Abstract
BackgroundAn accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins.MethodologyWe developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential.SignificanceRW and RWplus demonstrate a significantly better ability than the best performing pair-wise distance-dependent atomic potential functions in both native and near-native model selections. It has higher energy-RMSD and energy-TM-score correlations compared with other potentials of the same type in real-life structure assembly decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW.
Highlights
The basic hypothesis of protein folding theory is that protein structure generally has the lowest Gibbs free energy in the native state [1]
The second is knowledge-based potential [e.g. RAPDF [9], KBP [10], DFIRE [11], DOPE [12], OPUS-PSP [13,14], free-rotating chain-based potential [15], or the more composite TASSER/ITASSER [16,17,18] and ROSETTA [19] potentials], which is derived from the statistical regularities [20] of the solved protein structures in the PDB library [21]
We compared the results of RW and RWplus mainly with two frequently used atomic potentials, DFIRE [11] and DOPE [12]
Summary
The basic hypothesis of protein folding theory is that protein structure generally has the lowest Gibbs free energy in the native state [1]. An accurate energy function is the key to solve the protein folding and protein structure prediction problems. An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, which ignored the inherent sequence connectivity and entropic elasticity of proteins
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