Dead-end elimination was used to identify global minimum energy conformations for the prudent polar and no polar strategies.
An automated sequence selection algorithm, based on the dead-end elimination theorem, was used to find the optimal amino acid sequence fitting the target structure.
Beginning from dead-end elimination, we derive the first algorithm, to our knowledge, capable of deterministic global repacking of side chains compatible with many-body energy functions.
The method we use is based on a variety of dead-end elimination methods and the recently discovered dynamic programming algorithm for this problem.
The dead-end elimination (DEE) theorems are powerful tools for the combinatorial optimization of protein side-chain placement in protein design and homology modeling.
Disparate design strategies based upon dead-end elimination, simulated annealing and statistical design have each recently yielded striking successes involving de novo designed proteins with sizes on the order of 100 residues or greater.
Here, we study four common search techniques: Monte Carlo (MC) and Monte Carlo plus quench (MCQ); genetic algorithms (GA); self-consistent mean field (SCMF); and dead-end elimination (DEE).
The optimization was performed using optimization of rotamers by iterative techniques (ORBIT), a protein design program that utilizes a physically based force field and the Dead-End Elimination theorem to compute sequences that are optimal for a given protein scaffold.
The optimization problem is tackled with an algorithm, originally developed for side chain prediction, which combines a dead-end elimination, a branch-and-bound backtracking, and a graph decomposition approach [15].
Furthermore, Protonate 3D uses a prioritizing branch-and-bound algorithm in combination with a preceding dead-end elimination to handle the state space optimization problem and a force field based energy model including additional terms for tautomerism and ionization effects.
While PDB_REDO models average an Rfree value of 29.5% and MOLPROBITY score of 2.71 Å (77th percentile), dead-end elimination with the polarizable AMOEBA force field lowered Rfree by 2.8 26.7% and improved mean MOLPROBITY score to atomic resolution at 1.25 Å (100th percentile).
