PPubMed21 Feb 2024

Introduction to the <i> RSC Advances </i> themed collection on <i> New insights into biomolecular systems from large-scale simulations </i>

Megan L. O’Mara, Sarah Rauscher, Stacey D. Wetmore

Abstract

Megan O’Mara, Sarah Rauscher and Stacey Wetmore introduce the RSC Advances themed collection on New insights into biomolecular systems from large-scale simulations.

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The RSC Advances collection showcases large‑scale computational simulations revealing biomolecular mechanisms from nucleic acids to protein complexes.

What’s new

i.MD simulations coupled with TD‑DFT elucidate nucleic acid base pairing and stacking
ii.Simulation of a 1.2 million‑atom photosynthesis supercomplex reveals self‑organization mechanisms
iii.Machine‑learning models predict natural frequency spectra of proteins without full atomistic structures
iv.Classical atomistic MD and hybrid QM/MM techniques uncover the sulfilimine bond formation in collagen IV
v.Unbiased MD and Markov state models develop permeability models for cyclic peptides through lipid bilayers

How they did it

The collection highlights studies that employ a range of computational techniques, including molecular dynamics, time‑dependent density functional theory, quantum mechanics/molecular mechanics, multi‑conformation continuum electrostatics, coarse‑grained models, Markov state models, and data‑driven machine‑learning approaches. These methods are used to simulate biomolecular systems across multiple length and time scales, often validated against experimental data.

What they found

Simulation of a 1.2 million‑atom photosynthesis supercomplex revealed molecular underpinnings of self‑organization and regulation
MD + TD‑DFT provided detailed insights into nucleic acid base pairing and stacking interactions
Machine‑learning model accurately predicted protein natural frequency spectra without requiring full atomistic structures

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