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Siewert J. Marrink - One of the best experts on this subject based on the ideXlab platform.
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The MARTINI Model in Materials Science
arXiv: Materials Science, 2020Co-Authors: Riccardo Alessandri, Fabian Grünewald, Siewert J. MarrinkAbstract:The MARTINI model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here we highlight the main applications to date of the MARTINI model in materials science, and we give a perspective for the future developments in this field, in particular in light of recent developments such as the new version of the model, MARTINI 3.
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capturing choline aromatics cation π interactions in the MARTINI force field
Journal of Chemical Theory and Computation, 2020Co-Authors: Hanif Muhammad Khan, Siewert J. Marrink, Alex H. De Vries, Paulo C T Souza, Sebastian Thallmair, Jonathan Barnoud, Nathalie ReuterAbstract:Cation-π interactions play an important role in biomolecular recognition, including interactions between membrane phosphatidylcholine lipids and aromatic amino acids of peripheral proteins. While molecular mechanics coarse grain (CG) force fields are particularly well suited to simulate membrane proteins in general, they are not parameterized to explicitly reproduce cation-π interactions. We here propose a modification of the polarizable MARTINI coarse grain (CG) model enabling it to model membrane binding events of peripheral proteins whose aromatic amino acid interactions with choline headgroups are crucial for their membrane binding. For this purpose, we first collected and curated a dataset of eight peripheral proteins from different families. We find that the MARTINI CG model expectedly underestimates aromatics-choline interactions and is unable to reproduce membrane binding of the peripheral proteins in our dataset. Adjustments of the relevant interactions in the polarizable MARTINI force field yield significant improvements in the observed binding events. The orientation of each membrane-bound protein is comparable to reference data from all-atom simulations and experimental binding data. We also use negative controls to ensure that choline-aromatics interactions are not overestimated. We finally check that membrane properties, transmembrane proteins, and membrane translocation potential of mean force (PMF) of aromatic amino acid side-chain analogues are not affected by the new parameter set. This new version "MARTINI 2.3P" is a significant improvement over its predecessors and is suitable for modeling membrane proteins including peripheral membrane binding of peptides and proteins.
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Capturing Choline–Aromatics Cation−π Interactions in the MARTINI Force Field
Journal of chemical theory and computation, 2020Co-Authors: Hanif Muhammad Khan, Siewert J. Marrink, Alex H. De Vries, Paulo C T Souza, Sebastian Thallmair, Jonathan Barnoud, Nathalie ReuterAbstract:Cation-π interactions play an important role in biomolecular recognition, including interactions between membrane phosphatidylcholine lipids and aromatic amino acids of peripheral proteins. While molecular mechanics coarse grain (CG) force fields are particularly well suited to simulate membrane proteins in general, they are not parameterized to explicitly reproduce cation-π interactions. We here propose a modification of the polarizable MARTINI coarse grain (CG) model enabling it to model membrane binding events of peripheral proteins whose aromatic amino acid interactions with choline headgroups are crucial for their membrane binding. For this purpose, we first collected and curated a dataset of eight peripheral proteins from different families. We find that the MARTINI CG model expectedly underestimates aromatics-choline interactions and is unable to reproduce membrane binding of the peripheral proteins in our dataset. Adjustments of the relevant interactions in the polarizable MARTINI force field yield significant improvements in the observed binding events. The orientation of each membrane-bound protein is comparable to reference data from all-atom simulations and experimental binding data. We also use negative controls to ensure that choline-aromatics interactions are not overestimated. We finally check that membrane properties, transmembrane proteins, and membrane translocation potential of mean force (PMF) of aromatic amino acid side-chain analogues are not affected by the new parameter set. This new version "MARTINI 2.3P" is a significant improvement over its predecessors and is suitable for modeling membrane proteins including peripheral membrane binding of peptides and proteins.
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charmm gui MARTINI maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides
Journal of Computational Chemistry, 2017Co-Authors: Pinchia Hsu, Siewert J. Marrink, Jumin Lee, Bart M H Bruininks, Damien Jefferies, Paulo C T Souza, Dhilon S Patel, Syma KhalidAbstract:A complex cell envelope, composed of a mixture of lipid types including lipopolysaccharides, protects bacteria from the external environment. Clearly, the proteins embedded within the various components of the cell envelope have an intricate relationship with their local environment. Therefore, to obtain meaningful results, molecular simulations need to mimic as far as possible this chemically heterogeneous system. However, setting up such systems for computational studies is far from trivial, and consequently the vast majority of simulations of outer membrane proteins still rely on oversimplified phospholipid membrane models. This work presents an update of CHARMM-GUI MARTINI Maker for coarse-grained modeling and simulation of complex bacterial membranes with lipopolysaccharides. The qualities of the outer membrane systems generated by MARTINI Maker are validated by simulating them in bilayer, vesicle, nanodisc, and micelle environments (with and without outer membrane proteins) using the MARTINI force field. We expect this new feature in MARTINI Maker to be a useful tool for modeling large, complicated bacterial outer membrane systems in a user-friendly manner. © 2017 Wiley Periodicals, Inc.
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CHARMM‐GUI MARTINI Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides
Journal of computational chemistry, 2017Co-Authors: Pinchia Hsu, Siewert J. Marrink, Jumin Lee, Bart M H Bruininks, Damien Jefferies, Paulo C T Souza, Dhilon S Patel, Syma KhalidAbstract:A complex cell envelope, composed of a mixture of lipid types including lipopolysaccharides, protects bacteria from the external environment. Clearly, the proteins embedded within the various components of the cell envelope have an intricate relationship with their local environment. Therefore, to obtain meaningful results, molecular simulations need to mimic as far as possible this chemically heterogeneous system. However, setting up such systems for computational studies is far from trivial, and consequently the vast majority of simulations of outer membrane proteins still rely on oversimplified phospholipid membrane models. This work presents an update of CHARMM-GUI MARTINI Maker for coarse-grained modeling and simulation of complex bacterial membranes with lipopolysaccharides. The qualities of the outer membrane systems generated by MARTINI Maker are validated by simulating them in bilayer, vesicle, nanodisc, and micelle environments (with and without outer membrane proteins) using the MARTINI force field. We expect this new feature in MARTINI Maker to be a useful tool for modeling large, complicated bacterial outer membrane systems in a user-friendly manner. © 2017 Wiley Periodicals, Inc.
Ilpo Vattulainen - One of the best experts on this subject based on the ideXlab platform.
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Umbrella Sampling Simulations of TM Domain Dimerization
2018Co-Authors: Javanainen Matti, Hector Martinez-seara, Ilpo VattulainenAbstract:Umbrella sampling simulations of dimer formation of 2 TM domain dimers in DMPC/DLPC bilayers. Simulations are performed in the coarse-grained scheme using different force fields; normal MARTINI (N), MARTINI with all protein–protein interactions scaled (U) by 10% (U_10) or 20% (U_20), MARTINI with interactions among water-interacting beads scaled (W) either by 60% (W_60), 80% (W_80), or 90% (W_90), or the polarizable MARTINI (P). The original MARTINI is also repeated with an older set of 'common' simulation parameters (C). The tar files are named after the PDB codes of the corresponding dimer and the type of force field employed (see above). The tar files contain the run input files (.tpr) and the corresponding simulation parameter files (.mdp) for all umbrella windows; i.e. "EPHA_U_80_18.tpr" is the run input file for the EPHA dimer with all protein–protein interactions scaled down by 20% and with a protein–protein distance restrained to 18 Å by the umbrella potential. This tpr is generated from the simulation parameter file, the topology file (here EPHA_U_80.top), the index file (here EPHA.ndx), and the initial structure (here EPHA_start_18.gro, available in the EPHA-frames.tar). The index and initial structures for the polarizable model differ, and are provided in "EPHA-P.ndx" and "EPHA-frames-P.tar", respectively. All topologies (.itp) are provided in TOP.tar. The scaling is achieved by adding 'p' to the bead types in the proteins; either all types (scaling U) or to those more in contact with water than the membrane (scaling W) The corresponding parameters are given in the "MARTINI_v2.2_scaled_X.itp" file. Note that for uniform style, the unscaled parameters are given in a similar manner in a file "MARTINI_v2.2_unscaled.itp". Here, the .itp files follow the naming convention of the paper (see below) so that X=1 means downscaling of LJ epsilon by 10%, i.e. it corresponds to files with "_10". For a more thorough explanation of the purporse of the files and the simulation parameters, see the related publication:
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Excessive aggregation of membrane proteins in the MARTINI model
PloS one, 2017Co-Authors: Matti Javanainen, Hector Martinez-seara, Ilpo VattulainenAbstract:The coarse-grained MARTINI model is employed extensively to study membrane protein oligomerization. While this approach is exceptionally promising given its computational efficiency, it is alarming that a significant fraction of these studies demonstrate unrealistic protein clusters, whose formation is essentially an irreversible process. This suggests that the protein-protein interactions are exaggerated in the MARTINI model. If this held true, then it would limit the applicability of MARTINI to study multi-protein complexes, as the rapidly clustering proteins would not be able to properly sample the correct dimerization conformations. In this work we first demonstrate the excessive protein aggregation by comparing the dimerization free energies of helical transmembrane peptides obtained with the MARTINI model to those determined from FRET experiments. Second, we show that the predictions provided by the MARTINI model for the structures of transmembrane domain dimers are in poor agreement with the corresponding structures resolved using NMR. Next, we demonstrate that the first issue can be overcome by slightly scaling down the MARTINI protein-protein interactions in a manner, which does not interfere with the other MARTINI interaction parameters. By preventing excessive, irreversible, and non-selective aggregation of membrane proteins, this approach renders the consideration of lateral dynamics and protein-lipid interactions in crowded membranes by the MARTINI model more realistic. However, this adjusted model does not lead to an improvement in the predicted dimer structures. This implicates that the poor agreement between the MARTINI model and NMR structures cannot be cured by simply uniformly reducing the interactions between all protein beads. Instead, a careful amino-acid specific adjustment of the protein-protein interactions is likely required.
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Umbrella Sampling Simulations of TM Domain Dimerization
2017Co-Authors: Javanainen Matti, Hector Martinez-seara, Ilpo VattulainenAbstract:Umbrella sampling simulations of dimer formation of 2 TM domain dimers in DMPC/DLPC bilayers. Simulations are performed in the coarse-grained scheme using different force fields; normal MARTINI (N), MARTINI with all protein–protein interactions scaled (U) by 10% (U_10) or 20% (U_20), MARTINI with interactions among water-interacting beads scaled (W) either by 60% (W_60), 80% (W_80), or 90% (W_90), or the polarizable MARTINI (P). The original MARTINI is also repeated with an older set of 'common' simulation parameters (C). The tar files are named after the PDB codes of the corresponding dimer and the type of force field employed (see above). The tar files contain the run input files (.tpr) and the corresponding simulation parameter files (.mdp) for all umbrella windows; i.e. "EPHA_U_80_18.tpr" is the run input file for the EPHA dimer with all protein–protein interactions scaled down by 20% and with a protein–protein distance restrained to 18 Å by the umbrella potential. This tpr is generated from the simulation parameter file, the topology file (here EPHA_U_80.top), the index file (here EPHA.ndx), and the initial structure (here EPHA_start_18.gro, available in the EPHA-frames.tar). The index and initial structures for the polarizable model differ, and are provided in "EPHA-P.ndx" and "EPHA-frames-P.tar", respectively. All topologies (.itp) are provided in TOP.tar. The scaling is achieved by adding 'p' to the bead types in the proteins; either all types (scaling U) or to those more in contact with water than the membrane (scaling W) The corresponding parameters are given in the "MARTINI_v2.2_scaled_X.itp" file. Note that for uniform style, the unscaled parameters are given in a similar manner in a file "MARTINI_v2.2_unscaled.itp". Here, the .itp files follow the naming convention of the paper (see below) so that X=1 means downscaling of LJ epsilon by 10%, i.e. it corresponds to files with "_10". For a more thorough explanation of the purporse of the files and the simulation parameters, see the related publication: Javanainen M, Martinez-Seara H, Vattulainen I (2017) Excessive aggregation of membrane proteins in the MARTINI model. PLoS ONE 12(11): e0187936. https://doi.org/10.1371/journal.pone.018793
J. D. Bast - One of the best experts on this subject based on the ideXlab platform.
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First Report of the Visceral Leishmaniasis Vector Phlebotomus MARTINI (Diptera: Psychodidae) in Tanzania
Journal of medical entomology, 2013Co-Authors: Jeffrey W. Clark, Elizabeth Kioko, N. Odemba, F. Ngere, J. Kamanza, E. Oyugi, G. Kerich, E. Kimbita, J. D. BastAbstract:ABSTRACT Phlebotomus MARTINI is a known vector of visceral leishmaniasis caused by Leishmania donovani in sub-Saharan Africa. The disease is known to be endemic in areas of north and south Sudan, Kenya, Ethiopia, Uganda, and Somalia but has not been reported from Tanzania. In this report we present the first documented collection of P. MARTINI and P. vansomerenae in Tanzania. Sand flies were collected using standard dry-ice baited CDC light traps (John W. Hock Company, Gainesville, FL) from five sampling sites in the Arusha and Kilimanjaro regions from 14 to 20 July 2010. Phlebotomus MARTINI was collected from all sites and represented 6.6% of the total identified sand flies. Phlebotomus MARTINI ranged from 4.5 to 9.4% of the total identified catch from the four sites in the Kilimanjaro region and 17.9% of the total identified catch at the one collection site in the Arusha region. In addition, one male specimen of the sibling species, Phlebotomus vansomerenae, was found at Chemka Springs in the Kilimanjar...
Tong Yang - One of the best experts on this subject based on the ideXlab platform.
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ICNP - MARTINI: Bridging the Gap between Network Measurement and Control Using Switching ASICs
2020 IEEE 28th International Conference on Network Protocols (ICNP), 2020Co-Authors: Shuhe Wang, Chen Sun, Zili Meng, Minhu Wang, Jiamin Cao, Qun Huang, Masoud Moshref, Tong YangAbstract:Advanced network management systems, including network measurement and traffic control, rely on a remote controller to make control decisions. However, this approach incurs a long control loop of a few seconds to minutes. Even if we switch to switch-local controller, the latency is still tens of milliseconds and is unacceptable for many latency-sensitive tasks. In this paper, we propose MARTINI, a general framework that supports measurement-based timely control. The key idea is to perform measurement, control decision, and control entirely in the switch data plane. This could shorten the control loop of management tasks that require timely control based on only locally measured statistics in the switch. First, MARTINI introduces a set of primitives to describe management tasks. Next, MARTINI provides an innovative network-wide task placement mechanism to exploit resources of all switches to accommodate massive management tasks. Finally, MARTINI provides a code library and a compiler to support measurement and control on a state-of-the-art switching ASIC. Evaluation results show that MARTINI can effectively support a wide range of fine-timescale management tasks such as microburst detection and fast load balancing by reducing the control loop from seconds to nanoseconds.
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MARTINI bridging the gap between network measurement and control using switching asics
International Conference on Network Protocols, 2020Co-Authors: Shuhe Wang, Chen Sun, Zili Meng, Minhu Wang, Jiamin Cao, Qun Huang, Masoud Moshref, Tong Yang, Gong ZhangAbstract:Advanced network management systems, including network measurement and traffic control, rely on a remote controller to make control decisions. However, this approach incurs a long control loop of a few seconds to minutes. Even if we switch to switch-local controller, the latency is still tens of milliseconds and is unacceptable for many latency-sensitive tasks. In this paper, we propose MARTINI, a general framework that supports measurement-based timely control. The key idea is to perform measurement, control decision, and control entirely in the switch data plane. This could shorten the control loop of management tasks that require timely control based on only locally measured statistics in the switch. First, MARTINI introduces a set of primitives to describe management tasks. Next, MARTINI provides an innovative network-wide task placement mechanism to exploit resources of all switches to accommodate massive management tasks. Finally, MARTINI provides a code library and a compiler to support measurement and control on a state-of-the-art switching ASIC. Evaluation results show that MARTINI can effectively support a wide range of fine-timescale management tasks such as microburst detection and fast load balancing by reducing the control loop from seconds to nanoseconds.
Shuhe Wang - One of the best experts on this subject based on the ideXlab platform.
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ICNP - MARTINI: Bridging the Gap between Network Measurement and Control Using Switching ASICs
2020 IEEE 28th International Conference on Network Protocols (ICNP), 2020Co-Authors: Shuhe Wang, Chen Sun, Zili Meng, Minhu Wang, Jiamin Cao, Qun Huang, Masoud Moshref, Tong YangAbstract:Advanced network management systems, including network measurement and traffic control, rely on a remote controller to make control decisions. However, this approach incurs a long control loop of a few seconds to minutes. Even if we switch to switch-local controller, the latency is still tens of milliseconds and is unacceptable for many latency-sensitive tasks. In this paper, we propose MARTINI, a general framework that supports measurement-based timely control. The key idea is to perform measurement, control decision, and control entirely in the switch data plane. This could shorten the control loop of management tasks that require timely control based on only locally measured statistics in the switch. First, MARTINI introduces a set of primitives to describe management tasks. Next, MARTINI provides an innovative network-wide task placement mechanism to exploit resources of all switches to accommodate massive management tasks. Finally, MARTINI provides a code library and a compiler to support measurement and control on a state-of-the-art switching ASIC. Evaluation results show that MARTINI can effectively support a wide range of fine-timescale management tasks such as microburst detection and fast load balancing by reducing the control loop from seconds to nanoseconds.
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MARTINI bridging the gap between network measurement and control using switching asics
International Conference on Network Protocols, 2020Co-Authors: Shuhe Wang, Chen Sun, Zili Meng, Minhu Wang, Jiamin Cao, Qun Huang, Masoud Moshref, Tong Yang, Gong ZhangAbstract:Advanced network management systems, including network measurement and traffic control, rely on a remote controller to make control decisions. However, this approach incurs a long control loop of a few seconds to minutes. Even if we switch to switch-local controller, the latency is still tens of milliseconds and is unacceptable for many latency-sensitive tasks. In this paper, we propose MARTINI, a general framework that supports measurement-based timely control. The key idea is to perform measurement, control decision, and control entirely in the switch data plane. This could shorten the control loop of management tasks that require timely control based on only locally measured statistics in the switch. First, MARTINI introduces a set of primitives to describe management tasks. Next, MARTINI provides an innovative network-wide task placement mechanism to exploit resources of all switches to accommodate massive management tasks. Finally, MARTINI provides a code library and a compiler to support measurement and control on a state-of-the-art switching ASIC. Evaluation results show that MARTINI can effectively support a wide range of fine-timescale management tasks such as microburst detection and fast load balancing by reducing the control loop from seconds to nanoseconds.
