Hindi, R.; (2012) Discrimination of near-native decoy structures using statistical potentials. Doctoral thesis , UCL (University College London).

Preview

PDF 1338409.pdf Download (15MB)

Abstract

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.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item