Whole Cell Model

We are now entering the era of whole-cell simulations. Together with the group of Zan Luthey-Schulten we already modeled an entire cell (JCVI-Syn3a) at the Martini level of resolution. Featuring more than 60,000 proteins, millions of lipids and metabolites, 500 ribosomes and a 500 kbp circular dsDNA, more than 500 million CG beads in total.

For details, read our perspective paper: Stevens et al., 'Molecular dynamics simulation of an entire cell', Frontiers in Chemistry, vol 11, 2023. https://doi.org/10.3389/fchem.2023.1106495

Martini-cell.jpg

Viruses

virion-martini.jpgThe need for detailed models and dynamics of virus particles is clear, as part of our fight against pandemics such as COVID. Given the size and composition of typical viruses, the use of Martini to simulate their behavior is apparent. Indeed, viruses have already been targeted with Martini, including the capsid of the Cowpea Mosaic virus [1], and enveloped ones such as Influenza A virion [2],  Dengue [3], and flavivirus [4]. More examples are reviewed in [5,6].

Also the COVID virus envelope itself is now available at the Martini level [7] !

[1] X. Periole, M. Cavalli, S.J. Marrink, M. Ceruso. Combining an elastic network with a coarse-grained molecular force field: structure, dynamics and intermolecular recognition. J. Chem. Th. Comp., 5:2531-2543, 2009. abstract

[2] T. Reddy, D. Shorthouse, D.L. Parton, E. Jefferys, P.W. Fowler, M. Chavent, M. Baaden, M.S.P. Sansom. Nothing to sneeze at: a dynamic and integrative computational model of an influenza A virion. Structure, 23:584-597, 2015. Article
 
[3] T. Reddy, M.S.P. Sansom. The role of the membrane in the structure and biophysical robustness of the dengue virion envelope. Structure 24:375-382, 2016. https://doi.org/10.1016/j.str.2015.12.011
 
[4] R.G. Huber, J.K. Marzinek, P.L.S. Boon, W. Yue, P.J. Bond. Computational modelling of flavivirus dynamics: The ins and outs. Methods 185:28-38, 2021. https://doi.org/10.1016/j.ymeth.2020.06.004
 
[5] E.E. Jefferys, M.S.P. Sansom. Computational virology: molecular simulations of virus dynamics and interactions. Physical Virology, 201-233, 2019. https://doi.org/10.1007/978-3-030-14741-9_10
 
[6] J.K. Marzinek, R.G. Huber, P.J. Bond. Multiscale modelling and simulation of viruses. Current Opinion in Structural Biology 61, 146-152, 2020. https://doi.org/10.1016/j.sbi.2019.12.019
 
[7] W. Pezeshkian, F. Grünewald, O. Narykov, S. Lu, T.A. Wassenaar, S.J. Marrink, D. Korkin. Molecular architecture of SARS-CoV-2 envelope by integrative modeling. Structure 31 (4), 492-503, 2023. doi:10.1016/j.str.2023.02.006

IDPs / LLPS

F5.medium.gifIntrinsically disorder proteins (IDPs) are an important class of proteins. Traditionally, the application of Martini to study IDPs has been limited due to the constraints in 2ndary structure imposed by an elastic network. However, the use of Go-potentials [1] as well as the use of SAXS constraints [2] offers more possibilities to capture the broad range of configurations characteristic of IDPs.

Related to the ability to simulate IDPs comes the potential to simulate liquid-lquid phase separation (LLPS) of a variety of (bio)polymers. LLPS is gaining a lot of attention lately as it is believed to underly formation of membraneless organelles in the cell. The first examples of LLPS with Martini are starting to appear in the literature [3-7], with many more expected to follow.

[1] A.B. Poma, M. Cieplak, P.E Theodorakis. Combining the MARTINI and Structure-Based Coarse-Grained Approaches for the Molecular Dynamics Studies of Conformational Transitions in Proteins. J. Chem. Theory Comput. 13:1366–1374, 2017.

[2] A.H. Larsen, Y. Wang, S. Bottaro, S. Grudinin, L. Arleth, K. Lindorff-Larsen. Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution. PLoS Comput. Biol.16:e1007870, 2020.

[3] D. Priftis, M. Tirrell. Phase behaviour and complex coacervation of aqueous polypeptide solutions. Soft Matter 8:9396–9405, 2012.

[4] N.M. Milkovic, F.E. Thomasen, et al. Interplay of folded domains and the disordered low-complexity domain in mediating hnRNPA1 phase separation. BioRxiv, 2020. link

[5] T.J. Welsh, G. Krainer, J.R. Espinosa et al. Surface electrostatics govern the emulsion stability of biomolecular condensates. BioRxiv, 2020. https://doi.org/10.1101/2020.04.20.047910

[6] Z. Benayad, S. von Bülow, L.S. Stelzl, G. Hummer. Simulation of FUS protein condensates with an adapted coarse-grained model. BioRxiv, 2020. https://doi.org/10.1101/2020.10.10.334441

[7] M. Tsanai, P.W.J.M. Frederix, C.F.E. Schroer, P.C.T. Souza, S.J. Marrink. Coacervate formation studied by explicit solvent coarse-grain molecular dynamics with the Martini model. Chemical Science, 2021. pubs.rsc.org/en/content/art

Supramolecular Aggregates

In bionanotechnology, the field of creating functional materials consisting of bio-inspired molecules, the function and shape of a nanostructure only appear through the assembly of many small molecules together. The large number of building blocks required to define a nanostructure combined with the many degrees of freedom in packing small molecules has long precluded molecular simulations, but recent advances in computational hardware as well as software have made classical simulations available to this strongly expanding field, reviewed in [1].

The Martini model is increasingly contributing to this field, as the neglect of atomistic degrees of freedom allows to study supramolecular assemblies at spatiotemporal scales not accessible with all-atom models [2]. Key examples include the Martini simulations of self-assembly of (functionalized) peptide hydrogels [3,4], supramolecular polymers [5,6], self-replicating nanorings [7], and light harvesting nanotubes [8].

[1] P.W.J.M. Frederix, I. Patmanidis, S.J. Marrink. Molecular simulations of self-assembling bio-inspired supramolecular systems and their connection to experiments. Chem. Soc. Review, 47:3470 - 3489, 2018. doi:10.1039/C8CS00040A

[2] R. Alessandri, F. Grünewald, S.J. Marrink. Martini Perspective in Materials Science, Adv. Materials 2021. https://doi.org/10.1002/adma.202008635

[3] P.W.J.M. Frederix, G.G. Scott, Y.M. Abul-Haija, D. Kalafatovic, C.G. Pappas, .et al. Exploring the sequence space for (tri-) peptide self-assembly to design and discover new hydrogels. Nature Chemistry 7:30, 2015.

[4] E.R. Draper, B. Dietrich, K. McAulay, C. Brasnett, H. Abdizadeh, I. Patmanidis, et al. Using Small-Angle Scattering and Contrast Matching to Understand Molecular Packing in Low Molecular Weight Gels. Matter 2:764-778, 2020. https://doi.org/10.1016/j.matt.2019.12.028

[5] D. Bochicchio, G.M. Pavan. From cooperative self-assembly to water-soluble supramolecular polymers using coarse-grained simulations. ACS nano 11:1000-1011, 2017. https://doi.org/10.1021/acsnano.6b07628

[6] A. Sarkar, R. Sasmal, C. Empereur-Mot, D. Bochicchio, S.V.K. Kompella, et al. Self-Sorted, Random, and Block Supramolecular Copolymers via Sequence Controlled, Multicomponent Self-Assembly. J. Amer. Chem. Soc. 142:7606-7617, 2020. https://doi.org/10.1021/jacs.0c01822

[7] S. Maity, J. Ottelé, G.M. Santiago, P.W.J.M. Frederix, P. Kroon, O. Markovitch, M.C.A. Stuart, S.J. Marrink, S. Otto, W.H Roos. Caught in the act: mechanistic insight into supramolecular polymerization-driven self-replication from real-time visualization. J. Amer. Chem. Soc. 142:13709–13717, 2020. doi:org/10.1021/jacs.0c02635

[8] I. Patmanidis, P.C.T. Souza, S. Sami, R.W.A. Havenith, A.H. de Vries, S.J. Marrink. Modelling structural properties of cyanine dye nanotubes at coarse-grained level. Nanoscale Advances 4, 3033 - 3042, 2022. https://pubs.rsc.org/en/content/articlelanding/2022/na/d2na00158f

Nucleotides

ribosome-Martini.jpgPolynucleotides (DNA, RNA) form a fundamental class of biomolecules. Given their importance, Martini models for both DNA [1] and RNA [2, see Figure depicting a Martini ribosome] have been developed. Parameters for nucleotide cofactors are also available [3,4], as well as a dedicated tool to analyze nucleoside packing [5]. Given the limitations of Martini with respect to modeling the directionality of hydrogen bonds, nucleotide folding and hybridization events can not be captured. As for the protein model, an elastic network is required to keep the overall structure in place.

Despite this limitation, Martini can be used to study the interaction of nucleotides with other biomolecules, for instance, a DNA origami nanopore in a lipid membrane [6], DNA scaffolded nanodiscs [7], DNA-lipid formulations (lipoplexes) for gene delivery [8,9], or complexation of polymers with RNA [10] and DNA [11]. Recently, whole-cell genome has been modeled with Martini [13].

The Martini nucleotide force field has also been integrated into the HADDOCK framework to predict protein-DNA interfaces [12].

[1] J.J. Uusitalo, H.I. Ingólfsson, P. Akhshi, D.P. Tieleman, S.J. Marrink.Martini coarse-grained force field: extension to DNA. JCTC 11:3932-3945, 2015. open access

[2] J.J. Uusitalo, H.I. Ingólfsson, S.J. Marrink, I. Faustino. Martini coarse-grained force field: extension to RNA. Biophys. J., 113:246-256, 2017. abstract

[3] F.M. Sousa, L.M.P. Lima, C. Arnarez, M.M. Pereira, M.N. Melo. Coarse-Grained Parameterization of Nucleotide Cofactors and Metabolites: Protonation Constants, Partition Coefficients, and Model Topologies. J. Chem. Inform. Modeling 61:335-346, 2021. DOI: 10.1021/acs.jcim.0c01077t

[4] C.F.E. Schroer, L. Baldauf, L. van Buren, T.A. Wassenaar, M.N. Melo, G. Koenderink, S.J. Marrink. Charge-dependent interactions of monomeric and filamentous actin with lipid bilayers. PNAS,

[5] I. Faustino, S.J. Marrink. cgHeliParm: analysis of dsDNA helical parameters for coarse-grained MARTINI molecular dynamics simulations. Bioinformatics, 33:3813-3815, 2017. abstract

[6] V. Maingi, J.R. Burns, J.J. Uusitalo, S. Howorka, S.J. Marrink, M.S.P. Sansom. Stability and dynamics of membrane-spanning DNA nanopores. Nature Comm. 8:14784, 2017. open access

[7]  V. Maingi, P.W.K. Rothemund. Properties of DNA- and Protein-Scaffolded Lipid Nanodiscs. ACS Nano 15:751–764, 2021. https://doi.org/10.1021/acsnano.0c07128

[8] B.M.H. Bruininks, P.C.T. Souza, S.J. Marrink. A Practical View of the Martini Force Field. Biomolecular Simulations, 105-127, 2019. doi:10.1007/978-1-4939-9608-7_5 , pdf-reprint.

[9] B.M.H. Bruininks, P.C.T. Souza, H. Ingolfsson, S.J. Marrink.  A molecular view on the escape of lipoplexed DNA from the endosome. eLife,  9:e52012, 2020. doi.org/10.7554/eLife.52012

[10] F. Stojceski, G. Grasso, L. Pallante, A. Danani. Molecular and Coarse-Grained Modeling to Characterize and Optimize Dendrimer-Based Nanocarriers for Short Interfering RNA Delivery. ACS Omega 5:2978-2986, 2020.
DOI: 10.1021/acsomega.9b03908

[11] S. Mahajan, T. Tang. Polyethylenimine–DNA Ratio Strongly Affects Their Nanoparticle Formation: A Large-Scale Coarse-Grained Molecular Dynamics Study. JPC-B  123:9629-9640, 2019
DOI: 10.1021/acs.jpcb.9b07031

[12] R.V. Honorato, J. Roel-Touris, A.M.J.J. Bonvin. MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing. Front. Mol. Biosci., 2019. https://doi.org/10.3389/fmolb.2019.00102

[13] B.R. Gilbert, Z.R. Thornburg, T.A. Brier, J.A. Stevens, F. Grunewald, J.E. Stone, S.J. Marrink, Z.A. Luthey-Schulten. Dynamics of Chromosome Organization in a Minimal Bacterial Cell. Front. Cell Dev. Biol., 2023, online. doi:10.3389/fcell.2023.1214962

Realistic Cell Membranes

mitochondrion-molecular-resolution2.jpgBiomembranes are essential cellular components. Together with membrane-adhered structures, such as the cytoskeleton, cell membranes constitute incredibly heterogeneous and crowded environments, containing hundreds of different lipid types and being densely packed with a large variety of membrane proteins. They provide identity not only to the cell as a whole, through the enveloping plasma membrane, but also to many internal organelles.

The Martini model is highly suited to capture the complexity of such cell membranes, as recently reviewed in [1]. Available tools such as Insane [2] and Charmm-GUI [3,4] facilitate setting up cell membranes with arbitrary complex compositions. Current highlights include the >60 lipid mixture representing mammalian plasma membranes [5-7], mixtures of galactolipids modeling the thylakoid membranes [8], the membranes of an enitre mitochondrion with realistic composition as well as shape [9, see Figure], and multi-component bacterial membranes [10].

The ability to simulate membranes with realistic lipid compositions is now enabling researchers to study the interaction of a variety of proteins and other compounds with such membranes, e.g. [11-14].

[1] S.J. Marrink, V. Corradi, P.C.T. Souza, H.I. Ingolfsson, D.P. Tieleman, M.S.P. Sansom. Computational Modeling of Realistic Cell Membranes. Chem. Review, 119:6184–6226, 2019. doi:10.1021/acs.chemrev.8b00460

[2] T.A. Wassenaar, H.I. Ingólfsson, R.A. Böckmann, D.P. Tieleman, S.J. Marrink. Computational lipidomics with insane: a versatile tool for generating custom membranes for molecular simulations. JCTC, 11:2144–2155, 2015. abstract

[3] Y. Qi, H.I. Ingólfsson, X. Cheng, J. Lee, S.J. Marrink, W. Im. CHARMM-GUI Martini Maker for coarse-grained simulations with the Martini force field. JCTC, 11:4486–4494, 2015. abstract

[4] P.C. Hsu, B.M.H. Bruininks, D. Jefferies, P.C. Telles de Souza, J. Lee, D.S. Patel, S.J .Marrink, Y. Qi, S. Khalid, W. Im. CHARMM‐GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides. J. Comput. Chem., 38:2354–2363, 2017. abstract

[5] H.I. Ingólfsson, M.N. Melo, F.J. van Eerden, C. Arnarez, C.A. López, T.A. Wassenaar, X. Periole, A.H. De Vries, D.P. Tieleman, S.J. Marrink. Lipid organization of the plasma membrane. JACS, 136:14554-14559, 2014. open access

[6] H.I. Ingólfsson, T.S. Carpenter, H. Bhatia, P.T. Bremer, S.J. Marrink, F.C. Lightstone. Computational Lipidomics of the Neuronal Plasma Membrane. Biophys. J. 113:2271–2280, 2017. open access

[7] S. Thallmair, H.I. Ingólfsson, S.J. Marrink. Cholesterol Flip-Flop Impacts Domain Registration in Plasma Membrane Models. J. Phys. Chem. Lett. 9:5527–5533, 2018. doi:10.1021/acs.jpclett.8b01877

[8] F.J. van Eerden, D.H. de Jong, A.H de Vries, T.A. Wassenaar, S.J. Marrink. Characterization of thylakoid lipid membranes from cyanobacteria and higher plants by molecular dynamics simulations. BBA Biomembranes, 1848:1319–1330, 2015. abstract

[9] W. Pezeshkian, M. Konig, T.A. Wassenaar, S.J. Marrink. Backmapping triangulated surfaces to coarse-grained membrane models. Nature Commun. 11:2296, 2020. doi.org/10.1038/s41467-020-16094-y

[10] P.C. Hsu, F. Samsudin, J. Shearer, S. Khalid. It Is Complicated: Curvature, Diffusion, and Lipid Sorting within the Two Membranes of Escherichia coli. JPC-Lett. 8 (22), 5513-5518, 2017.

[11] V. Corradi, E. Mendez-Villuendas, H.I. Ingólfsson, R.X. Gu, I. Siuda, M.N. Melo, A. Moussatova, L.J. DeGagné, B.I. Sejdiu, G. Singh, T.A. Wassenaar, K. Delgado Magnero, S.J. Marrink, D.P. Tieleman. Lipid–Protein Interactions Are Unique Fingerprints for Membrane Proteins. ACS Central Science 4:709–717, 2018. doi:10.1021/acscentsci.8b00143

[12] S. Thallmair, P.A. Vainikka, S.J. Marrink. Lipid Fingerprints and Cofactor Dynamics of Light-Harvesting Complex II in Different Membranes. Biophys. J., 116:1446-1455, 2019. doi:10.1016/j.bpj.2019.03.009

[13] J. Shearer, D. Jefferies, S .Khalid. Outer membrane proteins OmpA, FhuA, OmpF, EstA, BtuB, and OmpX have unique lipopolysaccharide fingerprints. J. Chemical Theory and Computation 15 (4), 2608-2619, 2019.

[14] A. Buyan, C.D. Cox, J. Barnoud, J. Li, H.S.M. Chan, B. Martinac, S.J. Marrink, B. Corry. Piezo1 forms specific, functionally important interactions with phosphoinositides and cholesterol. Biophys. J. 119:1683-1697, 2020. doi.10.1016/j.bpj.2020.07.043