A decoy set for the thermostable subdomain from chicken villin headpiece, comparison of different free energy estimators

BACKGROUND: Estimators of free energies are routinely used to judge the quality of protein structural models. As these estimators still present inaccuracies, they are frequently evaluated by discriminating native or native-like conformations from large ensembles of so-called decoy structures. RESULTS: A decoy set is obtained from snapshots taken from 5 long (100 ns) molecular dynamics (MD) simulations of the thermostable subdomain from chicken villin headpiece. An evaluation of the energy of the decoys is given using: i) a residue based contact potential supplemented by a term for the quality of dihedral angles; ii) a recently introduced combination of four statistical scoring functions for model quality estimation (FRST); iii) molecular mechanics with solvation energy estimated either according to the generalized Born surface area (GBSA) or iv) the Poisson-Boltzmann surface area (PBSA) method. CONCLUSION: The decoy set presented here has the following features which make it attractive for testing energy scoring functions: 1) it covers a broad range of RMSD values (from less than 2.0 Å to more than 12 Å); 2) it has been obtained from molecular dynamics trajectories, starting from different non-native-like conformations which have diverse behaviour, with secondary structure elements correctly or incorrectly formed, and in one case folding to a native-like structure. This allows not only for scoring of static structures, but also for studying, using free energy estimators, the kinetics of folding; 3) all structures have been obtained from accurate MD simulations in explicit solvent and after molecular mechanics (MM) energy minimization using an implicit solvent method. The quality of the covalent structure therefore does not suffer from steric or covalent problems. The statistical and physical effective energy functions tested on the set behave differently when native simulation snapshots are included or not in the set and when averaging over the trajectory is performed

A pairwise residue contact area-based mean force potential for discrimination of native protein structure

Abstract Background Considering energy function to detect a correct protein fold from incorrect ones is very important for protein structure prediction and protein folding. Knowledge-based mean force potentials are certainly the most popular type of interaction function for protein threading. They are derived from statistical analyses of interacting groups in experimentally determined protein structures. These potentials are developed at the atom or the amino acid level. Based on orientation dependent contact area, a new type of knowledge-based mean force potential has been developed. Results We developed a new approach to calculate a knowledge-based potential of mean-force, using pairwise residue contact area. To test the performance of our approach, we performed it on several decoy sets to measure its ability to discriminate native structure from decoys. This potential has been able to distinguish native structures from the decoys in the most cases. Further, the calculated Z-scores were quite high for all protein datasets. Conclusions This knowledge-based potential of mean force can be used in protein structure prediction, fold recognition, comparative modelling and molecular recognition. The program is available at http://www.bioinf.cs.ipm.ac.ir/softwares/surfield</p

Scoring predictive models using a reduced representation of proteins: model and energy definition

BACKGROUND: Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an energy model capable, for instance, of detecting native or native-like structures among decoy sets. The aim of the present work is to provide a discrete empirical potential for a reduced protein model termed here PC2CA, because it employs a PseudoCovalent structure with only 2 Centers of interactions per Amino acid, suitable for protein model quality assessment. RESULTS: All protein structures in the set top500H have been converted in reduced form. The distribution of pseudobonds, pseudoangle, pseudodihedrals and distances between centers of interactions have been converted into potentials of mean force. A suitable reference distribution has been defined for non-bonded interactions which takes into account excluded volume effects and protein finite size. The correlation between adjacent main chain pseudodihedrals has been converted in an additional energetic term which is able to account for cooperative effects in secondary structure elements. Local energy surface exploration is performed in order to increase the robustness of the energy function. CONCLUSION: The model and the energy definition proposed have been tested on all the multiple decoys' sets in the Decoys'R'us database. The energetic model is able to recognize, for almost all sets, native-like structures (RMSD less than 2.0 Å). These results and those obtained in the blind CASP7 quality assessment experiment suggest that the model compares well with scoring potentials with finer granularity and could be useful for fast exploration of conformational space. Parameters are available at the url:

Artefacts and biases affecting the evaluation of scoring functions on decoy sets for protein structure prediction

Motivation: Decoy datasets, consisting of a solved protein structure and numerous alternative native-like structures, are in common use for the evaluation of scoring functions in protein structure prediction. Several pitfalls with the use of these datasets have been identified in the literature, as well as useful guidelines for generating more effective decoy datasets. We contribute to this ongoing discussion an empirical assessment of several decoy datasets commonly used in experimental studies

Discrimination of near-native decoy structures using statistical potentials

Being able to select decoy structures that are closest to the native one is essential to any folding simulation. Indeed, modern algorithms use heuristics to quickly sample the conformational space, and as such, will generate a large number of candidate structures. In this thesis, we create a new statistical energy function to correctly discriminate near-native decoy structures, using three complementary approaches to derive energies from known conformations and decoys. First, we used a classical definition, where the observed state is modelled by taking a set of 1078 short, well-resolved, non-redundant crystal structures from the PDB, and the reference state is taken as the distribution expected at random. In our second method, which we call “hybrid”, we used the native structures as the observed state, just as in the classical formulation, but this time using the worse generated decoys as the reference state. Finally, our third method, called “decoy-based”, uses only decoys, taking the better than average models as the observed state, and the worse than average as the reference state. Using the three methods above, we generated potentials to model solvation, hydrogen bonding, and pairwise atomic distances and orientation. We found that overall, combining solvation, atomic distance and orientation using the decoy-based method produced the best results, with a 10% enrichment score of 0.73 versus 0.51 for the classical formulation, and 0.41 for our benchmark potential, DFIRE2. Our final potential, called the DOS potential, was created by combining the classical, hybrid and decoy-based potentials, and achieved a 10% enrichment score of 0.75 versus 0.41 for DFIRE2