Improved protein structure selection using decoy-dependent discriminatory functions

Abstract

BackgroundA key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations.ResultsWe present a simple and nonparametric formula to estimate the accuracy of predicted conformations (or decoys). This scoring function, called the density score function, evaluates decoy conformations by performing an all-against-all Cα RMSD (Root Mean Square Deviation) calculation in a given decoy set. We tested the density score function on 83 decoy sets grouped by their generation methods (4state_reduced, fisa, fisa_casp3, lmds, lattice_ssfit, semfold and Rosetta). The density scores have correlations as high as 0.9 with the Cα RMSDs of the decoy conformations, measured relative to the experimental conformation for each decoy.We previously developed a residue-specific all-atom probability discriminatory function (RAPDF), which compiles statistics from a database of experimentally determined conformations, to aid in structure selection. Here, we present a decoy-dependent discriminatory function called self-RAPDF, where we compiled the atom-atom contact probabilities from all the conformations in a decoy set instead of using an ensemble of native conformations, with a weighting scheme based on the density scores. The self-RAPDF has a higher correlation with Cα RMSD than RAPDF for 76/83 decoy sets, and selects better near-native conformations for 62/83 decoy sets. Self-RAPDF may be useful not only for selecting near-native conformations from decoy sets, but also for fold simulations and protein structure refinement.ConclusionsBoth the density score and the self-RAPDF functions are decoy-dependent scoring functions for improved protein structure selection. Their success indicates that information from the ensemble of decoy conformations can be used to derive statistical probabilities and facilitate the identification of near-native structures.

An Atomic Environment Potential for use in Protein Structure Prediction

“…These values (log 10 P B10 as per Wang et al 33 ) are also presented in Supplementary Data, Table S5.…”

“…values is commonly employed 30,33 to show that a trend exists that might allow low C a -r.m.s.d. decoys to be chosen from the decoy set by choosing low-scoring decoys.…”

Review: The lord of the rings: Structure and mechanism of the sliding clamp loader

“…In the more distantly related AAA1 protein HslU, the nucleotide dependent motions are more subtle (58) but in the same direction as observed in the clamp loader. [131][132][133] The similar conformational changes induced by nucleotide hydrolysis cause reorganization of interfacial contacts within the AAA1 assembly. In most AAA1 machines, the AAA-lid domain contacts the AAA-ATP domain of the adjacent subunit's AAA1 module.…”

Membrane protein orientation and refinement using a knowledge-based statistical potential

“…Since our knowledge-based potential is “trained” using experimentally determined structures, it may well capture the intrinsic properties of native conformations well but often such potentials fail to evaluate the quality of near-native and misfolded conformations appropriately [55]. Rather than using the experimental structures, we therefore built homology models with MODELLER [56] using the native crystal structures as templates.…”