MiMiC: A high-performance framework for multiscale molecular dynamics simulations DOI
Andrej Antalík, Andrea Levy, Sonata Kvedaravičiūtė

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(2)

Published: July 11, 2024

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient flexible, we adopt an interoperable approach based on multiple-program multiple-data (MPMD) paradigm, serving as intermediary responsible fast data exchange interactions between the subsystems. The main goal to avoid interfering with underlying parallelization programs, including operability hybrid architectures (e.g., CPU/GPU), keep their setup execution close possible original. At moment, offers implementation electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling large biomolecules using CPMD GROMACS QM MM engines, respectively. However, designed high flexibility general models mind, can be straightforwardly extended beyond QM/MM. In this article, illustrate software design features framework, compelling choice upcoming era exascale high-performance computing.

Language: Английский

GōMartini 3: From large conformational changes in proteins to environmental bias corrections DOI Creative Commons
Paulo C. T. Souza, Luís Borges-Araújo,

Chris Brasnett

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 16, 2024

ABSTRACT Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency accurate representation of protein dynamics with the capabilities studying proteins in different biological environments. This paper introduces enhanced model, which a virtual-site implementation Gō models Martini 3. been extensively tested by community since release new version Martini. work demonstrates diverse case studies, ranging from protein-membrane binding protein-ligand interactions AFM force profile calculations. is also versatile, as it can address recent inaccuracies reported model. Lastly, discusses advantages, limitations, future perspectives 3 its combination models.

Language: Английский

Citations

21
Refining Ligand Poses in RNA/Ligand Complexes of Pharmaceutical Relevance: A Perspective by QM/MM Simulations and NMR Measurements DOI Creative Commons
Gia Linh Hoang, Manuel Röck,

Aldo Tancredi

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 1702 - 1708

Published: Feb. 10, 2025

Predicting the binding poses of ligands targeting RNAs is challenging. Here, we propose that using first-principles quantum mechanics/molecular mechanics (QM/MM) simulations, which incorporate automatically polarization effects, can help refine structural determinants ligand/RNA complexes in aqueous solution. In fact, recent advances massively parallel computer architectures (such as exascale machines), combined with power machine learning, are greatly expanding domain applicability these types notoriously expensive simulations. We corroborate this proposal by carrying out a QM/MM-based study on ligand CAG repeat-RNA, involved Huntington's disease. The calculations indeed show clear improvement properties, and they consistent NMR measurements, also performed here. Thus, type approach may be useful for practical applications design RNA near future.

Language: Английский

Citations

0
GōMartini 3: From large conformational changes in proteins to environmental bias corrections DOI Creative Commons
Paulo C. T. Souza, Luís Borges-Araújo, Christopher Brasnett

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 30, 2025

Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency accurate representation of protein dynamics with the capabilities studying proteins in different biological environments. This paper introduces enhanced model, which a virtual-site implementation Gō models Martini 3. been extensively tested by community since release reparametrized version Martini. work demonstrates diverse case studies, ranging from protein-membrane binding protein-ligand interactions AFM force profile calculations. is also versatile, as it can address recent inaccuracies reported model. Lastly, discusses advantages, limitations, future perspectives 3 its combination models.

Language: Английский

Citations

0
Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity DOI Creative Commons

Giorgio Bonollo,

Gauthier Trèves,

Д. В. Комаров

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 3606 - 3615

Published: April 3, 2025

Proteins and protein complexes form adaptable networks that regulate essential biochemical pathways define cell phenotypes through dynamic mechanisms interactions. Advances in structural biology molecular simulations have revealed how systems respond to changes their environments, such as ligand binding, stress conditions, or perturbations like mutations post-translational modifications, influencing signal transduction cellular phenotypes. Here, we discuss computational approaches, ranging from dynamics (MD) AI-driven methods, are instrumental studying isolated molecules large assemblies. These techniques elucidate conformational landscapes, ligand-binding mechanisms, protein-protein interactions starting support the construction of multiscale realistic representations highly complex systems, up whole models. With cryo-electron microscopy, tomography, AlphaFold accelerating characterization networks, suggest integrating AI Machine Learning with MD methods will enhance fundamental understating for ever-increasing complexity, usher exciting possibilities predictive modeling behavior compartments even cells. advances indeed transforming biophysics chemical biology, offering new opportunities study biomolecular at atomic resolution.

Language: Английский

Citations

0
MiMiC: A high-performance framework for multiscale molecular dynamics simulations DOI
Andrej Antalík, Andrea Levy, Sonata Kvedaravičiūtė

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(2)

Published: July 11, 2024

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient flexible, we adopt an interoperable approach based on multiple-program multiple-data (MPMD) paradigm, serving as intermediary responsible fast data exchange interactions between the subsystems. The main goal to avoid interfering with underlying parallelization programs, including operability hybrid architectures (e.g., CPU/GPU), keep their setup execution close possible original. At moment, offers implementation electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling large biomolecules using CPMD GROMACS QM MM engines, respectively. However, designed high flexibility general models mind, can be straightforwardly extended beyond QM/MM. In this article, illustrate software design features framework, compelling choice upcoming era exascale high-performance computing.

Language: Английский

Citations

1