Physical scoring function based on AMBER force field and Poisson–Boltzmann implicit solvent for protein structure prediction

Abstract

A well-behaved physics-based all-atom scoring function for protein structure prediction is analyzed with several widely used all-atom decoy sets. The scoring function, termed AMBER/Poisson-Boltzmann (PB), is based on a refined AMBER force field for intramolecular interactions and an efficient PB model for solvation interactions. Testing on the chosen decoy sets shows that the scoring function, which is designed to consider detailed chemical environments, is able to consistently discriminate all 62 native crystal structures after considering the heteroatom groups, disulfide bonds, and crystal packing effects that are not included in the decoy structures. When NMR structures are considered in the testing, the scoring function is able to discriminate 8 out of 10 targets. In the more challenging test of selecting near-native structures, the scoring function also performs very well: for the majority of the targets studied, the scoring function is able to select decoys that are close to the corresponding native structures as evaluated by ranking numbers and backbone Calpha root mean square deviations. Various important components of the scoring function are also studied to understand their discriminative contributions toward the rankings of native and near-native structures. It is found that neither the nonpolar solvation energy as modeled by the surface area model nor a higher protein dielectric constant improves its discriminative power. The terms remaining to be improved are related to 1-4 interactions. The most troublesome term is found to be the large and highly fluctuating 1-4 electrostatics term, not the dihedral-angle term. These data support ongoing efforts in the community to develop protein structure prediction methods with physics-based potentials that are competitive with knowledge-based potentials.

Assessing implicit models for nonpolar mean solvation forces: The importance of dispersion and volume terms

“…There is particular interest in the ability of implicit solvent models to provide atomic solvation forces with sufficient speed and accuracy for implementation in molecular dynamics simulations (43)(44)(45) and other ''high-throughput'' (15,16,46) calculations. However, most models are tested by using only global quantities such as solvation energies (13,(28)(29)(30)(31)(32)(33)41) or simulation rms deviation and stability (33,44).…”

Assessment of Detection and Refinement Strategies for de novo Protein Structures Using Force Field and Statistical Potentials

“…The potential or scoring functions to discriminate and refine protein structures are currently based on three methods: force-field based 10,17,18 and knowledge-based 19 and hybrids of the two. 6,7,20 Force-field based detection functions often employ a standard parameter set such as CHARMM PARAM22 21 or AMBER 22 and an implicit solvent function such as generalized Born(GB) 23 or Poisson-Boltzmann.…”

Cooperativity in the self-assembly of porphyrin ladders

“…This phenomenon has been dubbed the enthalpic chelate effect and is likely to be of greatest significance in processes that involve the cooperation of large numbers of weak interactions, biomolecular folding, binding, and catalysis. Thus, the stability of a folded protein will be larger than one might expect by simply adding up the free-energy contributions of all of the interactions involved, and this finding has important implications, for example, in any attempts to predict protein three-dimensional structure from sequence or model processes such as protein folding that rely on simple force fields that are essentially additive (8)(9)(10)(11)(12). There is considerable experimental evidence that the binding of substrates to proteins leads to structural tightening (13)(14)(15)(16)(17), and if this process were associated with a significant additional free energy contribution, then it may be the enthalpic chelate that is the key missing factor in our understanding of the remarkably high binding affinities and the rate accelerations that are achieved by biological molecules (13,(18)(19)(20).…”