An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction.

By Ram Samudrala, John Moult. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Toward the estimation of the absolute quality of individual protein structure models (2010, background): “…alternative models of the same protein sequence (Benkert et al., 2008; Eramian et al., 2008; Marti-Renom et al., 2000; McGuffin, 2008; Melo and Feytmans, 1998; Pettitt et al., 2005; Randall and Baldi, 2008; Samudrala and Moult, 1998; Tosatto, 2005; Wallner and Elofsson, 2003; Zhou and Zhou, 2002).”
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).”
The Victor/FRST Function for Model Quality Estimation (2005, unknown): “The Victor/FRST (Function of RAPDF, Solvation, and Torsion potentials) function is a statistical scoring function.”
Fast Protein Loop Sampling and Structure Prediction Using Distance-Guided Sequential Chain-Growth Monte Carlo Method (2014, unknown): “Empirical energy functions developed from databases have been shown to be very effective in protein structure prediction, decoy discrimination, and protein-ligand interactions [54,63,64,67–71].”
De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, background): “Another kind of energy function is knowledge-based, which is constructed by using the known structural information, such as the statistical potential based on the atom pair distance distribution [12], [13] and generic contact restraint of side-chain center [14].”
Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins (1999, background): “Recent results using atom pair distributions 27 suggest that improved discrimination can be obtained by scoring full atom models following the addition of explicit side chains.”
A distance‐dependent atomic knowledge‐based potential for improved protein structure selection (2001, methodology): “Interestingly, the atomic potential RAPDF needs to use a long cutoff such as 15 Å or 20 Å to achieve 100% correct selectivity, and gets about 80% correct when using a cutoff of 5 or 10 Å.(27) By contrast, using the current atomic knowledge-based potential, a 100% correct selection can be achieved us”
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction (2002, methodology): “For RAPDF (Samudrala and Moult 1998), the first bin covers 0–3.0 Å, the distance between 3.0–20 Å is binned every 1 Å.”
3DRobot: automated generation of diverse and well-packed protein structure decoys (2016, methodology): “All these potentials were derived from the statistics of known protein structures in the PDB library based on the Boltzmann formulation but using different reference states, including ideal-gas (Zhou and Zhou, 2002), spherical non-interaction (Shen and Sali, 2006), quasi-chemical approximation (Lu a”
Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction (2017, background): “This function is based on the RAPDF (Samudrala and Moult, 1998) and compares the distances between atoms of real structures to those in a decoy.”