HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations
CoRR(2024)
摘要
Molecular simulations have assumed a paramount role in the fields of
chemistry, biology, and material sciences, being able to capture the intricate
dynamic properties of systems. Within this realm, coarse-grained (CG)
techniques have emerged as invaluable tools to sample large-scale systems and
reach extended timescales by simplifying system representation. However, CG
approaches come with a trade-off: they sacrifice atomistic details that might
hold significant relevance in deciphering the investigated process. Therefore,
a recommended approach is to identify key CG conformations and process them
using backmapping methods, which retrieve atomistic coordinates. Currently,
rule-based methods yield subpar geometries and rely on energy relaxation,
resulting in less-than-optimal outcomes. Conversely, machine learning
techniques offer higher accuracy but are either limited in transferability
between systems or tied to specific CG mappings. In this work, we introduce
HEroBM, a dynamic and scalable method that employs deep equivariant graph
neural networks and a hierarchical approach to achieve high-resolution
backmapping. HEroBM handles any type of CG mapping, offering a versatile and
efficient protocol for reconstructing atomistic structures with high accuracy.
Focused on local principles, HEroBM spans the entire chemical space and is
transferable to systems of varying sizes. We illustrate the versatility of our
framework through diverse biological systems, including a complex real-case
scenario. Here, our end-to-end backmapping approach accurately generates the
atomistic coordinates of a G protein-coupled receptor bound to an organic small
molecule within a cholesterol/phospholipid bilayer.
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