A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking.
We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods.
Division of Systems Engineering, Boston University.
Feng
F
Nan
Division of Systems Engineering, Boston University.
Mohammad
M
Moghadasi
Department of Mechanical Engineering and Division of Systems Engineering, Boston University.
Pirooz
P
Vakili
Department of Biomedical Engineering, Boston University.
Sandor
S
Vajda
Department of Biomedical Engineering, Boston University.
Dima
D
Kozakov
Division of Systems Engineering, Boston University.
Ioannis
I
Ch Paschalidis