Energy functions that discriminate X-ray and near native folds from well-constructed decoys.

By Britt H. Park, Michael Levitt. 1996

This study generates ensembles of decoy or test structures for eight small proteins with a variety of different folds. Between 35,000 and 200,000 decoys were generated for each protein using our four-state off-lattice model together with a novel relaxation method. These give compact self-avoiding conformations each constrained to have native secondary structure. Ensembles of these decoy conformations were used to test the ability of several types of empirical contact, surface area and distance-dependent energy functions to distinguish between correct and incorrect conformations. These tests have shown that none of the functions is able to distinguish consistently either the X-ray conformation or the near-native conformations from others which are incorrect. Certain combinations of two of these energy functions were able, however, consistently to identify X-ray structures from amongst the decoy conformations. These same combinations are better also at identifying near-native conformations, consistently finding them with a hundred-fold higher frequency than chance. The fact that these combination energy functions perform better than generally accepted energy functions suggests their future use in folding simulations and perhaps threading predictions.

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, background): “…include discriminating the ground state conformation of a macromolecule from higher energy conformations (Novotny´, Bruccoleri, & Karplus, 1984; Park & Levitt, 1996; Simons et al., 1999), and predicting amino acid sidechain conformations (Bower, Cohen, & Dunbrack, 1997; Jacobson, Kaminski,…”
ProQ3D: improved model quality assessments using deep learning (2016, methodology): “For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method.”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, unknown): “CP_HLPL [153] r”
The Victor/FRST Function for Model Quality Estimation (2005, unknown): “Three widely used decoy sets were downloaded from the Decoys’R’Us database (Samudrala et al. , 2000): 4state_reduced (Park et al. , 1996), lattice_ssﬁt (Xia et al. , 2000), and LMDS (Keasar et al. , 2003).”
Statistical potentials for fold assessment (2002, unknown): “The reference state used to calculate the potentials was other residues (Materials and Methods).”
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”
Can correct protein models be identified? (2003, background): “These focus either on the identification of native, or native-like, protein models from a large set of decoys (Sippl 1990; Park and Levitt 1996; Alternative to threading, decoys can be created by using lattice models (Park and Levitt 1995, 1996), and fragment building (Simons et al. 1997).”
An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. (1998, background): “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).”
Developing optimal non-linear scoring function for protein design (2004, methodology): “…derived from simple decoys generated by gapless threading, a more challenging test is to discriminate native proteins from an ensemble of explicitly generated three dimensional decoy structures with a signiﬁcant number of near-native conformations (Park and Levitt, 1996; Samudrala and Moult, 1998).”
3DRobot: automated generation of diverse and well-packed protein structure decoys (2016, background): “For example, Park and Levitt (1996) proposed to construct structure decoys by randomly rotating structural segments of known native structures around a set of selected flexible hinges.”
How to derive a protein folding potential? A new approach to an old problem. (1996, background): “…acid geometry and approximation of multiple-body interactions (Kolinsky & Skolnick, 1993, 1994), dihedral angles (Kolaskar & Prashanth, 1979; Nishikawa & Matsuo, 1993; Rooman et al., 1992; DeWitte & Shakhnovich, 1994), and better treatment of solvent–protein interactions (Park & Levitt, 1996).”
Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins (1999, methodology): “In addition, only compact structures (radius of gyration , 3*length 1/3 ) 11 were used yielding a ﬁnal data-base size of 325 proteins, 83,151 positions.”