Unmasking the True Identity of Rapamycin's Minor Conformer.

Rapamycin, a well-known macrocyclic natural product with myriad biological activities, has been the subject of intense study since its first isolation and characterization over five decades ago. Rapamycin has been found to adopt a single conformation in the solid state (both when protein bound and uncomplexed) and exists as a mixture of two conformations in solution. Early work established that the major conformer in solution is the trans amide isomer but left the minor conformer mostly uncharacterized. Since that time, it has been widely accepted that the minor conformer of rapamycin is the cis amide, based solely on analogy to FK-506, another potent immunosuppressive compound with some shared key structural elements. To address this long-standing and unresolved question, the solution structure of the minor conformer of rapamycin was investigated using a combination of NMR techniques and computational methods and determined to be a trans amide species with rotation about the ester linkage.

Decisions with Confidence: Application to the Conformation Sampling of Molecules in the Solid State.

Accurate conformations of a molecule are critical for reliable prediction of its properties, so good predictive models require good conformations. Here, we present a method for conformer sampling based on distance geometry, implemented in our conformation generator OMEGA, which we apply to both macrocycles and druglike molecules. We validate it in the usual fashion, reproducing conformations from the solid state, and compare its performance in detail to other methods. We find that OMEGA performs well on three key criteria: accuracy, speed, and ensemble size. To support our conclusions quantitatively, particularly on accuracy, we developed a workflow for method comparison that uses parameter estimation, inference from confidence intervals, classical null hypothesis significance testing, Bayesian estimation, and effect size. The workflow is designed to be robust to the highly skewed performance data often found when validating tools in computational chemistry and to provide reliable, easy to interpret results. In this workflow, we emphasize the importance of confidently distinguishing between methods, with particular reference to a priori estimation of sample size and statistical power (false negative or Type II error rate), a topic almost completely ignored hitherto in computational chemistry.

Conformation Generation: The State of the Art.

The generation of conformations for small molecules is a problem of continuing interest in cheminformatics and computational drug discovery. This review will present an overview of methods used to sample conformational space, focusing on those methods designed for organic molecules commonly of interest in drug discovery. Different approaches to both the sampling of conformational space and the scoring of conformational stability will be compared and contrasted, with an emphasis on those methods suitable for conformer sampling of large numbers of drug-like molecules. Particular attention will be devoted to the appropriate utilization of information from experimental solid-state structures in validating and evaluating the performance of these tools. The review will conclude with some areas worthy of further investigation.

The application of statistical methods to cognate docking: a path forward?

Cognate docking has been used as a test for pose prediction quality in docking engines for decades. In this paper, we report a statistically rigorous analysis of cognate docking performance using tools in the OpenEye docking suite. We address a number of critically important aspects of the cognate docking problem that are often handled poorly: data set quality, methods of comparison of the predicted pose to the experimental pose, and data analysis. The focus of the paper lies in the third problem, extracting maximally predictive knowledge from comparison data. To this end, we present a multistage protocol for data analysis that by combining classical null-hypothesis significance testing with effect size estimation provides crucial information about quantitative differences in performance between methods as well as the probability of finding such differences in future experiments. We suggest that developers of software and users of software have different levels of interest in different parts of this protocol, with users being primarily interested in effect size estimation while developers may be most interested in statistical significance. This protocol is completely general and therefore will provide the basis for method comparisons of many different kinds.

Conformer generation with OMEGA: learning from the data set and the analysis of failures.

We recently published a high quality validation set for testing conformer generators, consisting of structures from both the PDB and the CSD (Hawkins, P. C. D. et al. J. Chem. Inf. Model. 2010, 50, 572.), and tested the performance of our conformer generator, OMEGA, on these sets. In the present publication, we focus on understanding the suitability of those data sets for validation and identifying and learning from OMEGA's failures. We compare, for the first time we are aware of, the coverage of the applicable property spaces between the validation data sets we used and the parent compound sets to determine if our data sets adequately sample these property spaces. We also introduce the concept of torsion fingerprinting and compare this method of dissimilation to the more traditional graph-centric diversification methods we used in our previous publication. To improve our ability to programmatically identify cases where the crystallographic conformation is not well reproduced computationally, we introduce a new metric to compare conformations, RMSTanimoto. This new metric is used alongside those from our previous publication to efficiently identify reproduction failures. We find RMSTanimoto to be particularly effective in identifying failures for the smallest molecules in our data sets. Analysis of the nature of these failures, particularly those for the CSD, sheds further light on the issue of strain in crystallographic structures. Some of the residual failure cases not resolved by simple changes in OMEGA's defaults present significant challenges to conformer generation engines like OMEGA and are a source of new avenues to further improve their performance, while others illustrate the pitfalls of validating against crystallographic ligand conformations, particularly those from the PDB.

Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database.

Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.

Ligand-Based Methods in GPCR Computer-Aided Drug Design.

This chapter describes two powerful 3D ligand-based shape similarity and scoring methods called ROCS and EON, their basic operation and selected validation data. The steps required to prepare a database of molecules for successful use with ROCS and EON are described and selected examples of their application in prospective lead discovery experiments are summarized.

Conformational Sampling of Macrocyclic Drugs in Different Environments: Can We Find the Relevant Conformations?

Conformational flexibility is a major determinant of the properties of macrocycles and other drugs in beyond rule of 5 (bRo5) space. Prediction of conformations is essential for design of drugs in this space, and we have evaluated three tools for conformational sampling of a set of 10 bRo5 drugs and clinical candidates in polar and apolar environments. The distance-geometry based OMEGA was found to yield ensembles spanning larger structure and property spaces than the ensembles obtained by MOE-LowModeMD (MOE) and MacroModel (MC). Both MC and OMEGA but not MOE generated different ensembles for polar and apolar environments. All three conformational search methods generated conformers similar to the crystal structure conformers for 9 of the 10 compounds, with OMEGA performing somewhat better than MOE and MC. MOE and OMEGA found all six conformers of roxithromycin that were identified by NMR in aqueous solutions, whereas only OMEGA sampled the three conformers observed in chloroform. We suggest that characterization of conformers using molecular descriptors, e.g., the radius of gyration and polar surface area, is preferred to energy- or root-mean-square deviation-based methods for selection of biologically relevant conformers in drug discovery in bRo5 space.

Comparison of shape-matching and docking as virtual screening tools.

Ligand docking is a widely used approach in virtual screening. In recent years a large number of publications have appeared in which docking tools are compared and evaluated for their effectiveness in virtual screening against a wide variety of protein targets. These studies have shown that the effectiveness of docking in virtual screening is highly variable due to a large number of possible confounding factors. Another class of method that has shown promise in virtual screening is the shape-based, ligand-centric approach. Several direct comparisons of docking with the shape-based tool ROCS have been conducted using data sets from some of these recent docking publications. The results show that a shape-based, ligand-centric approach is more consistent than, and often superior to, the protein-centric approach taken by docking.

How to do an evaluation: pitfalls and traps.

The recent literature is replete with papers evaluating computational tools (often those operating on 3D structures) for their performance in a certain set of tasks. Most commonly these papers compare a number of docking tools for their performance in cognate re-docking (pose prediction) and/or virtual screening. Related papers have been published on ligand-based tools: pose prediction by conformer generators and virtual screening using a variety of ligand-based approaches. The reliability of these comparisons is critically affected by a number of factors usually ignored by the authors, including bias in the datasets used in virtual screening, the metrics used to assess performance in virtual screening and pose prediction and errors in crystal structures used.