Effective energy function for proteins in solution

By T. Lazaridis, Martin Karplus. 1999

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “…Strauss, Misura, & Baker, 2004), the default fullatom energy function in Rosetta, consists of a Lennard–Jones term, an implicit solvation term (Lazaridis & Karplus, 1999), an orientation-dependent H-bond term (Kortemme, Morozov, & Baker, 2003), sidechain and backbone torsion potentials…”
The Rosetta all-atom energy function for macromolecular modeling and design (2017, methodology): “Instead, Rosetta represents the solvent as bulk water based upon the Lazaridis-Karplus (LK) implicit Gaussian exclusion model.”
Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz (2012, background): “Future improvements in the Rosetta energy function, particularly in its highly oversimplified solvent model [37,38], may better guide SWA modeling in early stages to partial loop conformations that give better all-atom energies and/or accuracies.”
Water mediation in protein folding and molecular recognition. (2006, background): “, continuum dielectric models such as generalized Born models) (5, 47), dielectric screening functions (84), or solvent-accessible surface area (41).”
Modeling structurally variable regions in homologous proteins with rosetta (2004, methodology): “The complete full-atom potential is thus comprised of 1) the attractive portion of the 12-6 Lennard–Jones potential, 2) a linear repulsive term used in place of the repulsive portion of the 12-6 potential, 3) backbone-dependent internal free energies of the rotamers estimated from PDB statistics, 4)”
High-resolution Structural and Thermodynamic Analysis of Extreme Stabilization of Human Procarboxypeptidase by Computational Protein Design (2007, background): “The energy function is a linear combination of a 6-12 Lennard-Jones potential, the Lazaridis – Karplus implicit solvation model, 47 an empirical hydrogen bonding potential, 48 backbone-dependent rotamer probabilities, 49 amino acid probabilities for particular regions of ϕ / ψ space, and a simple…”
Computational approaches to structure-based ligand design. (1999, background): “For example, an excluded volume-implicit solvation model can be used that assumes that the solvation free energy for each group or residue in the system is equal to the calculated solvation free energy for that group in a small model compound less the amount of solvation lost due to solvent exclusio”
Accurate and efficient loop selections by the DFIRE‐based all‐atom statistical potential (2004, unknown): “The results of DFIRE are compared with the results of several physical-based energy functions obtained by Forrest and Woolf These physical-based energy functions are the combination of CHARMM with various solvation models denoted by FDPB (Brooks et al. 1983), ACS UN (Schaefer et al. 1998), DDD + EEF”
RosettaAntibody: antibody variable region homology modeling server (2009, background): “The highresolution energy function includes van der Waals energy, orientation-dependent hydrogen bonding (14), implicit Gaussian solvation ( 15 ), side-chain rotamer propensities (16), and a low-weighted distance-dependent dielectric electrostatic energy (17).”