Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method

Abstract

Protein structure prediction directly from sequences is a very challenging problem in computational biology. One of the most successful approaches employs stochastic conformational sampling to search an empirically derived energy function landscape for the global energy minimum state. Due to the errors in the empirically derived energy function, the lowest energy conformation may not be the best model. We have evaluated the use of energy calculated by the fragment molecular orbital method (FMO energy) to assess the quality of predicted models and its ability to identify the best model among an ensemble of predicted models. The fragment molecular orbital method implemented in GAMESS was used to calculate the FMO energy of predicted models. When tested on eight protein targets, we found that the model ranking based on FMO energies is better than that based on empirically derived energies when there is sufficient diversity among these models. This model diversity can be estimated prior to the FMO energy calculations. Our result demonstrates that the FMO energy calculated by the fragment molecular orbital method is a practical and promising measure for the assessment of protein model quality and the selection of the best protein model among many generated.

The fragment molecular orbital method: theoretical development, implementation in GAMESS, and applications

“…[176][177][178] The validity of FMO to order native and decoy structures, as related to protein folding, was examined. 179,180 The ligand binding analysis was compared to experiment. 170,176,181 Molecular electrostatic potential can be evaluated with FMO, 66,176 which is useful to gain insight into binding.…”

“…Pair interactions provide useful information about the recognition of ligands by proteins, which can be used in drug discovery . The validity of FMO to order native and decoy structures, as related to protein folding, was examined . The ligand binding analysis was compared to experiment .…”

Recognition of repulsive and attractive regions of selected serotonin receptor binding site using FMO-EDA approach

“…Currently, FMO allows also to estimate the solvation contribution by interfacing with the polarizable continuum model (PCM) method [4]. The FMO methodology was successfully applied to various large biological systems, primarily in a retrospective analysis of binding sites [5][6][7][8][9][10][11][12][13][14][15][16], but also as a tool supporting drug design [17][18][19][20]. Among these, several studies [7,11,16,18] have focused on one of the most important groups of biological targets, a G protein-coupled receptors (GPCRs) family.…”

Peptide Mediated Antimicrobial Dental Adhesive System

“…The decoy generation method is stochastic and therefore cannot accurately predict flexibility changes if the lowest energy structures possible are found. However, the lowest energy structure may differ from the native structure [ 84 , 85 ]. The parallel optimization of many decoys at once may also provide insight into the distribution of the ensemble of structures sampled by peptides in solution.…”