Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.
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http://dx.doi.org/10.1021/acs.jctc.6b00712 | DOI Listing |
J Comput Chem
January 2025
Scuola Superiore Meridionale, Napoli, Italy.
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January 2025
Dipartimento di Scienze Matematiche, Informatiche e Fisiche (DMIF), University of Udine, 33100 Udine, Italy.
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View Article and Find Full Text PDFJ Phys Chem B
January 2025
Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States.
Macrocyclization or stapling is an important strategy for increasing the conformational stability and target-binding affinity of peptides and proteins, especially in therapeutic contexts. Atomistic simulations of such stapled peptides and proteins could help rationalize existing experimental data and provide predictive tools for the design of new stapled peptides and proteins. Standard approaches exist for incorporating nonstandard amino acids and functional groups into the force fields required for MD simulations and have been used in the context of stapling for more than a decade.
View Article and Find Full Text PDFSci Data
January 2025
IBM Research, Hursley, SO21 2JN, UK.
A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface.
View Article and Find Full Text PDFJ Phys Chem B
January 2025
Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, Via A. Moro 2, Siena 53100, Italy.
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