Michal Vieth

Detailed analysis of grid-based molecular docking: A case study of CDOCKER—A CHARMm-based MD docking algorithm

The influence of various factors on the accuracy of protein‐ligand docking is examined. The factors investigated include the role of a grid representation of protein‐ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper‐surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated‐annealing‐based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid‐based approximations to explicit all‐atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all‐atom representation of the protein (full force field) is used, while a lower accuracy of 66–76% is observed for grid‐based methods. All docking experiments considered a 41‐member protein‐ligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid‐based docking is achieved if the explicit all‐atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower‐accuracy grid‐based energy representations can be effectively used when followed with full force field minimization. The results of these grid‐based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures.

Kinomics: characterizing the therapeutically validated kinase space

The annotation and visualization of medicinally relevant kinase space revealed that kinase inhibitors in the clinic are, on average, of higher molecular weight and more lipophilic than all other clinically investigated drugs. Tyrosine kinases from the vascular endothelial growth factor and epidermal growth factor receptor families are the most pursued targets. Furthermore, oncological indications account for 75% of all kinase-related clinical interest. In addition, analysis of the similarity between kinase targets with respect to sequence, selectivity and structure has revealed that kinases with > or =60% sequence identity are most likely to be inhibited by the same classes of compounds and have similar ATP-binding sites. The identification of this threshold, together with the widely accepted representation of the sequence-based kinase space, is expanding our understanding of the clinical and structural space of the kinome.

Assessing Energy Functions for Flexible Docking

A good docking algorithm requires an energy function that is selective, in that it clearly differentiates correctly docked structures from misdocked ones, and that is efficient, meaning that a correctly docked structure can be identified quickly. We assess the selectivity and efficiency of a broad spectrum of energy functions, derived from systematic modifications of the CHARMM param19/toph19 energy function. In particular, we examine the effects of the dielectric constant, the solvation model, the scaling of surface charges, reduction of van der Waals repulsion, and nonbonded cutoffs. Based on an assessment of the energy functions for the docking of five different ligand–receptor complexes, we find that selective energy functions include a variety of distance‐dependent dielectric models together with truncation of the nonbonded interactions at 8 Å. We evaluate the docking efficiency, the mean number of docked structures per unit of time, of the more selective energy functions, using a simulated annealing molecular dynamics protocol. The largest improvements in efficiency come from a reduction of van der Waals repulsion and a reduction of surface charges. We note that the most selective potential is quite inefficient, although a hierarchical approach can be employed to take advantage of both selective and efficient energy functions. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1612–1622, 1998

Assessing Search Strategies for Flexible Docking

We assess the efficiency of molecular dynamics (MD), Monte Carlo (MC), and genetic algorithms (GA) for docking five representative ligand–receptor complexes. All three algorithms employ a modified CHARMM‐based energy function. The algorithms are also compared with an established docking algorithm, AutoDock. The receptors are kept rigid while flexibility of ligands is permitted. To test the efficiency of the algorithms, two search spaces are used: an 11‐Å‐radius sphere and a 2.5‐Å‐radius sphere, both centered on the active site. We find MD is most efficient in the case of the large search space, and GA outperforms the other methods in the small search space. We also find that MD provides structures that are, on average, lower in energy and closer to the crystallographic conformation. The GA obtains good solutions over the course of the fewest energy evaluations. However, due to the nature of the nonbonded interaction calculations, the GA requires the longest time for a single energy evaluation, which results in a decreased efficiency. The GA and MC search algorithms are implemented in the CHARMM macromolecular package. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1623–1631, 1998

Structure-guided expansion of kinase fragment libraries driven by support vector machine models

This work outlines a new de novo design process for the creation of novel kinase inhibitor libraries. It relies on a profiling paradigm that generates a substantial amount of kinase inhibitor data from which highly predictive QSAR models can be constructed. In addition, a broad diversity of X-ray structure information is needed for binding mode prediction. This is important for scaffold and substituent site selection. Borrowing from FBDD, the process involves fragmentation of known actives, proposition of binding mode hypotheses for the fragments, and model-driven recombination using a pharmacophore derived from known kinase inhibitor structures. The support vector machine method, using Merck atom pair derived fingerprint descriptors, was used to build models from activity from 6 kinase assays. These models were qualified prospectively by selecting and testing compounds from the internal compound collection. Overall hit and enrichment rates of 82% and 2.5%, respectively, qualified the models for use in library design. Using the process, 7 novel libraries were designed, synthesized and tested against these same 6 kinases. The results showed excellent results, yielding a 92% hit rate for the 179 compounds that made up the 7 libraries. The results of one library designed to include known literature compounds, as well as an analysis of overall substituent frequency, are discussed.

What general conclusions can we draw from kinase profiling data sets

Understanding general selectivity trends across the kinome has implications ranging from target selection, compound prioritization, toxicity and patient tailoring. Several recent publications have described the characterization of kinase inhibitors via large assay panels, offering a range of generalizations that influenced kinase inhibitor research trends. Since a subset of profiled inhibitors overlap across reports, we evaluated the concordance of activity results for the same compound-kinase pairs across four data sources generated from different kinase biochemical assay technologies. Overall, 77% of all results are within 3 fold or qualitatively in agreement across sources. However, the agreement for active compounds is only 37%, indicating that different profiling panels are in better agreement to determine a compounds lack of activity rather than degree of activity. Low concordance is also found when comparing the promiscuity of kinase targets evaluated from different sources, and the pharmacological similarity of kinases. In contrast, the overall promiscuity of kinase inhibitors was consistent across sources. We highlight the difficulty of drawing general conclusions from such data by showing that no significant selectivity difference distinguishes type I vs. type II inhibitors, and limited kinase space similarity that is consistent across different sources. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).

A High-Throughput Screen against Pantothenate Synthetase (PanC) Identifies 3-Biphenyl-4-Cyanopyrrole-2-Carboxylic Acids as a New Class of Inhibitor with Activity against Mycobacterium tuberculosis

The enzyme pantothenate synthetase, PanC, is an attractive drug target in Mycobacterium tuberculosis. It is essential for the in vitro growth of M. tuberculosis and for survival of the bacteria in the mouse model of infection. PanC is absent from mammals. We developed an enzyme-based assay to identify inhibitors of PanC, optimized it for high-throughput screening, and tested a large and diverse library of compounds for activity. Two compounds belonging to the same chemical class of 3-biphenyl-4- cyanopyrrole-2-carboxylic acids had activity against the purified recombinant protein, and also inhibited growth of live M. tuberculosis in manner consistent with PanC inhibition. Thus we have identified a new class of PanC inhibitors with whole cell activity that can be further developed.

A simple technique to estimate partition functions and equilibrium constants from Monte Carlo simulations

A combined Monte Carlo (MC) simulation‐statistical mechanical treatment is proposed to calculate the internal partition function and equilibrium constant. The method has been applied to a number of one and multidimensional analytical functions. When sampling is incomplete, various factorization approximations for estimating the partition function are discussed. The resulting errors are smaller when the ratios of the partition functions are calculated (as in the determination of equilibrium constants) as opposed to the partition function itself.

Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compounds crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocols pose prediction accuracy (80-82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs.

Selectivity Data: Assessment, Predictions, Concordance, and Implications

Could high-quality in silico predictions in drug discovery eventually replace part or most of experimental testing? To evaluate the agreement of selectivity data from different experimental or predictive sources, we introduce the new metric concordance minimum significant ratio (cMSR). Empowered by cMSR, we find the overall level of agreement between predicted and experimental data to be comparable to that found between experimental results from different sources. However, for molecules that are either highly selective or potent, the concordance between different experimental sources is significantly higher than the concordance between experimental and predicted values. We also show that computational models built from one data set are less predictive for other data sources and highlight the importance of bias correction for assessing selectivity data. Finally, we show that small-molecule target space relationships derived from different data sources and predictive models share overall similarity but can significantly differ in details.