Weifan Zheng

Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques

Computationally efficient structure-based virtual screening methods have recently been reported that seek to find effective means to utilize experimental structure information without employing detailed molecular docking calculations. These tools can be coupled with efficient experimental screening technologies to improve the probability of identifying hits and leads for drug discovery research. Commercial software ROCS (rapid overlay of chemical structures) from Open Eye Scientific is such an example, which is a shape-based virtual screening method using the 3D structure of a ligand, typically from a bound X-ray costructure, as the query. We report here the development of a new structure-based pharmacophore search method (called Shape4) for virtual screening. This method adopts a variant of the ROCS shape technology and expands its use to work with an empty crystal structure. It employs a rigorous computational geometry method and a deterministic geometric casting algorithm to derive the negative image (i.e., pseudoligand) of a target binding site. Once the negative image (or pseudoligand) is generated, an efficient shape comparison algorithm in the commercial OE SHAPE Toolkit is adopted to compare and match small organic molecules with the shape of the pseudoligand. We report the detailed computational protocol and its computational validation using known biologically active compounds extracted from the WOMBAT database. Models derived for five selected targets were used to perform the virtual screening experiments to obtain the enrichment data for various virtual screening methods. It was found that our approach afforded similar or better enrichment ratios than other related methods, often with better diversity among the top ranking computational hits.

Molecular shape technologies in drug discovery: methods and applications.

Shape complementarity is a critically important factor in molecular recognition among drugs and their biological receptors. The notion that molecules with similar 3D shapes tend to have similar biological activity has been recognized and implemented in computational drug discovery tools for decades. But the low computational efficiency and the lack of widely accessible software tools limited the use of early shape-matching algorithms. However, recent development of fast and accurate shape comparison tools has changed the landscape, and facilitated the wide spread use of both the ligand-based and receptor-based shape-matching technologies in drug discovery. In this article, we summarize some of the well-known shape algorithms. We first describe the computational principles for both the superposition-based and the superposition-free shape-matching methods. These include ROCS (Rapid Overlay of Compound Structures), SQ, and the CatShape method in the former category; and the shape signatures algorithm and USR (Ultrafast Shape Recognition) that belong to the latter category. We then highlight some recent validation studies and practical applications of various shape technologies. Because of the rapid development of modern shape-matching algorithms, and the increasingly affordable computational resources and software tools, we anticipate much broader use of the molecular shape technologies in future drug discovery. They will be especially useful in chemogenomics research, where large scale associations between small molecules and protein targets are studied. Thus, molecular shape technologies, together with well-defined pharmacophore constraints, can afford both efficient and effective means for drug discovery and chemical genomics research.

Unconventional 2D shape similarity method affords comparable enrichment as a 3D shape method in virtual screening experiments.

3D molecular shape similarity search has recently become an attractive method for virtual screening and scaffold hopping in drug discovery and chemical genomics research. Among these 3D similarity methods is ROCS (Rapid Overlay of Chemical Structures), a popular tool because of its efficiency and effectiveness. However, searching a large multiconformer molecular database remains a very challenging task because of the nature of such calculations. To simplify shape similarity calculations and potentially increase the efficiency for large scale virtual screening, we have explored an alternative shape similarity approach that does not depend on multiconformers of molecules. The hypothesis underlying this approach is that similar chemical structures tend to have similar 2D chemical depictions and that shape comparison techniques can be utilized to effectively compare the shapes between chemical depictions. We use a 2D depiction program to generate 2-D chemical drawings for both the query molecule and database molecules. We have built a 2D shape comparison program based on the OESHAPE Toolkit (OE Scientific, NM) that compares the molecular depictions and quantifies the shape similarity between the molecules. We demonstrate that this unconventional 2D shape similarity method performs fairly well in virtual screening experiments compared to the 3D Shape method ROCS, with an added advantage of its computational efficiency.

Structure-based shape pharmacophore modeling for the discovery of novel anesthetic compounds

Current anesthetics, especially the inhaled ones, have troublesome side effects and may be associated with durable changes in cognition. It is therefore highly desirable to develop novel chemical entities that reduce these effects while preserving or enhancing anesthetic potency. In spite of progress toward identifying protein targets involved in anesthesia, we still do not have the necessary atomic level structural information to delineate their interactions with anesthetic molecules. Recently, we have described a protein target, apoferritin, to which several anesthetics bind specifically and in a pharmacodynamically relevant manner. Further, we have reported the high resolution X-ray structure of two anesthetic/apoferritin complexes (Liu, R.; Loll, P. J.; Eckenhoff, R. G. FASEB J. 2005, 19, 567). Thus, we describe in this paper a structure-based approach to establish validated shape pharmacophore models for future application to virtual and high throughput screening of anesthetic compounds. We use the 3D structure of apoferritin as the basis for the development of several shape pharmacophore models. To validate these models, we demonstrate that (1) they can be used to effectively recover known anesthetic agents from a diverse database of compounds; (2) the shape pharmacophore scores afford a significant linear correlation with the measured binding energetics of several known anesthetic compounds to the apoferritin site; and (3) the computed scores based on the shape pharmacophore models also predict the trend of the EC(50) values of a set of anesthetics. Therefore, we have now obtained a set of structure-based shape pharmacophore models, using ferritin as the surrogate target, which may afford a new way to rationally discover novel anesthetic agents in the future.

From Anesthetic Mechanisms Research to Drug Discovery

The ability to render patients insensible and amnesic to remarkably invasive procedures that are uncomfortable to watch, let alone experience, has been rightly designated as one of the greatest medical discoveries of all time. General anesthesia, introduced formally in the mid‐nineteenth century, is now delivered to ~40 million patients every year in the United States alone. Given its central role in health care, it is indeed extraordinary how poorly we understand anesthesia and anesthetics. In fact, definitions are at best operational and convey little understanding of the underlying neurobiology, while the hypothetical mechanisms are surprisingly superficial. Worse, there is growing concern that the anesthetic drugs in current use, especially the inhaled anesthetics, have durable adverse effects on cognition.

Receptor-Based Pharmacophore and Pharmacophore Key Descriptors for Virtual Screening and QSAR Modeling

The intuitive nature of the pharmacophore concept has made it widely accepted by the medicinal chemistry community, evidenced by the past 3 decades of development and application of computerized pharmacophore modeling tools. On the other hand, shape complementarity has been recognized as a critical factor in molecular recognition between drugs and their receptors. Recent development of fast and accurate shape comparison tools has facilitated the wide spread use of shape matching technologies in drug discovery. However, pharmacophore and shape technologies, if used separately, often lead to high false positive rate. Thus, integrating pharmacophore matching and shape matching technologies into one program has the potential to reduce the false positive rates in virtual screening. Other issues of current pharmacophore technologies include sometimes high false negative rate and non-quantitative prediction. In this article, we first focus on a recently implemented method (Shape4) that combines receptor based shape matching and pharmacophore comparison in a single algorithm to create shape pharmacophore models for virtual screening. We also examine a recent example that utilizes multi-complex information to develop receptor-based pharmacophore models that promises to reduce false negative rate. Finally, we review several methods that employ receptor-based pharmacophore map and pharmacophore key descriptors for QSAR modeling. We conclude by emphasizing the concept of receptor-based shape pharmacophore and its roles in future drug discovery.

A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hanschs seminal work, and more are still being developed. Motivated by Hopfingers receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method.

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecules pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

Informatics Approach to the Rational Design of siRNA Libraries

This chapter surveys the literature for state-of-the-art methods for the rational design of siRNA libraries. It identifies and presents major milestones in the field of computational modeling of siRNAs gene silencing efficacy. Commonly used features of siRNAs are summarized along with major machine learning techniques employed to build the predictive models. It has also outlined several web-enabled siRNA design tools. To face the challenge of modeling and rational design of chemically modified siRNAs, it also proposes a new cheminformatics approach for the representation and characterization of siRNA molecules. Some preliminary results with this new approach are presented to demonstrate the promising potential of this method for the modeling of siRNAs efficacy. Together with novel delivery technologies and chemical modification techniques, rational siRNA design algorithms will ultimately contribute to chemical biology research and the efficient development of siRNA therapeutics.

Cheminformatics Approach to Gene Silencing: Z Descriptors of Nucleotides and SVM Regression Afford Predictive Models for siRNA Potency.

Short interfering RNA mediated gene silencing technology has been through tremendous development over the past decade, and has found broad applications in both basic biomedical research and pharmaceutical development. Critical to the effective use of this technology is the development of reliable algorithms to predict the potency and selectivity of siRNAs under study. Existing algorithms are mostly built upon sequence information of siRNAs and then employ statistical pattern recognition or machine learning techniques to derive rules or models. However, sequence‐based features have limited ability to characterize siRNAs, especially chemically modified ones. In this study, we proposed a cheminformatics approach to describe siRNAs. Principal component scores (z1, z2, z3, z4) have been derived for each of the 5 nucleotides (A, U, G, C, T) from the descriptor matrix computed by the MOE program. Descriptors of a given siRNA sequence are simply the concatenation of the z values of its composing nucleotides. Thus, for each of the 2431 siRNA sequences in the Huesken dataset, 76 descriptors were generated for the 19‐NT representation, and 84 descriptors were generated for the 21‐NT representation of siRNAs. Support Vector Machine regression (SVMR) was employed to develop predictive models. In all cases, the models achieved Pearson correlation coefficient r and R about 0.84 and 0.65 for the training sets and test sets, respectively. A minimum of 25 % of the whole dataset was needed to obtain predictive models that could accurately predict 75 % of the remaining siRNAs. Thus, for the first time, a cheminformatics approach has been developed to successfully model the structure–potency relationship in siRNA‐based gene silencing data, which has laid a solid foundation for quantitative modeling of chemically modified siRNAs.