LipidWrapper: An Algorithm for Generating Large-Scale Membrane Models of Arbitrary Geometry

Durrant, Jacob D.; Amaro, Rommie E.

doi:10.1371/journal.pcbi.1003720

Cited by 65 publications

(88 citation statements)

References 86 publications

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“…Future challenges for such studies include a full model of glycosylation of viral surface proteins, which will enable more realistic and hence predictive modeling of virions binding to models of target cell membranes, and the effects of antibodies on such interactions. Models of virions and other complex mesoscale biomolecular assemblies can be built with tools such as cellPACK with LipidWrapper, incorporating a variety of experimental data. These models can be built in atomic detail, allowing simulations from this level and potentially connecting to larger scales …”

Section: Membrane Simulations and Coarse Grainingmentioning

confidence: 99%

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Huggins

Biggin

Dämgen

et al. 2018

WIREs Comput Mol Sci

140

142

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development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. enzyme, membrane, molecular dynamics, multiscale, protein, QM/MM 1 | INTRODUCTION Biomolecular simulations are now making significant contributions to a wide variety of problems in drug discovery, drug development, biocatalysis, biotechnology, nanotechnology, chemical biology, and medicine. Biomolecular simulation is a rapidly growing field in scale and impact, increasingly demonstrating its worth in understanding mechanisms and analyzing activities, and contributing to the design of drugs and biocatalysts. Physics-based simulations complement experiments in building a molecular-level understanding of biology: They can test hypotheses and interpret and analyze experimental data in terms of interactions at the atomic level. Different types of simulation techniques have been developed, which are applicable to a range of different problems in biomolecular science. Simulations have already shown their worth in helping to analyze how enzymes catalyze biochemical reactions, and how proteins adopt their functional structures, for example, within cell membranes. They contribute to the design of drugs and catalysts, and in understanding the molecular basis of disease. Simulations have played a key role in developing the conceptual framework now at the heart of biomolecular science: that the dynamics of biological molecules is central to their function. Developing methods from chemical physics and computational science will open exciting new opportunities in biomolecular science, including in drug design and development, biotechnology, and biocatalysis. With high-performance computing resources, large-scale atomistic simulations of biological machines such as the ribosome, proton pumps and motors, membrane receptor complexes, and even whole viruses have become possible. Useful simulations of smaller systems can be carried out with desktop resources, thanks to developments allowing, for example, graphics processing units (GPUs) to be used. A particular challenge across the field is the integration of simulations crossing the span of length-and timescales as different types of simulation method are required for different types of problems. 1 Biomolecular systems pose fundamental scientific challenges (e.g., protein folding, enzyme catalysis, gene regulation, disease mechanisms, and antimicrobial resistance) and are at the heart of many advanced technological developments (drug discovery, biotechnology, biocatalysis, biomaterials, and genetic engineering). Biomolecular systems are inherently complex and pose significant challenges in modeling. An essential underlying paradigm is the need to consider biomolecular ensembles and their dynamics, rather than simply static biomolecular structures to understand and predict their behavior and propertie...

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Section: Membrane Simulations and Coarse Grainingmentioning

confidence: 99%

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Huggins

Biggin

Dämgen

et al. 2018

WIREs Comput Mol Sci

140

142

View full text Add to dashboard Cite

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“…A plausable first pass at a bacterial map has surprisingly few unique environments (Figure 8). Methods, such as LipidWrapper [15], might then be used to populate these regions with structural "tiles" [16]. Of course, this approach completely breaks down for features with large-scale addressability, such as the bacterial genome.…”

Section: The Mesoscale Level: Self-similarity and Regularitymentioning

confidence: 99%

Symmetry at the Cellular Mesoscale

Goodsell

2019

Symmetry

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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.

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“…Far from being trivial, this point requires algorithms to fill a 3D space with cellular components, which should satisfy a number of experimental constraints to capture each compartment spatial ordering (Lučić et al 2013;Mahamid et al 2016;Danev and Baumeister 2017). CellPack, Chrom3D, and LipidWrapper are examples of software that address this first challenge and that might be employed to model the eukaryotic cell architecture (Durrant and Amaro 2014;Johnson et al 2015;Earnest et al 2017;Paulsen et al 2017).…”

Section: Conclusion and Future Challengesmentioning

confidence: 99%

Molecular simulations of cellular processes

Trovato

Fumagalli

2017

Biophys Rev

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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 cellcycle-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.

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LipidWrapper: An Algorithm for Generating Large-Scale Membrane Models of Arbitrary Geometry

Cited by 65 publications

References 86 publications

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

Symmetry at the Cellular Mesoscale

Molecular simulations of cellular processes

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