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Recent experiments have shown that enzyme activity can preserved in harsh environments by complexing enzyme with polymer into a Protein Polymer Hybrid (PPH). In a successful PPH, heteropolymer strands bind to the enzyme surface and restrain the folded protein without adversely affecting the binding and active sites. It is believed that hybridization is driven by noncovalent interactions at the enzyme surface including hydrophobicity and electrostatics. Molecular modeling of these interactions is not practical at the all atom scale due to the long timescales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse grained model in which one particle represents several atoms, significantly reducing the cost of simulation. In this study we present two coarse grained enzyme models, lipase and dehalogenase, prepared using a top down modeling strategy. We simulate each enzyme in aqueous solution and calculate statistics of protein surface features and shape descriptors. The values from the coarse grained data are compared with the same calculations performed on all atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small angle X ray scattering (SAXS) experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment.
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This content will become publicly available on August 21, 2025
Expanding density-correlation machine learning representations for anisotropic coarse-grained particles
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to increase the computational efficiency of simulation via coarse-graining or to understand molecular influences on system behavior. In such cases, atom-centered representations will have limited utility, as groups of atoms may not be well-approximated as spheres. In this work, we extend the popular Smooth Overlap of Atomic Positions (SOAP) ML representation for systems consisting of non-spherical anisotropic particles or clusters of atoms. We show the power of this anisotropic extension of SOAP, which we deem AniSOAP, in accurately characterizing liquid crystal systems and predicting the energetics of Gay–Berne ellipsoids and coarse-grained benzene crystals. With our study of these prototypical anisotropic systems, we derive fundamental insights on how molecular shape influences mesoscale behavior and explain how to reincorporate important atom–atom interactions typically not captured by coarse-grained models. Moving forward, we propose AniSOAP as a flexible, unified framework for coarse-graining in complex, multiscale simulation.
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- Award ID(s):
- 2309000
- PAR ID:
- 10578282
- Publisher / Repository:
- AIP Publishing
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 161
- Issue:
- 7
- ISSN:
- 0021-9606
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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