A Physical Approach to Protein Structure Prediction

Abstract

We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4th Critical Assessment of Techniques for Protein Structure Prediction competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.

ProteinShop: A tool for interactive protein manipulation and steering

“…The stochastic perturbation (SP) method for protein structure prediction developed by a group of researchers in LBNL and the University of Colorado (SP group) is a physics-based method that uses secondary structure information [1]. Such information is used to create initial protein configurations that contain secondary structure according to the Figure 7 shows a block diagram of the setup and optimization phases.…”

Reliable protein structure refinement using a physical energy function

“…(1) are taken from our previous model, where V GB is the generalized Born (GB) description of the electrostatic component of solvent free energy, 28 and V HPMF is the HPMF to describe the hydrophobic solute-solute interaction induced by water. 29,30 We refer the reader to our previous work 24,25 for the functional form and the parameter details regarding these terms. Finally, we have added an energy term developed by Kabsch and Sander 31 to provide a means for improving the geometry of protein backbone hydrogen bond formation.…”

De Novo Protein Structure Prediction

“…Finally, examples of de novo structure prediction methods that are more theoretical and physics-based can be found in the works of Wolynes and colleagues (Onuchic and Wolynes, 2004;Wolynes, 2005), Daggett and colleagues (Daggett and Fersht, 2003;Beck and Daggett, 2004), and Head-Gordon and colleagues (Crivelli et al, 2002;Head-Gordon and Brown, 2003). Compared with the approaches described in the previous paragraph, this category of methods encounters substantially greater challenges because they have much less reliance on fold recognition and other bioinformatic information.…”