Discrimination of near-native decoy structures using statistical potentials
2012
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.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
0
Citations
NaN
KQI