources.

Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models

We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.

Wrapping Up Viruses at Multiscale Resolution: Optimizing PACKMOL and SIRAH Execution for Simulating the Zika Virus

Simulating huge biomolecular complexes of million atoms at relevant biological time scales is becoming accessible to the broad scientific community. That proves to be crucial for urgent responses against emergent diseases in real time. Yet, there are still issues to sort regarding the system setup so that molecular dynamics (MD) simulations can be run in a simple and standard way. Here, we introduce an optimized pipeline for building and simulating enveloped virus-like particles (VLP). First, the membrane packing problem is tackled with new features and optimized options in PACKMOL. This allows preparing accurate membrane models of thousands of lipids in the context of a VLP within a few hours using a single CPU. Then, the assembly of the VLP system is done within the multiscale framework of the coarse-grained SIRAH force field. Finally, the equilibration protocol provides a system ready for production MD simulations within a few days on broadly accessible GPU resources. The pipeline is applied to study the Zika virus as a test case for large biomolecular systems. The VLP stabilizes at approximately 0.5 μs of MD simulation, reproducing correlations greater than 0.90 against experimental density maps from cryo-electron microscopy. Detailed structural analysis of the protein envelope also shows very good agreement in root-mean-square deviations and B-factors with the experimental data. The level of details attained shows for the first time a possible role for anionic phospholipids in stabilizing the envelope. Combining an efficient and reliable setup procedure with an accurate coarse-grained force field provides a valuable pipeline for simulating arbitrary viral systems or subcellular compartments, paving the way toward whole-cell simulations.

AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Backmapping triangulated surfaces to coarse-grained membrane models

Many biological processes involve large-scale changes in membrane shape. Computer simulations of these processes are challenging since they occur across a wide range of spatiotemporal scales that cannot be investigated in full by any single current simulation technique. A potential solution is to combine different levels of resolution through a multiscale scheme. Here, we present a multiscale algorithm that backmaps a continuum membrane model represented as a dynamically triangulated surface (DTS) to its corresponding molecular model based on the coarse-grained (CG) Martini force field. Thus, we can use DTS simulations to equilibrate slow large-scale membrane conformational changes and then explore the local properties at CG resolution. We demonstrate the power of our method by backmapping a vesicular bud induced by binding of Shiga toxin and by transforming the membranes of an entire mitochondrion to near-atomic resolution. Our approach opens the way to whole cell simulations at molecular detail.

Computer simulations of large-scale changes in membrane shape are challenging since they occur across a wide range of spatiotemporal scales. Here, authors present a multiscale algorithm that backmaps a continuum membrane model represented as a dynamically triangulated surface to its corresponding molecular model based on the coarse-grained Martini force field.

Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism

We report a 100-million atom-scale model of an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium, that reveals the cascade of energy conversion steps culminating in the generation of ATP from sunlight. Molecular dynamics simulations of this vesicle elucidate how the integral membrane complexes influence local curvature to tune photoexcitation of pigments. Brownian dynamics of small molecules within the chromatophore probe the mechanisms of directional charge transport under various pH and salinity conditions. Reproducing phenotypic properties from atomistic details, a kinetic model evinces that low-light adaptations of the bacterium emerge as a spontaneous outcome of optimizing the balance between the chromatophore's structural integrity and robust energy conversion. Parallels are drawn with the more universal mitochondrial bioenergetic machinery, from whence molecular-scale insights into the mechanism of cellular aging are inferred. Together, our integrative method and spectroscopic experiments pave the way to first-principles modeling of whole living cells.

Mathematical determination of the HIV-1 matrix shell structure and its impact on the biology of HIV-1

Since its discovery in the early 1980s, there has been significant progress in understanding the biology of type 1 human immunodeficiency virus (HIV-1). Structural biologists have made tremendous contributions to this challenge, guiding the development of current therapeutic strategies. Despite our efforts, there are unresolved structural features of the virus and consequently, significant knowledge gaps in our understanding. The superstructure of the HIV-1 matrix (MA) shell has not been elucidated. Evidence by various high-resolution microscopy techniques support a model composed of MA trimers arranged in a hexameric configuration consisting of 6 MA trimers forming a hexagon. In this manuscript we review the mathematical limitations of this model and propose a new model consisting of a 6-lune hosohedra structure, which aligns with available structural evidence. We used geometric and rotational matrix computation methods to construct our model and predict a new mechanism for viral entry that explains the increase in particle size observed during CD4 receptor engagement and the most common HIV-1 ellipsoidal shapes observed in cryo-EM tomograms. A better understanding of the HIV-1 MA shell structure is a key step towards better models for viral assembly, maturation and entry. Our new model will facilitate efforts to improve understanding of the biology of HIV-1.

Symmetry at the Cellular Mesoscale

Symmetry plays a functional role in the structure and action of biomolecules and their associations and interactions in living cells. This symmetry is a natural consequence of the evolutionary mechanisms that lead to the development of life, and it ranges from perfect point-group symmetry in protein oligomers to more approximate symmetries in the higher-order mesoscale structure of cellular environments.

Computational Modeling of Realistic Cell Membranes

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.

Simulation Best Practices for Lipid Membranes [Article v1.0]

We establish a reliable and robust standardization of settings for practical molecular dynamics (MD) simulations of pure and mixed (single- and multi-component) lipid bilayer membranes. In lipid membranes research, particle-based molecular simulations are a powerful tool alongside continuum theory, lipidomics, and model, in vitro, and in vivo experiments. Molecular simulations can provide precise and reproducible spatiotemporal (atomic- and femtosecond-level) information about membrane structure, mechanics, thermodynamics, kinetics, and dynamics. Yet the simulation of lipid membranes can be a daunting task, given the uniqueness of lipid membranes relative to conventional liquid-liquid and solid-liquid interfaces, the immense and complex thermodynamic and statistical mechanical theory, the diversity of multiscale lipid models, limitations of modern computing power, the difficulty and ambiguity of simulation controls, finite size effects, competitive continuum simulation alternatives, and the desired application, including vesicle experiments and biological membranes. These issues can complicate an essential understanding of the field of lipid membranes, and create major bottlenecks to simulation advancement. In this article, we clarify these issues and present a consistent, thorough, and user-friendly framework for the design of state-of-the-art lipid membrane MD simulations. We hope to allow early-career researchers to quickly overcome common obstacles in the field of lipid membranes and reach maximal impact in their simulations.

Multiscale Modeling and Simulation Approaches to Lipid-Protein Interactions

Lipid membranes play a crucial role in living systems by compartmentalizing biological processes and forming a barrier between these processes and the environment. Naturally, a large apparatus of biomolecules is responsible for construction, maintenance, transport, and degradation of these lipid barriers. Additional classes of biomolecules are tasked with transport of specific substances or transduction of signals from the environment across lipid membranes. In this article, we intend to describe a set of techniques that enable one to build accurate models of lipid systems and their associated proteins, and to simulate their dynamics over a variety of time and length scales. We discuss the methods and challenges that allow us to derive structural, mechanistic, and thermodynamic information from these models. We also show how these models have recently been applied in research to study some of the most complex lipid-protein systems to date, including bacterial and viral envelopes, neuronal membranes, and mammalian signaling systems.

BUMPy: A Model-Independent Tool for Constructing Lipid Bilayers of Varying Curvature and Composition

Molecular dynamics is a powerful tool to investigate atomistic and mesoscopic phenomena in lipid bilayer systems. These studies have progressed with the advent of increased computational power, and efforts are now increasingly being directed toward investigating the role of curvature and bilayer morphology, as these are critical features of biological processes. Computational studies of lipid bilayers benefit from tools that can create starting configurations for molecular dynamics simulations, but the majority of such tools are restricted to generating flat bilayers. Generating curved bilayer configurations comes with practical complications and potential ramifications on physical properties in the simulated system if the bilayer is initiated in a high-strain state. We present a new tool for creating curved lipid bilayers that combines flexibility of shape, force field, model resolution, and bilayer composition. A key aspect of our approach is the use of the monolayer pivotal plane location to accurately estimate interleaflet area differences in a curved bilayer. Our tool is named BUMPy (Building Unique Membranes in Python), is written in Python, is fast, and has a simple command line interface.

A Computational Assay that Explores the Hemagglutinin/Neuraminidase Functional Balance Reveals the Neuraminidase Secondary Site as a Novel Anti-Influenza Target

Studies of pathogen-host specificity, virulence, and transmissibility are critical for basic research as well as for assessing the pandemic potential of emerging infectious diseases. This is especially true for viruses such as influenza, which continue to affect millions of people annually through both seasonal and occasional pandemic events. Although the influenza virus has been fairly well studied for decades, our understanding of host-cell binding and its relation to viral transmissibility and infection is still incomplete. Assessing the binding mechanisms of complex biological systems with atomic-scale detail is challenging given current experimental limitations. Much remains to be learned, for example, about how the terminal residue of influenza-binding host-cell receptors (sialic acid) interacts with the viral surface. Here, we present an integrative structural-modeling and physics-based computational assay that reveals the sialic acid association rate constants (k _on) to three influenza sites: the hemagglutinin (HA), neuraminidase (NA) active, and NA secondary binding sites. We developed a series of highly detailed (atomic-resolution) structural models of fully intact influenza viral envelopes. Brownian dynamics simulations of these systems showed how structural properties, such as stalk height and secondary-site binding, affect sialic acid k _on values. Comparing the k _on values of the three sialic acid binding sites across different viral strains suggests a detailed model of encounter-complex formation and indicates that the secondary NA binding site may play a compensatory role in host-cell receptor binding. Our method elucidates the competition among these sites, all present on the same virion, and provides a new technology for directly studying the functional balance between HA and NA.

From Atoms to Cells: Using Mesoscale Landscapes to Construct Visual Narratives

Modeling and visualization of the cellular mesoscale, bridging the nanometer scale of molecules to the micrometer scale of cells, is being studied by an integrative approach. Data from structural biology, proteomics, and microscopy are combined to simulate the molecular structure of living cells. These cellular landscapes are used as research tools for hypothesis generation and testing, and to present visual narratives of the cellular context of molecular biology for dissemination, education, and outreach.

Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.

Lattice Models of Bacterial Nucleoids

Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.

Instant Construction and Visualization of Crowded Biological Environments

We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.

Molecular simulations of cellular processes

It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cell-cycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms. Graphical abstract.

Lipid topology and electrostatic interactions underpin lytic activity of linear cationic antimicrobial peptides in membranes

Linear cationic antimicrobial peptides are a diverse class of molecules that interact with a wide range of cell membranes. Many of these peptides disrupt cell integrity by forming membrane-spanning pores that ultimately lead to their death. Despite these peptides high potency and ability to evade acquired bacterial drug resistance, there is a lack of knowledge on their selectivity and activity mechanisms. Such an understanding would provide an informative framework for rational design and could lead to potential antimicrobial therapeutic targets. In this paper, we use a high-throughput microfluidic platform as a quantitative screen to assess peptide activity and selectivity by precisely controlling exposure to vesicles with lipid compositions that mimic both bacterial and mammalian cell membranes. We explore the complexity of the lipid-peptide interactions governing membrane-disruptive behaviors and establish a link between peptide pore formation and both lipid-peptide charge and topological interactions. We propose a topological model for linear antimicrobial peptide activity based on the increase in membrane strain caused by the continuous adsorption of peptides to the target vesicle coupled with the effects of both lipid-peptide charge and topographical interactions. We also show the validity of the proposed model by investigating the activity of two prototypical linear cationic peptides: magainin 2 amide (which is selective for bacterial cells) and melittin (which targets both mammalian and bacterial cells indiscriminately). Finally, we propose the existence of a negative feedback mechanism that governs the pore formation process and controls the membrane's apparent permeability.

Scoria: a Python module for manipulating 3D molecular data

Third-party packages have transformed the Python programming language into a powerful computational-biology tool. Package installation is easy for experienced users, but novices sometimes struggle with dependencies and compilers. This presents a barrier that can hinder the otherwise broad adoption of new tools. We present Scoria, a Python package for manipulating three-dimensional molecular data. Unlike similar packages, Scoria requires no dependencies, compilation, or system-wide installation. One can incorporate the Scoria source code directly into their own programs. But Scoria is not designed to compete with other similar packages. Rather, it complements them. Our package leverages others (e.g. NumPy, SciPy), if present, to speed and extend its own functionality. To show its utility, we use Scoria to analyze a molecular dynamics trajectory. Our FootPrint script colors the atoms of one chain by the frequency of their contacts with a second chain. We are hopeful that Scoria will be a useful tool for the computational-biology community. A copy is available for download free of charge (Apache License 2.0) at http://durrantlab.com/scoria/.Graphical abstract

Computational virology: From the inside out

Viruses typically pack their genetic material within a protein capsid. Enveloped viruses also have an outer membrane made up of a lipid bilayer and membrane-spanning glycoproteins. X-ray diffraction and cryoelectron microscopy provide high resolution static views of viral structure. Molecular dynamics (MD) simulations may be used to provide dynamic insights into the structures of viruses and their components. There have been a number of simulations of viral capsids and (in some cases) of the inner core of RNA or DNA packaged within them. These simulations have generally focussed on the structural integrity and stability of the capsid and/or on the influence of the nucleic acid core on capsid stability. More recently there have been a number of simulation studies of enveloped viruses, including HIV-1, influenza A, and dengue virus. These have addressed the dynamic behaviour of the capsid, the matrix, and/or of the outer envelope. Analysis of the dynamics of the lipid bilayer components of the envelopes of influenza A and of dengue virus reveals a degree of biophysical robustness, which may contribute to the stability of virus particles in different environments. Significant computational challenges need to be addressed to aid simulation of complex viruses and their membranes, including the need to integrate structural data from a range of sources to enable us to move towards simulations of intact virions. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

The Role of the Membrane in the Structure and Biophysical Robustness of the Dengue Virion Envelope

The dengue virion is surrounded by an envelope of membrane proteins surrounding a lipid bilayer. We have combined the cryoelectron microscopy structures of the membrane proteins (PDB: 3J27) with a lipid bilayer whose composition is based on lipidomics data for insect cell membranes, to obtain a near-atomic resolution computational model of the envelope of the dengue virion. A coarse-grained molecular dynamics simulation on the microsecond timescale enables analysis of key biophysical properties of the dengue outer envelope. Properties analyzed include area per lipid values (for a spherical virion with a mixed lipid composition), bilayer thickness, and lipid diffusion coefficients. Despite the absence of cholesterol from the lipid bilayer, the virion exhibits biophysical robustness (slow lipid diffusion alongside stable bilayer thickness, virion diameter, and shape) that matches the cholesterol-rich membrane of influenza A, with similarly anomalous diffusion of lipids. Biophysical robustness of the envelope may confer resilience to environmental perturbations.

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The dengue virus envelope is a lipid bilayer plus an outer layer of membrane proteins

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The structures of the proteins plus lipidomics data were used to model the envelope

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Microsecond MD simulations revealed the dynamic behavior of the lipid bilayer

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Protein interactions confer “raft-like” robustness on a cholesterol-free membrane

CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field

Coarse-grained simulations are widely used to study large biological systems. Nonetheless, building such simulation systems becomes nontrivial, especially when membranes with various lipid types are involved. Taking advantage of the frameworks in all-atom CHARMM-GUI modules, we have developed CHARMM-GUI Martini Maker for building solution, micelle, bilayer, and vesicle systems as well as systems with randomly distributed lipids using the Martini force field. Martini Maker supports 82 lipid types and different flavors of the Martini force field, including polar and nonpolar Martini, Dry Martini, and ElNeDyn (an elastic network model for proteins). The qualities of the systems generated by Martini Maker are validated by simulations of various examples involving proteins and lipids. We expect Martini Maker to be a useful tool for modeling large, complicated biomolecular systems in a user-friendly way.

Alchembed: A Computational Method for Incorporating Multiple Proteins into Complex Lipid Geometries

A necessary step prior to starting any membrane protein computer simulation is the creation of a well-packed configuration of protein(s) and lipids. Here, we demonstrate a method, alchembed, that can simultaneously and rapidly embed multiple proteins into arrangements of lipids described using either atomistic or coarse-grained force fields. During a short simulation, the interactions between the protein(s) and lipids are gradually switched on using a soft-core van der Waals potential. We validate the method on a range of membrane proteins and determine the optimal soft-core parameters required to insert membrane proteins. Since all of the major biomolecular codes include soft-core van der Waals potentials, no additional code is required to apply this method. A tutorial is included in the Supporting Information.

Computational Lipidomics with insane: A Versatile Tool for Generating Custom Membranes for Molecular Simulations

For simulations of membranes and membrane proteins, the generation of the lipid bilayer is a critical step in the setup of the system. Membranes comprising multiple components pose a particular challenge, because the relative abundances need to be controlled and the equilibration of the system may take several microseconds. Here we present a comprehensive method for building membrane containing systems, characterized by simplicity and versatility. The program uses preset, coarse-grain lipid templates to build the membrane, and also allows on-the-fly generation of simple lipid types by specifying the headgroup, linker, and lipid tails on the command line. The resulting models can be equilibrated, after which a relaxed atomistic model can be obtained by reverse transformation. For multicomponent membranes, this provides an efficient means for generating equilibrated atomistic models. The method is called insane, an acronym for INSert membrANE. The program has been made available, together with the complementary method for reverse transformation, at http://cgmartini.nl/ . This work highlights the key features of insane and presents a survey of properties for a large range of lipids as a start of a computational lipidomics project.