Comparison of multiple Amber force fields and development of improved protein backbone parameters

By Viktor Hornak, Robert Abel, Asim Okur, Bentley Strockbine, Adrian E. Roitberg, Carlos Simmerling. 2006

6,725 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

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In Materials Chemistry

1. David E. Shaw
2. Stefano Piana
3. Ron O. Dror
4. Vijay S. Pande
5. K. Lindorff‐Larsen
6. ST John
7. Wouter Boomsma
8. Brian G. Pierce

In Molecular Biology

1. David L. Mobley
2. David E. Shaw
3. Vijay S. Pande
4. Frank No'e
5. Ron O. Dror
6. Stefano Piana
7. Ken A. Dill
8. Alexandre M. J. J. Bonvin

In Computational Chemistry & Drug Discovery

1. ST John
2. Brian G. Pierce
3. Ron O. Dror
4. Kyle Mills
5. Christa M. Homenick
6. Isaac Tamblyn
7. Kevin Ryczko
8. Iryna Luchak

Citations in the corpus

• Learning inverse folding from millions of predicted structures (2022, methodology): “Even after Amber relaxation, the backbone coordinates predicted by AlphaFold2 contain artifacts in the sub-Angstrom scale that may give away amino acid identities.”
• An overview of the Amber biomolecular simulation package (2013, background): “Amber now supports a wide range of force fields including all of the pair wise amino and nucleic acid variants 2–4 , 8 , 10 of which the ff10 force field represents a collection”
• Spherical convolutions and their application in molecular modelling (2017, methodology): “For all structures, hydrogen atoms were added using the Reduce program (Word et al., 1999), after which partial charges were assigned using the OpenMM framework (Eastman et al., 2012), using the amber99sb force field (Hornak et al., 2006).”
• AutoDock-GIST: Incorporating Thermodynamics of Active-Site Water into Scoring Function for Accurate Protein-Ligand Docking (2016, methodology): “We assigned protein parameters from AMBER99SB force field [73] and solvated the system in a TIP3P [74] water box with the periodic boundary condition, keeping the minimum distance of 10 Å away from any atom of the protein.”
• Convolutional neural networks for atomistic systems (2017, background): “In the domain of phenomenological fits, force fields informed from high-level theory calculations and experiment have seen success [4, 5, 6, 7, 8, 9, 10] in modelling phenomena that occur on time and length scales beyond the reach of the higher level electronic structure methods.”