Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading.

By S. Miyazawa, Robert L. Jernigan. 1996

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Citations in the corpus

How well can the accuracy of comparative protein structure models be predicted? (2008, background): “Because of the wide accuracy range of models, many assessment scores have been developed for tasks including (1) determining whether or not a model has the correct fold (Miyazawa and Jernigan 1996; Park and Levitt 1996; Domingues et al. 1999; Lazaridis and Karplus 1999; Gatchell et al. 2000; Melo et”
Positive and Negative Design in Stability and Thermal Adaptation of Natural Proteins (2006, methodology): “The residues interact with each other via the Miyazawa–Jernigan pairwise contact potential [22].”
Improved scoring function for comparative modeling using the M4T method (2008, background): “Originally, it combined three scoring methods of different nature: environment specific substitution matrices from FUGUE [24], secondary structure based 3D–1D substitution matrix H3P2 [25] and pairwise residue-based contact potential [26].”
Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins (1999, background): “The environment classes Ei in equations (5) and (6) were defined solely in terms of residue burial.”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, unknown): “CP_MJ2h [155] r”
New statistical potential for quality assessment of protein models and a survey of energy functions (2010, background): “The residue level representations use C a , C b atoms or side chain centroids and are usually based on the 20 naturally occurring amino acids [2,15,24,25], although both reduced [7,26,27] and extended [28,29] amino acid alphabets were explored, where the extended alphabet further classifies each…”
Statistical potential for assessment and prediction of protein structures (2006, background): “Subsequently, a large number of different statistical potentials were described and tested (Hendlich et al. 1990; Colovos and Yeates 1993; Sippl 1993a; Kocher et al. 1994; Huang et al. 1995; Rooman and Wodak 1995; Jernigan and Bahar 1996; Jones and Thornton 1996; Miyazawa and Jernigan 1996; Moult 19”
Sequence determinants of protein phase behavior from a coarse-grained model (2017, methodology): “Here we have introduced two 99 different models: the first is based on the amino acid hydrophobicity [51] and uses 100 functional form introduced by Ashbaugh and Hatch [52]; the second is based on the 101 Miyazawa-Jerningan potential [53] with the parameterized functional form taken from 102 Kim and”
Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. (1997, methodology): “…between the calculated and observed distributions indicates that the density term used in previous treatments involves considerable overcounting; a proper correction for many body effects would primarily compensate for the lack of explicit treatment of excluded volume (Miyazawa & Jernigan, 1996).”
Fast Protein Loop Sampling and Structure Prediction Using Distance-Guided Sequential Chain-Growth Monte Carlo Method (2014, unknown): “Potential function To evaluate the energy of loops, we develop a simple atombased distance-dependent empirical potential function, following well-established practices [46,52,60–66].”