Materials Informatics

Senderowitz, Hanoch; Tropsha, Alexander

doi:10.1021/acs.jcim.8b00927

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Ever since the conception of the Protein Data Bank (PDB) , and Genbank, , open access to standardized and searchable pools of experimental data has revolutionized scientific research. Constantly growing and improving in fidelity due to collaborative effort, − the now hundreds of databanks fuel the data-driven development of biomolecular structure determination, refinement, prediction, and design approaches as well as the development of drugs, , materials, , and more. , It is clear that open data enables scientific progress that is far beyond the resources of a single research group or institute. Consequently, the call for public availability and conservation of data has extended to molecular dynamics (MD) simulation trajectories of biomolecules, − and the discussion on how and by whom such databanks for dynamic structures would be set up is currently active. − While there are currently no general MD databanks in operation, individual databanks are accepting contributions on nucleic acid, protein/DNA/RNA, cyclodextrin, G-protein-coupled receptor, and lipid bilayer simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Ever since the conception of the Protein Data Bank (PDB) 1,2 and Genbank, 3,4 open access to standardized and searchable pools of experimental data has revolutionized scientific research. Constantly growing and improving in fidelity due to collaborative effort, 5−8 the now hundreds of databanks 9 fuel the data-driven development of biomolecular structure determination, 10 refinement, 11 prediction, 12 and design 13 approaches as well as the development of drugs, 14,15 materials, 16,17 and more. 18,19 It is clear that open data enables scientific progress that is far beyond the resources of a single research group or institute.…”

Section: ■ Introductionmentioning

confidence: 99%

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Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics

Antila

Ferreira

Ollila

et al. 2021

J. Chem. Inf. Model.

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Molecular dynamics (MD) simulations are widely used to monitor time-resolved motions of biomacromolecules, although it often remains unknown how closely the conformational dynamics correspond to those occurring in real life. Here, we used a large set of open-access MD trajectories of phosphatidylcholine (PC) lipid bilayers to benchmark the conformational dynamics in several contemporary MD models (force fields) against nuclear magnetic resonance (NMR) data available in the literature: effective correlation times and spin–lattice relaxation rates. We found none of the tested MD models to fully reproduce the conformational dynamics. That said, the dynamics in CHARMM36 and Slipids are more realistic than in the Amber Lipid14, OPLS-based MacRog, and GROMOS-based Berger force fields, whose sampling of the glycerol backbone conformations is too slow. The performance of CHARMM36 persists when cholesterol is added to the bilayer, and when the hydration level is reduced. However, for conformational dynamics of the PC headgroup, both with and without cholesterol, Slipids provides the most realistic description because CHARMM36 overestimates the relative weight of ∼1 ns processes in the headgroup dynamics. We stress that not a single new simulation was run for the present work. This demonstrates the worth of open-access MD trajectory databanks for the indispensable step of any serious MD study: benchmarking the available force fields. We believe this proof of principle will inspire other novel applications of MD trajectory databanks and thus aid in developing biomolecular MD simulations into a true computational microscope—not only for lipid membranes but for all biomacromolecular systems.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics

Antila

Ferreira

Ollila

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Ever since the conception of Protein Data Bank (PDB) 1,2 and GenBank, 3,4 open access to standardised and searchable pools of experimental data has revolutionized scientific research. Constantly growing and improving in fidelity due to collaborative effort, 5–8 the now hundreds of databanks 9 fuel the data-driven development of biomolecular structure determination, 10 refinement, 11 prediction, 12 and design 13 approaches, as well as development of drugs, 14,15 materials, 16,17 and more. 18,19 It is clear that open data enables scientific progress that is far beyond the resources of a single research group or institute.…”

Section: Introductionmentioning

confidence: 99%

Using open data to rapidly benchmark biomolecular simulations: Phospholipid conformational dynamics

Antila

Ferreira

Ollila

et al. 2020

Preprint

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Molecular dynamics (MD) simulations are widely used to monitor time-resolved motions of biomacromolecules, although it often remains unknown how closely the conformational dynamics correspond to those occurring in real life. Here, we used a large set of open-access MD trajectories of phosphatidylcholine (PC) lipid bilayers to benchmark the conformational dynamics in several contemporary MD models (force fields) against nuclear magnetic resonance (NMR) data available in the literature: effective correlation times and spin-lattice relaxation rates.We found none of the tested MD models to fully reproduce the conformational dynamics. That said, the dynamics in CHARMM36 and Slipids are more realistic than in the Amber Lipid14, OPLS-based MacRog, and GROMOS-based Berger force fields, whose sampling of the glycerol backbone conformations is too slow. The performance of CHARMM36 persists when cholesterol is added to the bilayer, and when the hydration level is reduced. However, for conformational dynamics of the PC headgroup, both with and without cholesterol, Slipids provides the most realistic description, because CHARMM36 overestimates the relative weight of ~1-ns processes in the headgroup dynamics.We stress that not a single new simulation was run for the present work. This demonstrates the worth of open-access MD trajectory databanks for the indispensable step of any serious MD study: Benchmarking the available force fields. We believe this proof of principle will inspire other novel applications of MD trajectory databanks, and thus aid in developing biomolecular MD simulations into a true computational microscope—not only for lipid membranes, but for all biomacromolecular systems.Graphical TOC Entry

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“…Multi-element materials are often investigated by machine learning, which is used because of its ability for multi-dimensional analysis [7][8][9][10][11][12][13][14][15][16][17] . It is noteworthy that ML has already been used to develop materials for magnets 18,19 , batteries 20,21 , superconductors 22,23 , ferroelectrics 24,25 , thermoelectrics 26,27 and photovoltaics 28,29 .…”

mentioning

confidence: 99%

Identification of stable alloy with giant magnetization via machine learning based autonomous materials search

Iwasaki¹,

Sawada²,

Saitoh³

et al. 2020

Preprint

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Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe3Co1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element alloy system has been expected to include an alloy with magnetization beyond that of Fe3Co1, but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion in the multi-element system. In this work, we found a new alloy with giant magnetization beyond that of Fe3Co1 via an autonomous materials search system combining machine learning and ab-initio calculation. After autonomous/automated exploration in the large material space of multi-element alloy for six weeks, the system unexpectedly indicated that Ir and/or Pt impurities would enhance the magnetization of FeCo alloys. To confirm this experimentally, we comprehensively synthesized FexCoyIr1-x-y and FexCoyPt1-x-y alloys and found that some of them have magnetization beyond that of Fe3Co1.

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Materials Informatics

Cited by 7 publications

References 16 publications

Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics

Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics

Using open data to rapidly benchmark biomolecular simulations: Phospholipid conformational dynamics

Identification of stable alloy with giant magnetization via machine learning based autonomous materials search

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