Recognition of native structure from complete enumeration of low-resolution models with constraints

Abstract

Complete sets of low-resolution conformations are generated for eight small proteins by rotating the Calpha-Calpha virtual bonds at selected flexible regions, while the remaining structural elements are assumed to move in rigid blocks. Several filtering criteria are used to reduce the ensemble size and to ensure the sampling of well-constructed conformations. These filters, based on structure and energy constraints deduced from knowledge-based studies, include the excluded volume requirement, the radius of gyration constraint, and the occurrence of sufficiently strong attractive inter-residue potentials to stabilize compact forms. About 8,000 well-constructed decoys or "probable folds" (PFs) are constructed for each protein. A correlation between root-mean-square (rms) deviations from X-ray structure and total energies is observed, revealing a decrease in energy as the rms deviation decreases. The conformation with the lowest energy exhibits an rms deviation smaller than 3.0 A, in most of the proteins considered. The results are highly sensitive to the choice of flexible regions. A strong tendency to assume native state rotational angles is revealed for some flexible bonds from the analysis of the distributions of dihedral angles in the PFs, suggesting the formation of foldons near these locally stable regions at early folding pathway.

Protein structure: geometry, topology and classification

“…As will be outlined in the following section, this is important for the prediction of structure from sequence. Similar approaches have also been made from the direction of a more continuous simplification of protein structure through progressive smoothing (Hinds and Levitt 1992, Crippen and Maiorov 1995, Özkan and Bahar 1998.…”

A computational pathway for bracketing native-like structures for small alpha helical globular proteins

“…Structure prediction attempts using distance geometry approach 108,109 incorporating experimental information in the form of inter-residue distances in minimization procedures, 110,111 metric matrix distance geometry 112 for generating native-like folds, and scoring functions such as residue pair potentials, hydrophobicity function 113 have shown to select native-like structures. Hierarchical approaches, 114,115 build up procedures 93,[116][117][118][119] and simulated annealing techniques [120][121][122][123] as well as Monte Carlo 124,125 methods have been used extensively for conformational sampling of proteins. A few other initiatives in this area are also reported in the literature.…”

From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction

“…Statistical functions, also referred to as knowledge-based functions, rely on statistical analysis of known native conformations deposited in the PDB, whereas physics-based functions consider the physical principles of atomic interactions. Despite the apparent superiority of statistical functions in discriminating native conformations supported by a large body of work [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ], some physics-based functions have also been proved effective in decoy evaluation [ 32 ]. For instance, work in [ 33 ] conjectures that a scoring function based on physical properties is effective in identifying the native conformation.…”