Fast and simple monte carlo algorithm for side chain optimization in proteins: Application to model building by homology

By Lisa Holm, Christopher Sander. 1992

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Citations in the corpus

Protein sequence design with a learned potential (2022, background): “…for ﬁ xed-backbone design commonly involve specifying an energy function and sampling sequence space to ﬁ nd a minimum-energy con ﬁ guration 13 – 15 , and enormous effort has gone into the development of carefully modeled and parameterized energy functions to guide design, which continue…”
Energy-based models for atomic-resolution protein conformations (2020, methodology): “The generalization of neural energy functions to harder problem settings used in the protein design community, e.g. combinatorial side chain optimization (Tuffery et al., 1991; Holm & Sander, 1992), and inverse-folding (Pabo, 1983), is a direction for future work.”
Protein sequence design with a learned potential (2020, methodology): “Methods for optimizing over residues and rotamers include variations on the dead-end elimination (DEE) algorithm [32, 33, 34, 35], integer linear programming [36, 37], belief propagation [38, 39], and Markov Chain Monte Carlo (MCMC) with simulated annealing [40, 41, 42].”
Principles of Flexible Protein-Protein Docking (2008, unknown): “Monte Carlo (MC)116 is an iterative method.”
An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. (1998, methodology): “Decoys have been based on lattice models (Park & Levitt, 1992), molecular dynamics trajectories (Wang et al., 1995), crystal structures of different resolutions (Subramaniam et al., 1996) and amino acid sequences mounted on radically different folds (Holm & Sander, 1992a).”
Exploiting evolutionary relationships for predicting protein structures (2003, background): “…the position of the side chains in the model (Cardozo et al., 1995; Chakrabarti and Pal, 2001; Chinea et al., 1995; Clementi et al., 2003; Holm and Sander, 1992; Kallblad and Dean, 2003; Keller et al., 1995; Kussel et al., 2001; Ogata and Umeyama, 1997; Petrella and Karplus, 2001; Xiang…”
Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. (1997, methodology): “The fastest energy functions focus upon very local interactions and are typically limited to van der Waals or hard-sphere energies (Lee & Subbiah, 1991; Holm & Sander, 1992; Koehl & Delarue, 1994; Laughton, 1994; Vasquez, 1995).”
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction (2002, background): “For decoy sets misfold (Holm and Sander 1992), asilomar (Mosimann et al. 1995), pdberr & sgpa (Avbelj et al. 1990), all three potentials (RAPDF, Atomic KBP, and DFIRE-A) achieved 100% correct identifications.”