A distance‐dependent atomic knowledge‐based potential for improved protein structure selection

By Hui Lu, Jeffrey Skolnick. Proteins: Structure, Function, and Bioinformatics, 2001

306 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6568414c8f412ff7de92bf1e86f231f68d769b08

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Citations in the corpus

Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction (2002, methodology): “2000) and distance-dependent potentials (Hendlich et al. 1990; Sippl 1990; Jones et al. 1992; Samudrala and Moult 1998; Lu and Skolnick 2001).”
MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology. (2004, background): “For example, it is quite feasible to generate native topologies from folding simulations using simple lattice models; however, it is much more difficult to actually discern native or near-native conformations from other, non-native conformations that are also generated with the same model [15].”
Statistical potential for assessment and prediction of protein structures (2006, background): “…a conditional pdf to a distance pdf for the reference state (Samudrala 1998), and AKBP relies on a mole fraction-dependent reference state function (Lu and Skolnick 2001). compare the DOPE n ð r Þ function to that of DFIRE, most similar statistical potential to DOPE. picture of noninteracting…”
The Victor/FRST Function for Model Quality Estimation (2005, unknown): “A total of eight different published state-of-the-art methods are also shown for comparison: RAPDF (Samudrala et al. , 1998), AKBP (Lu et al. , 2001), DFIRE (Zhou et al. , 2002; Zhang et al. , 2004), SNAPP (Krishnamoorthy et al. , 2003), Errat (Colovos et al. , 1993), Prosa II (Sippl, 1993b), Verify”
New statistical potential for quality assessment of protein models and a survey of energy functions (2010, background): “Quasi-chemical approximation, a popular model for defining the reference state [2,13-16], uses molar fractions of the corresponding interacting centers to calculate the expected frequency of their pairs in the system without interactions and otherwise does not provide any other assumptions regarding”
Developing optimal non-linear scoring function for protein design (2004, methodology): “…greater than a constant b > 0: A widely used functional form for protein scoring function H is the weighted linear sum of pairwise contacts (Tanaka and Scheraga, 1976; Miyazawa and Jernigan, 1985; Tobi et al. , 2000; Vendruscolo and Domany, 1998; Samudrala and Moult, 1998; Lu and Skolnick, 2001).”
Application of asymmetric statistical potentials to antibody-protein docking (2012, background): “Most structure-based potentials have been derived from folded protein structures (Lu and Skolnick, 2001; Miyazawa and Jernigan, 1985; Rojnuckarin and Subramaniam, 1999; Skolnick et al., 1997) where the symmetric pairwise assumption, ignoring multi-body interaction terms, was the natural first choice”
PIPER: An FFT‐based protein docking program with pairwise potentials (2006, unknown): “18,19,30 The novelty of our approach is that we generate a large decoy set of docked conformations to be used as a reference state.”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, methodology): “Some (e.g., DFIRE and DOPE) use analytical methods to estimate the reference state, while others use statistical methods such as KBP (Lu and Skolnick, 2001) and RAPDF (Samudrala and Moult, 1998).”
3DRobot: automated generation of diverse and well-packed protein structure decoys (2016, methodology): “…reference states, including ideal-gas (Zhou and Zhou, 2002), spherical non-interaction (Shen and Sali, 2006), quasi-chemical approximation (Lu and Skolnick, 2001), atomic average (Samudrala and Moult, 1998), random-walk (Zhang and Zhang, 2010) and atomic shuffling (Rykunov and Fiser, 2007).”