Heather A. Carlson

Development of polyphosphate parameters for use with the AMBER force field

Accurate force fields are essential for reproducing the conformational and dynamic behavior of condensed‐phase systems. The popular AMBER force field has parameters for monophosphates, but they do not extend well to polyphorylated molecules such as ADP and ATP. This work presents parameters for the partial charges, atom types, bond angles, and torsions in simple polyphosphorylated compounds. The parameters are based on molecular orbital calculations of methyldiphosphate and methyltriphosphate at the RHF/6‐31+G* level. The new parameters were fit to the entire potential energy surface (not just minima) with an RMSD of 0.62 kcal/mol. This is exceptional agreement and a significant improvement over the current parameters that produce a potential surface with an RMSD of 7.8 kcal/mol to that of the ab initio calculations. Testing has shown that the parameters are transferable and capable of reproducing the gas‐phase conformations of inorganic diphosphate and triphosphate. Also, the parameters are an improvement over existing parameters in the condensed phase as shown by minimizations of ATP bound in several proteins. These parameters are intended for use with the existing AMBER 94/99 force field, and they will permit users to apply AMBER to a wider variety of important enzymatic systems.

Protein flexibility and drug design: how to hit a moving target

The most advanced methods for computer-aided drug design and database mining incorporate protein flexibility. Such techniques are not only needed to obtain proper results; they are also critical for dealing with the growing body of information from structural genomics.

Binding MOAD (Mother Of All Databases)

Binding MOAD (Mother of All Databases) is the largest collection of high‐quality, protein–ligand complexes available from the Protein Data Bank. At this time, Binding MOAD contains 5331 protein–ligand complexes comprised of 1780 unique protein families and 2630 unique ligands. We have searched the crystallography papers for all 5000+ structures and compiled binding data for 1375 (26%) of the protein–ligand complexes. The binding‐affinity data ranges 13 orders of magnitude. This is the largest collection of binding data reported to date in the literature. We have also addressed the issue of redundancy in the data. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 “best” complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This significant collection of protein–ligand complexes will be very useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding‐affinity data will help in the development of improved scoring functions and structure‐based drug discovery techniques. The dataset can be accessed at http://www.BindingMOAD.org. Proteins 2005.

Accuracy of free energies of hydration for organic molecules from 6-31G-derived partial charges

Absolute free energies of hydration have been computed for 13 diverse organic molecules using partial charges derived from ab initio 6‐31G* wave functions. Both Mulliken charges and charges fit to the electrostatic potential surface (EPS) were considered in conjunction with OPLS Lennard–Jones parameters for the organic molecules and the TIP4P model of water. Monte Carlo simulations with statistical perturbation theory yielded relative free energies of hydration. These were converted to absolute quantities through perturbations to reference molecules for which absolute free energies of hydration had been obtained previously in TIP4P water. The average errors in the computed absolute free energies of hydration are 1.1 kcal/mol for the 6‐31G* EPS charges and 4.0 kcal/mol for the Mulliken charges. For the EPS charges, the largest individual errors are under 2 kcal/mol except for acetamide, in which case the error is 3.7 kcal/mol. The hydrogen bonding between the organic solutes and water has also been characterized.

Binding MOAD, a high-quality protein–ligand database

Binding MOAD (Mother of All Databases) is a database of 9836 protein–ligand crystal structures. All biologically relevant ligands are annotated, and experimental binding-affinity data is reported when available. Binding MOAD has almost doubled in size since it was originally introduced in 2004, demonstrating steady growth with each annual update. Several technologies, such as natural language processing, help drive this constant expansion. Along with increasing data, Binding MOAD has improved usability. The website now showcases a faster, more featured viewer to examine the protein–ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. Lastly, logins are no longer necessary, and Binding MOAD is freely available to all at http://www.BindingMOAD.org.

CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions

As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein–ligand crystal structures were assembled with experimentally determined Kd or Ki information from the literature. We encouraged the community to score the crystallographic poses of the complexes by any method of their choice. The goal of the exercise was to (1) evaluate the current ability of the field to predict activity from structure and (2) investigate the properties of the complexes and methods that appear to hinder scoring. A total of 19 different methods were submitted with numerous parameter variations for a total of 64 sets of scores from 16 participating groups. Linear regression and nonparametric tests were used to correlate scores to the experimental values. Correlation to experiment for the various methods ranged R2 = 0.58–0.12, Spearman ρ = 0.74–0.37, Kendall τ = 0.55–0.25, and median unsigned error = 1.00–1.68 pKd units. All types of scoring functions—force field based, knowledge based, and empirical—had examples with high and low correlation, showing no bias/advantage for any particular approach. The data across all the participants were combined to identify 63 complexes that were poorly scored across the majority of the scoring methods and 123 complexes that were scored well across the majority. The two sets were compared using a Wilcoxon rank-sum test to assess any significant difference in the distributions of >400 physicochemical properties of the ligands and the proteins. Poorly scored complexes were found to have ligands that were the same size as those in well-scored complexes, but hydrogen bonding and torsional strain were significantly different. These comparisons point to a need for CSAR to develop data sets of congeneric series with a range of hydrogen-bonding and hydrophobic characteristics and a range of rotatable bonds.

CSAR Benchmark Exercise 2011–2012: Evaluation of Results from Docking and Relative Ranking of Blinded Congeneric Series

The Community Structure–Activity Resource (CSAR) recently held its first blinded exercise based on data provided by Abbott, Vertex, and colleagues at the University of Michigan, Ann Arbor. A total of 20 research groups submitted results for the benchmark exercise where the goal was to compare different improvements for pose prediction, enrichment, and relative ranking of congeneric series of compounds. The exercise was built around blinded high-quality experimental data from four protein targets: LpxC, Urokinase, Chk1, and Erk2. Pose prediction proved to be the most straightforward task, and most methods were able to successfully reproduce binding poses when the crystal structure employed was co-crystallized with a ligand from the same chemical series. Multiple evaluation metrics were examined, and we found that RMSD and native contact metrics together provide a robust evaluation of the predicted poses. It was notable that most scoring functions underpredicted contacts between the hetero atoms (i.e., N, O, S, etc.) of the protein and ligand. Relative ranking was found to be the most difficult area for the methods, but many of the scoring functions were able to properly identify Urokinase actives from the inactives in the series. Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended. Pregenerated ligand conformations performed better than those that were generated on the fly. Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting. Lastly, for both sampling and ranking scoring functions, the use of the empirical scoring function appeared to trend positively with the RMSD. Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

Binding of a Small Molecule at a Protein–Protein Interface Regulates the Chaperone Activity of Hsp70–Hsp40

Heat shock protein 70 (Hsp70) is a highly conserved molecular chaperone that plays multiple roles in protein homeostasis. In these various tasks, the activity of Hsp70 is shaped by interactions with co-chaperones, such as Hsp40. The Hsp40 family of co-chaperones binds to Hsp70 through a conserved J-domain, and these factors stimulate ATPase and protein-folding activity. Using chemical screens, we identified a compound, 115-7c, which acts as an artificial co-chaperone for Hsp70. Specifically, the activities of 115-7c mirrored those of a Hsp40; the compound stimulated the ATPase and protein-folding activities of a prokaryotic Hsp70 (DnaK) and partially compensated for a Hsp40 loss-of-function mutation in yeast. Consistent with these observations, NMR and mutagenesis studies indicate that the binding site for 115-7c is adjacent to a region on DnaK that is required for J-domain-mediated stimulation. Interestingly, we found that 115-7c and the Hsp40 do not compete for binding but act in concert. Using this information, we introduced additional steric bulk to 115-7c and converted it into an inhibitor. Thus, these chemical probes either promote or inhibit chaperone functions by regulating Hsp70-Hsp40 complex assembly at a native protein-protein interface. This unexpected mechanism may provide new avenues for exploring how chaperones and co-chaperones cooperate to shape protein homeostasis.

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein–ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein–ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured to better fit the needs of the community are presented. Many groups generously participated in improving the data set, and this underscores the value of a supportive, collaborative effort in moving our field forward.

Computational studies and peptidomimetic design for the human p53–MDM2 complex

The interaction between human p53 and MDM2 is a key event in controlling cell growth. Many studies have suggested that a p53 mimic would be sufficient to inhibit MDM2 to reduce cell growth in cancerous tissue. In order to design a potent p53 mimic, molecular dynamics (MD) simulations were used to examine the binding interface and the effect of mutating key residues in the human p53–MDM2 complex. The Generalized Born surface area (GBSA) method was used to estimate free energies of binding, and a computational alanine‐scanning approach was used to calculate the relative effects in the free energy of binding for key mutations. Our calculations determine the free energy of binding for a model p53–MDM2 complex to be −7.4 kcal/mol, which is in very good agreement with the experimentally determined values (−6.6–−8.8 kcal/mol). The alanine‐scanning results are in good agreement with experimental data and calculations by other groups. We have used the information from our studies of human p53–MDM2 to design a β‐peptide mimic of p53. MD simulations of the mimic bound to MDM2 estimate a free energy of binding of −8.8 kcal/mol. We have also applied alanine scanning to the mimic–MDM2 complex and reveal which mutations are most likely to alter the binding affinity, possibly giving rise to escape mutants. The mimic was compared to nutlins, a new class of inhibitors that block the formation of the p53–MDM2 complex. There are interesting similarities between the nutlins and our mimic, and the differences point to ways that both inhibitors may be improved. Finally, an additional hydrophobic pocket is noted in the interior of MDM2. It may be possible to design new inhibitors to take advantage of that pocket. Proteins 2005.