Chemoinformatic Approaches for Inhibitors of DNA Methyltransferases: Comprehensive Characterization of Screening Libraries

Jakyung Yoo, José Luis Medina-Franco

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DOI: 10.4236/cmb.2011.11002                

Abstract

Virtual screening of compound databases is a promising approach to identify inhibitors of DNA methyltransferases and other epigenetic targets. An important first step before conducting virtual screening is to characterize the structural diversity and chemical space coverage of the screening collections. Herein, we report a comprehensive chemoinformatic characterization of novel screening libraries, including a focused collection directed to inhibitors of DNA methyltransferases (DNMTs), and two natural product databases. The compound databases were assessed in terms of physicochemical properties, molecular scaffolds, and fingerprints. As part of the scaffold diversity analysis, a recently developed method, based on Shannon Entropy, was used. The overall approach enabled the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity. Overall, the analysis of the distribution of physicochemical properties indicates that the DNMT focused library and the two natural products collections have molecules with properties similar to approved drugs. Moreover, the natural products databases analyzed in this work have different chemical structures from approved drugs and synthetic databases and therefore are attractive for virtual screening for DNMT inhibitors. The scaffold analysis revealed that the focused library has, overall, the largest scaffold diversity and that the most frequent scaffolds are not identified in the other analyzed collections. Therefore, the focused library is also attractive to perform virtual and experimental screening for novel inhibitors. This study represents a first step towards the virtual screening of novel compound databases to identify inhibitors of DNMTs. Results of this study are general and can be used for the virtual screening of the compound databases against targets directed to other therapeutic applications.

Keywords

Chemical Space, Epigenetics, Fingerprints, Natural Products, Scaffold Analysis, Shannon Entropy

Virtual screening of compound databases is a promising approach to identify inhibitors of DNA methyltransferases and other epigenetic targets. An important first step before conducting virtual screening is to characterize the structural diversity and chemical space coverage of the screening collections. Herein, we report a comprehensive chemoinformatic characterization of novel screening libraries, including a focused collection directed to inhibitors of DNA methyltransferases (DNMTs), and two natural product databases. The compound databases were assessed in terms of physicochemical properties, molecular scaffolds, and fingerprints. As part of the scaffold diversity analysis, a recently developed method, based on Shannon Entropy, was used. The overall approach enabled the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity. Overall, the analysis of the distribution of physicochemical properties indicates that the DNMT focused library and the two natural products collections have molecules with properties similar to approved drugs. Moreover, the natural products databases analyzed in this work have different chemical structures from approved drugs and synthetic databases and therefore are attractive for virtual screening for DNMT inhibitors. The scaffold analysis revealed that the focused library has, overall, the largest scaffold diversity and that the most frequent scaffolds are not identified in the other analyzed collections. Therefore, the focused library is also attractive to perform virtual and experimental screening for novel inhibitors. This study represents a first step towards the virtual screening of novel compound databases to identify inhibitors of DNMTs. Results of this study are general and can be used for the virtual screening of the compound databases against targets directed to other therapeutic applications.

1. Introduction

Inhibitors of DNMT are relevant for the treatment of cancer and other diseases [1-3]. Most of the inhibitors known so far have been identified fortuitously. Only two drugs, 5-azacytidine and 5-aza-2’-deoxycytidine (decitabine), have been developed clinically. These drugs, however, have relatively low specificity and are characterized by substantial cellular and clinical toxicity [4]. Therefore, there is an urgent need to identify novel and more specific DNMT inhibitors that do not function via incorporation into DNA. To this end, computational approaches are increasingly being used to better understand at the molecular level the mechanism of established inhibitors of DNMTs [5].

Chemical libraries are becoming and important role for the discovery of inhibitors of DNMT. Structure-based virtual screening of the National Cancer Institute (NCI) database [6], followed by experimental validation, has identified hits with novel scaffolds [6,7]. Promising hits have been proposed from a docking-based virtual screening of a large natural product collection available in the ZINC database [8]. There are several additional promising databases for structureand ligand-based virtual screening for novel DNMT inhibitors and other molecular targets [9]. For example, the Traditional Chinese Medicine (TCM) represents an attractive source to identify novel inhibitors of natural origin. Also, a focused library for DNMT inhibitors has been recently developed. An initial and important step before conducting the virtual screening of these compound databases is the comprehensive characterization of the molecular properties, scaffold content and chemical space coverage of the screening libraries [10,11]. There are available several chemoinformatic tools that have been employed by the authors and other research groups to analyze chemical libraries. Representative and recent examples include the characterization of the NCI database [12], a natural product collection available in ZINC [12] and other natural products databases [14,15], several commercially available libraries and approved drugs [16,17] and public repositories [18].

As part of on-going efforts to conduct virtual screening for novel DNMT inhibitors [19-21] and compounds directed to other targets of therapeutic interest [22,23] herein, we report a comprehensive chemoinformatic characterization of a focused library on DNMT inhibitors, two natural products databases including the TCM collection available in ZINC, and other reference databases. The analysis was performed using a comprehensive and complementary set of criteria including physicochemical properties, molecular fingerprints, and scaffolds [13].

2. Methods

2.1. Data Sets

We analyzed two natural product databases including TCM implemented in ZINC [24], a recently developed DNMT focused library, and the NCI diversity set that was used as a reference. Table 1 summarizes the source and sizes each collection after removal of duplicates. In addition, a collection of 1403 approved drugs obtained from DrugBank [25] was used as a reference to characterize the physicochemical properties and structural diversity of the screening collections. All molecular databases were protonated and prepared using the “Wash” function implemented in Molecular Operating Environment (MOE, v2010. 10, Chemical Computing Group, Montreal, Canada).

Table 1. Molecular databases characterized in this work.

2.2. Physicochemical Properties

The following properties were computed with MOE: molecular weight (MW), number of rotatable bonds (RB), hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), topological polar surface area (TPSA), and the octanol/water partition coefficient (SlogP). The six descriptors used here have been used widely to compare the property space of compound collections [12,13,26].

2.3. Molecular Fingerprints

Molecules were represented by 2D MACCS key fingerprints (166 bits) [27] as implemented in MOE and the similarity of the i-th and j-th molecules was computed using the well-known Tanimoto similarity coefficient [28,29]:

(1)

where a and b are the number of  fragment bits corresponding to the i-th and j-th molecules and c is the number of fragment bits common to both molecules. Despite some caveats related to size-dependent effects [30,31], the Tanimoto coefficient is the measure of choice to assess the molecular similarity of molecules based on 2-D fingerprints, because on its extensive usage in a wide variety of studies [32]. To obtain a visual representation of the chemical space, a subset of 1000 compounds was randomly selected from each database in the molecular weight range 60 - 1000. Several visualization methods of the chemical space are available [16,17,33,34]. In this work, a visual representation of the chemical space [33] was obtained with principal component analysis (PCA) of the similarity matrix of the databases computed using MACCS keys and the Tanimoto coefficient [35]. PCA was carried out in Spotfire 7.1.2 [36].

2.4. Scaffold Content and Diversity

In this work the scaffolds were defined as the cyclic systems that result from iteratively removing all vertices of degree one, in other words, by iteratively removing the side chains of the molecule. The cyclic systems are part of the chemotype methodology and were computed with Molecular Equivalence Indices (MEQI) developed by Johnson and Xu [37]. A chemotype code or chemotype identifier (a code of five characters) is assigned to each scaffold using a unique naming algorithm. This approach has been successfully used to classify collections of combinatorial libraries, drugs, natural products, and other compound databases [13,38,39]. An advantageous feature of using cyclic systems to compare databases is that they represent equivalence classes and molecules classified in a given cyclic system [40]. The number of scaffolds in each database were recorded along with the number of scaffolds containing only one compound (e.g., singletons) using MEQI. The fraction of scaffolds relative to the data set size, and the fraction of singletons relative to the data set size and relative to the number of scaffolds provide information regarding the scaffold diversity in the collection. The distribution of molecules over the different scaffolds was obtained using the cyclic systems retrieval (CSR) curves [38-41]. In these curves, the fraction of cyclic systems (x) is plotted by the fraction of compounds (y) that contain those cyclic systems. The CSR curves were further characterized by obtaining the fraction of cyclic systems required to retrieve 50% of the corresponding database and the area under the curve (AUC).

The specific distribution of compounds in the n most populated cyclic systems was quantified with the implementation of the Shannon entropy (SE) [42,43] the authors introduced recently [38]. The SE of a population of P compounds contained in n cyclic systems is defined as:

; (2)

where p_i is the relative frequency of the cyclic system i in a population of P compounds containing a total of n distinct cyclic systems; c_i corresponds to the absolute number of molecules containing a particular cyclic system i. The values of SE range between 0 and log₂n and hence depend on n, but not explicitly on P. If SE = 0, then all P compounds possess only a single cyclic system. If SE = log₂n, then the P compounds are uniformly distributed among the n cyclic systems which represents maximum cyclic system diversity on the data set. To normalize the SE values for different values of n, the scaled SE (SSE) is defined as [43]:

(3)

The values of SSE range between 0, where all P compounds are contained in one cyclic system, and 1.0, where each cyclic system contains an equal number of compounds. Therefore, SSE values closer to 1.0 indicate large scaffold diversity within the n most populated cyclic systems.

3. Results and Discussion

3.1. Physicochemical Properties

Figure 1 summarizes the distribution of the six physicochemical properties described as box plots implemented in Spotfire 9.1.2. The three important molecular properties of size, flexibility, and molecular polarity are described by MW; RB; and SlogP, TPSA, HBA, and HBD, respectively. The yellow boxes enclose data points with values within the first and third quartiles of the distribution; the lines above and below indicate the upper and lower adjacent values. The black and blue triangles denote the mean and median of distributions, respectively, and the red squares indicate outliers. The summary of the maximum, minimum, median, mean, and standard deviations of the distributions are presented at the bottom of the box plots. According to the distribution of properties in Figure 1, the DNMT focused library (labeled in this figure as “ChemDiv”), has slightly larger number of HBA than drugs as reflected by the mean and median values. The two natural products collections, Specs and TCM, have a distribution of HBA similar to drugs although with lower mean values. Only NCI diverse set has a narrower distribution of HBA than drugs. Overall, the four libraries have a smaller number of HDB than drugs. The focused library and Specs databases have the same median values of HDB. Specs, TCM and NCI databases have lower values of RB than drugs. The focused library has similar values of RB as drugs. The NCI diverse set and TCM databases have a distribution of SlogP values similar to drugs as reflected by the median and mean values. In comparison, the focused library and Specs have slightly larger SlogP values than drugs. It means that the focused library and Specs are more hydrophobic than drugs and the other databases. The distribution of TPSA values of the four databases was similar to drugs. Regarding MW, the focused library and Specs are similar to drugs, while NCI and TCM have slightly smaller values. Taken together, the analysis of the distribution of physicochemical properties indicates that the DNMT focused library and the two natural products collections have molecules with properties similar to approved drugs.

3.2. Molecular Fingerprints and Chemical Space

Figure 2 shows a visual representation of the chemical space obtained with PCA of the similarity matrix using MACCS keys and Tanimoto coefficient as described in the Methods section. The first three principal components account for 80% of the variance. Figure 2(a) shows all databases in the same space. For the sake of clarity, Figure 2(b)-(f) shows a comparison of approved drugs with each compound collection separately but within the same coordinates. As a reference, we included in the chemical space the position of the known DNMT inhibitor SGI- 1027 [44] (the chemical structure is shown in Figure 2(b)). This compound is particularly attractive because it seems to have a distinct mode of enzymatic inhibition of DNMT and represents an attractive reference compound for similarity-based virtual screening. Figure 2(b) shows that SGI- 1027 is within the chemical space of drugs. Figure 2(c) clearly shows that the DNMT focused library is located within the dense populated area of the drugs and that the

Conflicts of Interest

The authors declare no conflicts of interest.

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