Effective energy functions for protein structure prediction.

By T. Lazaridis, Martin Karplus. Current Opinion in Structural Biology, 2000

412 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/282aed73bdb9d245e36c3b1e16c66910ce18104d

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Citations in the corpus

The FoldX web server: an online force field (2005, background): “These are designated by Lazaridis and Karplus (1) as statistical (SEEF) and physical (PEEF) effective energy functions, respectively.”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “7, 8, and 9), boosting the effectiveness of the resulting two-body energies J ( dashed and solid lines in Fig.”
How well can the accuracy of comparative protein structure models be predicted? (2008, background): “2002; McGuffin and Jones 2003; Melo and Sali 2007), (2) discriminating between the native and near-native states (Sippl 1993; Melo and Feytmans 1997, 1998; Park et al. 1997; Fiser et al. 2000; Lazaridis and Karplus 2000; Zhou and Zhou 2002; Seok et al. 2003; Tsai et al. 2003; Shen and Sali 2006), (3”
QMEAN: A comprehensive scoring function for model quality assessment (2008, background): “The three-residue torsion angle potential turned out to be very effective in recognizing the native fold. under physiological conditions.8 There are basically two categories of scoring functions: physics-based energy functions and knowledge-based statistical potentials.”
Solvent accessible surface area approximations for rapid and accurate protein structure prediction (2009, background): “While molecular mechanics force-fields seek to individually describe each of these starting from first principles, knowledge-based potentials (KBPs) seek to derive energy functions that describe the net effect of all these contributions in a specific setting, e.g., protein structures [10].”
rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction obsolete (2018, methodology): “When homology modelling is not viable, de novo techniques are needed, either based on physical-based potentials [2] or knowledge-based potentials [3–6].”
Prediction of protein stability changes for single‐site mutations using support vector machines (2005, background): “Statistical potential approaches achieve predictive accuracy comparable to physical potential approaches.(26) The empirical potential approach derives an en-”
Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. (2009, background): “…potentials (PBP), which are based on the fundamental analyses of the forces between atoms (Brooks et al., 1983; Pearlman et al., 1995; Lazaridis and Karplus, 2000), and knowledge-based potentials (KBP), which rely on statistical analysis of different properties extracted from protein…”
A distance‐dependent atomic knowledge‐based potential for improved protein structure selection (2001, background): “The Park and Levitt decoy set had been shown to be quite a challenge for a simple residue contact potential and van der Waals potential, where the lowest-energy structures typically were 6–10 Å RMSD away from native ones.”
The RosettaDock server for local protein–protein docking (2008, background): “der Waals energies (7), orientation-dependent hydrogen bonding (11), implicit Gaussian solvation (12), side-chain rotamer probabilities (13) and a low-weighted electrostatics energy (7).”
Exploiting evolutionary relationships for predicting protein structures (2003, background): “This implies that the required accuracy of our energetic calculations must be far more accurate than currently possible (Finkelstein, 1997; Lazaridis and Karplus, 2000; Moult, 1997, 1999).”
Statistical potentials for fold assessment (2002, neutral): “…Cramer and Truhlar 1999; Dominy and Brooks, III 1999; Lazaridis and Karplus 1999; Rapp and Friesner 1999; Gatchell et al. 2000; Kollman et al. 2000; Lee et al. 2000; Petrey and Honig 2000; Wang and Kollman 2000; Zhang et al. 2001), spatial distribution of charged groups (Bryant and Lawrence 1991),…”
Energy-based models for atomic-resolution protein conformations (2020, methodology): “Our work is a logical extension of statistical potential methods (Tanaka & Scheraga, 1976; Sippl, 1990; Lazaridis & Karplus, 2000) that ﬁt energetic terms from data, which, in combination with physically motivated force Published as a conference paper at ICLR 2020 ﬁelds, have contributed to the…”
Ab initio protein structure prediction. (2002, methodology): “…explicit and implicit solvent models to predict the folding of small peptides and to discriminate between the native state and static decoy sets (van Gunsteren and co-workers, this issue, pp 190–196; [7 • ]), a more practical use has been as an adjunct to reduced-model ab initio protocols [8 •• ].”