WikiSysop: Created page with "== Citation == Haloi, N., Howard, R.J. and Lindahl, E. 2025. Cryo-EM ligand building using AlphaFold3-like model and molecular dynamics. PLOS Computational Biology. 21, 8 (2025), e1013367. == Abstract == Resolving protein-ligand interactions in atomic detail is key to understanding how small molecules regulate macromolecular function. Although recent breakthroughs in cryogenic electron microscopy (cryo-EM) have enabled high-quality reconstruction of numerous complex b..."

2025-08-29T08:07:50Z

Created page with "== Citation == Haloi, N., Howard, R.J. and Lindahl, E. 2025. Cryo-EM ligand building using AlphaFold3-like model and molecular dynamics. PLOS Computational Biology. 21, 8 (2025), e1013367. == Abstract == Resolving protein-ligand interactions in atomic detail is key to understanding how small molecules regulate macromolecular function. Although recent breakthroughs in cryogenic electron microscopy (cryo-EM) have enabled high-quality reconstruction of numerous complex b..."

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== Citation ==

Haloi, N., Howard, R.J. and Lindahl, E. 2025. Cryo-EM ligand building using AlphaFold3-like model and molecular dynamics. PLOS Computational Biology. 21, 8 (2025), e1013367.

== Abstract ==

Resolving protein-ligand interactions in atomic detail is key to understanding how small
molecules regulate macromolecular function. Although recent breakthroughs in cryogenic
electron microscopy (cryo-EM) have enabled high-quality reconstruction of numerous
complex biomolecules, the resolution of bound ligands is often relatively poor.
Furthermore, methods for building and refining molecular models into cryo-EM maps
have largely focused on proteins and may not be optimized for the diverse properties
of small-molecule ligands. Here, we present an approach that integrates artificial intelligence
(AI) with cryo-EM density-guided simulations to fit ligands into experimental maps.
Using three inputs: 1) a protein amino acid sequence, 2) a ligand specification, and 3)
an experimental cryo-EM map, we validated our approach on a set of biomedically relevant
protein-ligand complexes including kinases, GPCRs, and solute transporters, none
of which were present in the AI training data. In cases for which AI was not sufficient to
predict experimental poses outright, integration of flexible fitting into molecular dynamics
simulations improved ligand model-to-map cross-correlation relative to the deposited
structure from 40-71% to 82-95%. This work offers a straightforward pipeline for integrating
AI and density-guided simulations to model building in cryo-EM maps of ligandprotein
complexes.

== Keywords ==

== Links ==

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013367

== Related software ==

== Related methods ==

== Comments ==

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