An assessment of nine scoring functions commonly applied in docking using a set of 189 protein-ligand complexes is presented. The scoring functions include the CHARMm potential, the scoring function DrugScore, the scoring function used in AutoDock, the three scoring functions implemented in DOCK, as well as three scoring functions implemented in the CScore module in SYBYL (PMF, Gold, ChemScore). We evaluated the abilities of these scoring functions to recognize near-native configurations among a set of decoys and to rank binding affinities. Binding site decoys were generated by molecular dynamics with restraints. To investigate whether the scoring functions can also be applied for binding site detection, decoys on the protein surface were generated. The influence of the assignment of protonation states was probed by either assigning "standard" protonation states to binding site residues or adjusting protonation states according to experimental evidence. The role of solvation models in conjunction with CHARMm was explored in detail. These include a distance-dependent dielectric function, a generalized Born model, and the Poisson equation. We evaluated the effect of using a rigid receptor on the outcome of docking by generating all-pairs decoys ("cross-decoys") for six trypsin and seven HIV-1 protease complexes. The scoring functions perform well to discriminate near-native from misdocked conformations, with CHARMm, DOCK-energy, DrugScore, ChemScore, and AutoDock yielding recognition rates of around 80%. Significant degradation in performance is observed in going from decoy to cross-decoy recognition for CHARMm in the case of HIV-1 protease, whereas DrugScore and ChemScore, as well as CHARMm in the case of trypsin, show only small deterioration. In contrast, the prediction of binding affinities remains problematic for all of the scoring functions. ChemScore gives the highest correlation value with R(2) = 0.51 for the set of 189 complexes and R(2) = 0.43 for the set of 116 complexes that does not contain any of the complexes used to calibrate this scoring function. Neither a more accurate treatment of solvation nor a more sophisticated charge model for zinc improves the quality of the results. Improved modeling of the protonation states, however, leads to a better prediction of binding affinities in the case of the generalized Born and the Poisson continuum models used in conjunction with the CHARMm force field.

A solvation term based on the solvent accessible surface area (SASA) is combined with the CHARMM polar hydrogen force field for the efficient simulation of peptides and small proteins in aqueous solution. Only two atomic solvation parameters are used: one is negative for favoring the direct solvation of polar groups and the other positive for taking into account the hydrophobic effect on apolar groups. To approximate the water screening effects on the intrasolute electrostatic interactions, a distance-dependent dielectric function is used and ionic side chains are neutralized. The use of an analytical approximation of the SASA renders the model extremely efficient (i.e., only about 50% slower than in vacuo simulations). The limitations and range of applicability of the SASA model are assessed by simulations of proteins and structured peptides. For the latter, the present study and results reported elsewhere show that with the SASA model it is possible to sample a significant amount of folding/unfolding transitions, which permit the study of the thermodynamics and kinetics of folding at an atomic level of detail.

Protein folding is a grand challenge of the postgenomic era. In this paper, 58 folding events sampled during 47 molecular dynamics trajectories for a total simulation time of more than 4 s provide an atomic detail picture of the folding of a 20-residue synthetic peptide with a stable three-stranded antiparallel ␤-sheet fold. The simulations successfully reproduce the NMR solution conformation, irrespective of the starting structure. The sampling of the conformational space is sufficient to determine the free energy surface and localize the minima and transition states. The statistically predominant folding pathway involves the formation of contacts between strands 2 and 3, starting with the side chains close to the turn, followed by association of the N-terminal strand onto the preformed 2-3 ␤-hairpin. The folding mechanism presented here, formation of a ␤-hairpin followed by consolidation, is in agreement with a computational study of the free energy surface of another synthetic three-stranded antiparallel ␤-sheet by Bursulaya and Brooks [(1999) J. Am. Chem. Soc. 121, 9947-9951]. Hence, it might hold in general for antiparallel ␤-sheets with short turns.protein folding ͉ energy landscape ͉ implicit solvation model T heoretical and experimental studies have provided insights into the protein folding process (1). Yet, a more detailed understanding is required not only to predict the structure of a given amino acid sequence but also for protein engineering purposes, e.g., to design a sequence for a given fold. For the latter, applications relevant to biomedicine are possible. Most proteins contain regular elements of secondary structure, ␣-helices, and͞or ␤-sheets. Therefore, it generally is thought that elucidating the formation of regular secondary structure elements will improve the understanding of the protein folding reaction (2-4). As it is currently not yet feasible to simulate the folding of a protein by using molecular dynamics (MD) simulations with an all-atom model (5), the common approach taken in the past is to unfold starting from the native state (6, 7). Reversible folding simulations have been limited to non-natural oligopeptides in methanol (8) or peptides with an implicit solvation model and adaptive umbrella sampling (9).Beta3s is a designed amino acid sequence (Thr 1 -Trp 2 -Ile 3 -Gln 4 -Asn 5 -Gly 6 -Ser 7 -Thr 8 -Lys 9 -Trp 10 -Tyr 11 -Gln 12 -Asn 13 -Gly 14 -Ser 15 -Thr 16 -Lys 17 -Ile 18 -Tyr 19 -Thr 20 ), and its solution conformation has been studied by NMR (10). Nuclear Overhauser enhancement (NOE) and chemical shift data indicate that at 10°C Beta3s populates a single structured form, the expected three-stranded antiparallel ␤-sheet conformation with turns at Gly-6-Ser-7 and Gly-14-Ser-15, in equilibrium with the random coil. The ␤-sheet population was 13-31% based on NOE intensities and 30-55% based on the chemical shift data (10). Furthermore, Beta3s was shown to be monomeric in aqueous solution by equilibrium sedimentation and NMR dilution experiments (10). In this report, the foldi...

Articles you may be interested inStudy on the conformational equilibrium of the alanine dipeptide in water solution by using the averaged solvent electrostatic potential from molecular dynamics methodology J. Chem. Phys. 135, 194502 (2011); 10.1063/1.3658857 Free energy of conformational transition paths in biomolecules: The string method and its application to myosin VI J. Chem. Phys. 134, 085103 (2011); 10.1063/1.3544209An application of coupled reference interaction site model/molecular dynamics to the conformational analysis of the alanine dipeptide Optimal free energy paths ͑OFEPs͒ for conformational transitions are parallel to the mean force at every nonstationary point of the free energy landscape. In contrast to adiabatic paths, which are parallel to the force, OFEPs include the effect of entropy and are relevant even for systems with diffusive degrees of freedom. In this study the OFEPs are computed for the alanine dipeptide in solution. The potential of mean force is calculated and an effective potential is derived that is used to obtain the paths with a minimization based algorithm. The comparison of the calculated paths with the adiabatic paths in vacuo shows the influence of the environment on conformational transitions. The dynamics of the alanine dipeptide in water are more complex, since there are more minima and the barriers are lower. Two simpler methods for the calculation of reaction pathways in solution are evaluated by comparing their results with the OFEPs. In the first method the mean electrostatic field of the water is approximated by an analytical continuum model. The obtained paths show qualitative agreement with the OFEPs and the height of the barriers are similar. Targeted molecular dynamics ͑TMD͒, the second approach, constrains the distance to a target conformation to accelerate the transition. In the general case, however, it is difficult to assess the physical significance of the obtained paths. Changing the initial conditions by assigning different velocities leads to different solutions for the conformational transition. Furthermore, it is shown that by performing the simulations with different reaction coordinates or in opposite directions different pathways are preferred. This result can be explained by the structure of the free energy landscape around the initial conformations. In a first approximation the physical significance of different pathways is assumed to depend mainly on the free energy at the highest saddle point. In the literature the total energy of the system has often been used to estimate the position and the height of the energy barriers in the path. By comparing the total energy with the calculated free energy it is shown that the former largely overestimates the height of the barriers. Furthermore, the positions of the maxima of the total energy do not coincide with the free energy barriers. Simple approximations to the free energy lead to good quantitative agreement.