Cation-π interactions of methylated ammonium ions: a quantum mechanical study.

Cation-π interactions of methylated ammonium ions play a key role in a broad range of biochemical systems. These include methyl-lysine binding proteins which bind to methylated sites on histone proteins, lysine demethylase enzymes which demethylate these sites, and neurotransmitter receptor complexes which bind choline-derived ligands. Recognition in these systems is achieved through an 'aromatic cage' motif in the binding site. Here we use high-level quantum mechanical calculations to address how cation-π interactions of methylated ammonium ions are modulated by a change in methylation state and interaction geometry. We survey methyl-lysine and choline-derived complexes in the Protein Databank to validate our results against available structural data. A quantitative description of cation-π interactions of methylated ammonium systems is critical to structure-based efforts to target methyl-lysine binding proteins and demethylase enzymes in the treatment of unregulated transcriptional control, and neurotransmitter receptors in the treatment of neurological disease. It is our hope that our work will serve as a benchmark for the development of physical chemistry based force fields that can accurately model the contribution of cation-π interactions to binding and specificity in these systems.

The role of tyrosine sulfation in the dimerization of the CXCR4:SDF-1 complex.

Oligomerization of G protein-coupled receptors is a recognized mode of regulation of receptor activities, with alternate oligomeric states resulting in different signaling functions. The CXCR4 chemokine receptor is a G protein-coupled receptor that is post-translationally modified by tyrosine sulfation at three sites on its N-terminus (Y7, Y12, Y21), leading to enhanced affinity for its ligand, stromal cell derived factor (SDF-1, also called CXCL12). The complex has been implicated in cancer metastasis and is a therapeutic target in cancer treatment. Using molecular dynamics simulation of NMR-derived structures of the CXCR4 N-terminus in complex with SDF-1, and calculations of electrostatic binding energies for these complexes, we address the role of tyrosine sulfation in this complex. Our results show that sulfation stabilizes the dimeric state of the CXCR4:SDF-1 complex through hydrogen bonding across the dimer interface, conformational changes in residues at the dimer interface, and an enhancement in electrostatic binding energies associated with dimerization. These findings suggest a mechanism through which post-translational modifications such as tyrosine sulfation might regulate downstream function through modulation of the oligomeric state of the modified system.

Hydrogen bond strengths in phosphorylated and sulfated amino acid residues.

Post-translational modification by the addition of an oxoanion functional group, usually a phosphate group and less commonly a sulfate group, leads to diverse structural and functional consequences in protein systems. Building upon previous studies of the phosphoserine residue (pSer), we address the distinct nature of hydrogen bonding interactions in phosphotyrosine (pTyr) and sulfotyrosine (sTyr) residues. We derive partial charges for these modified residues and then study them in the context of molecular dynamics simulation of model tripeptides and sulfated protein complexes, potentials of mean force for interacting residue pairs, and a survey of the interactions of modified residues among experimental protein structures. Overall, our findings show that for pTyr, bidentate interactions with Arg are particularly dominant, as has been previously demonstrated for pSer. sTyr interactions with Arg are significantly weaker, even as compared to the same interactions made by the Glu residue. Our work sheds light on the distinct nature of these modified tyrosine residues, and provides a physical-chemical foundation for future studies with the goal of understanding their roles in systems of biological interest.

A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series.

We introduce the "Prime-ligand" method for ranking ligands in congeneric series. The method employs a single scoring function, the OPLS-AA/GBSA molecular mechanics/implicit solvent model, for all stages of sampling and scoring. We evaluate the method using 12 test sets of congeneric series for which experimental binding data is available in the literature, as well as the structure of one member of the series bound to the protein. Ligands are "docked" by superimposing a common stem fragment among the compounds in the series using a crystal complex from the Protein Data Bank and sampling the conformational space of the variable region. Our results show good correlation between our predicted rankings and the experimental data for cases in which binding affinities differ by at least 1 order of magnitude. For 11 out of 12 cases, >90% of such ligand pairs could be correctly ranked, while for the remaining case, Factor Xa, 76% of such pairs were correctly ranked. A small number of compounds could not be docked using the current protocol because of the large size of functional groups that could not be accommodated by a rigid receptor. CPU requirements for the method, involving CPU minutes per ligand, are modest compared with more rigorous methods that use similar force fields, such as free energy perturbation. We also benchmark the scoring function using series of ligands bound to the same protein within the CSAR data set. We demonstrate that energy minimization of ligands in the crystal structures is critical to obtain any correlation with experimentally determined binding affinities.

Automated site preparation in physics-based rescoring of receptor ligand complexes.

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.

Prediction of protein loop geometries in solution.

The ability to determine the structure of a protein in solution is a critical tool for structural biology, as proteins in their native state are found in aqueous environments. Using a physical chemistry based prediction protocol, we demonstrate the ability to reproduce protein loop geometries in experimentally derived solution structures. Predictions were run on loops drawn from (1)NMR entries in the Protein Databank (PDB), and from (2) the RECOORD database in which NMR entries from the PDB have been standardized and re-refined in explicit solvent. The predicted structures are validated by comparison with experimental distance restraints, a test of structural quality as defined by the WHAT IF structure validation program, root mean square deviation (RMSD) of the predicted loops to the original structural models, and comparison of precision of the original and predicted ensembles. Results show that for the RECOORD ensembles, the predicted loops are consistent with an average of 95%, 91%, and 87% of experimental restraints for the short, medium and long loops respectively. Prediction accuracy is strongly affected by the quality of the original models, with increases in the percentage of experimental restraints violated of 2% for the short loops, and 9% for both the medium and long loops in the PDB derived ensembles. We anticipate the application of our protocol to theoretical modeling of protein structures, such as fold recognition methods; as well as to experimental determination of protein structures, or segments, for which only sparse NMR restraint data is available.

Strengths of hydrogen bonds involving phosphorylated amino acid side chains.

Post-translational phosphorylation plays a key role in regulating protein function. Here, we provide a quantitative assessment of the relative strengths of hydrogen bonds involving phosphorylated amino acid side chains (pSer, pAsp) with several common donors (Arg, Lys, and backbone amide groups). We utilize multiple levels of theory, consisting of explicit solvent molecular dynamics, implicit solvent molecular mechanics, and quantum mechanics with a self-consistent reaction field treatment of solvent. Because the approximately 6 pKa of phosphate suggests that -1 and -2 charged species may coexist at physiological pH, hydrogen bonds involving both protonated and deprotonated phosphates for all donor-acceptor pairs are considered. Multiple bonding geometries for the charged-charged interactions are also considered. Arg is shown to be capable of substantially stronger salt bridges with phosphorylated side chains than Lys. A pSer hydrogen-bond acceptor tends to form more stable interactions than a pAsp acceptor. The effect of phosphate protonation state on the strengths of the hydrogen bonds is remarkably subtle, with a more pronounced effect on pAsp than on pSer.

Crystal packing effects on protein loops.

The effects of crystal packing on protein loop structures are examined by (1) a comparison of loops in proteins that have been crystallized in alternate packing arrangements, and (2) theoretical prediction of loops both with and without the inclusion of the crystal environment. Results show that in a minority of cases, loop geometries are dependent on crystal packing effects. Explicit representation of the crystal environment in a loop prediction algorithm can be used to model these effects and to reconstruct the structures, and relative energies, of a loop in alternative packing environments. By comparing prediction results with and without the inclusion of the crystal environment, the loop prediction algorithm can further be used to identify cases in which a crystal structure does not represent the most stable state of a loop in solution. We anticipate that this capability has implications for structural biology.

A hierarchical approach to all-atom protein loop prediction.

The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.