Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs

Activity cliffs are formed by pairs or groups of structurally similar compounds having large differences in potency and are focal points of structure-activity relationship (SAR) analysis. The choice of molecular representations is a critically important aspect of activity cliffs analysis. Thus far, activity cliffs have predominantly been defined on the basis of molecular graph or fingerprint representations. Herein we introduce 3D activity cliffs derived from comparisons of experimentally determined compound binding modes. The analysis of 3D activity cliffs is generally applicable to target proteins for which structures of multiple ligand complexes are available. For two popular targets, β-secretase 1 (BACE1) and factor Xa (FXa), public domain X-ray structures with bound inhibitors were collected. Crystallographic binding modes of inhibitors were systematically compared using a 3D similarity method taking conformational, positional, and atomic property differences into account. In addition, standard 2D similarity relationships were also determined. SAR information associated with individual compounds substantially changed when either bioactive conformations or 2D molecular graphs were used for similarity evaluation. 3D activity cliffs were identified for BACE1 and FXa inhibitor sets and systematically compared to 2D cliffs. It was found that less than 40% of 3D activity cliffs were conserved when 2D similarity was applied. The limited conservation of 3D and 2D cliffs provides further evidence for the strong molecule representation dependence of activity cliffs. Moreover, 3D cliffs represent a new class of activity cliffs that convey SAR information in ways that differ from graph-based similarity measures. In cases where sufficient structural information is available, the comparison of 3D and 2D cliffs is expected to aid in SAR analysis and mapping of critical binding determinants.     

Design of multitarget activity landscapes that capture hierarchical activity cliff distributions

An activity landscape model of a compound data set can be rationalized as a graphical representation that integrates molecular similarity and potency relationships. Activity landscape representations of different design are utilized to aid in the analysis of structure-activity relationships and the selection of informative compounds. Activity landscape models reported thus far focus on a single target (i.e., a single biological activity) or at most two targets, giving rise to selectivity landscapes. For compounds active against more than two targets, landscapes representing multitarget activities are difficult to conceptualize and have not yet been reported. Herein, we present a first activity landscape design that integrates compound potency relationships across multiple targets in a formally consistent manner. These multitarget activity landscapes are based on a general activity cliff classification scheme and are visualized in graph representations, where activity cliffs are represented as edges. Furthermore, the contributions of individual compounds to structure-activity relationship discontinuity across multiple targets are monitored. The methodology has been applied to derive multitarget activity landscapes for compound data sets active against different target families. The resulting landscapes identify single-, dual-, and triple-target activity cliffs and reveal the presence of hierarchical cliff distributions. From these multitarget activity landscapes, compounds forming complex activity cliffs can be readily selected.     

From activity cliffs to activity ridges: informative data structures for SAR analysis

The extraction of SAR information from structurally diverse compound data sets is a challenging task. One of the focal points of systematic SAR analysis is the search for activity cliffs, that is, structurally similar compounds having large potency differences, from which SAR determinants can be deduced. The assessment of SAR information is usually based on pairwise similarity and potency comparisons of data set compounds. As a consequence, activity cliffs are mostly evaluated at a compound pair level. Here, we present an extension of the activity cliff concept by introducing "activity ridges" that are formed by overlapping "combinatorial" activity cliffs between participating compounds, giving rise to ridge-like structures in activity landscapes. Activity ridges are rich in SAR information. In a systematic analysis of 242 compound data sets, we have identified well-defined activity ridges in 71 different sets. In addition, an information-theoretic approach has been devised to characterize the structural composition of activity ridges. Taken together, our results show that activity ridges frequently occur in sets of active compounds and that different categories of ridges can be distinguished on the basis of their structural content. The computational identification of activity ridges provides access to compound subsets having high priority for SAR analysis.     

Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure

Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.     

Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes

We systematically compare X-ray structures of inhibitor complexes of four well-known enzymes and correlate two- and three-dimensional (2D and 3D) similarity of inhibitors with their potency. The analysis reveals the presence of unexpected systematic relationships between molecular similarity and potency. These findings explain why apparently inconsistent structure-activity relationships (SARs) can coexist in different targets, and they have general implications for compound screening and optimization. The results suggest that (1) even for active sites with significant binding constraints, there is a high probability that structurally diverse ligands with similar activity can be identified, (2) different types of SARs are not mutually exclusive, and (3) the chemical nature of ligands is of comparable importance for SARs as the features of active sites. These insights aid in the understanding of target-specific SARs and their intrinsic degree of variability.     

Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account

Activity cliffs are formed by pairs or groups of structurally similar compounds with significant differences in potency. They represent a prominent feature of activity landscapes of compound data sets and a primary source of structure–activity relationship (SAR) information. Thus far, activity cliffs have only been considered for active compounds, consistent with the principles of the activity landscape concept. However, from an SAR perspective, pairs formed by structurally similar active and inactive compounds should often also be informative. Therefore, we have extended the activity cliff concept to also take inactive compounds into consideration. As source of both confirmed active and inactive compounds, we have exclusively focused on PubChem confirmatory bioassays. Activity cliffs formed between pairs of active compounds (homogeneous pairs) and pairs of active and inactive compounds (heterogeneous pairs) were systematically analyzed on a per-assay basis, hence ensuring the currently highest possible degree of experimental consistency in activity measurement. Only very small numbers of large-magnitude activity cliffs formed between active compounds were detected in PubChem bioassays. However, when taking confirmed inactive compounds from confirmatory assays into account, the activity cliff frequency in assay data significantly increased, involving 11–15 % of all qualifying pairs of similar compounds, depending on the molecular representations that were used. Hence, these non-conventional activity cliffs provide an additional source of SAR information.     

Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching

Shape similarity searching is a popular approach for ligand-based virtual screening on the basis of three-dimensional reference compounds. It is generally thought that well-defined experimentally determined binding modes of active reference compounds provide the best possible basis for shape searching. Herein, we show that experimental binding modes are not essential for successful shape similarity searching. Furthermore, we show that ensembles of analogs of X-ray ligands—in the absence of these ligands—further improve the search performance of single crystallographic reference compounds. This is even the case if ensembles of virtually generated analogs are used whose activity status is unknown. Taken together, the results of our study indicate that analog ensembles representing fuzzy reference states are effective starting points for shape similarity searching.     

AsteriX: a Web server to automatically extract ligand coordinates from figures in PDF articles

Coordinates describing the chemical structures of small molecules that are potential ligands for pharmaceutical targets are used at many stages of the drug design process. The coordinates of the vast majority of ligands can be obtained from either publicly accessible or commercial databases. However, interesting ligands sometimes are only available from the scientific literature, in which case their coordinates need to be reconstructed manually--a process that consists of a series of time-consuming steps. We present a Web server that helps reconstruct the three-dimensional (3D) coordinates of ligands for which a two-dimensional (2D) picture is available in a PDF file. The software, called AsteriX, analyses every picture contained in the PDF file and attempts to determine automatically whether or not it contains ligands. Areas in pictures that may contain molecular structures are processed to extract connectivity and atom type information that allow coordinates to be subsequently reconstructed. The AsteriX Web server was tested on a series of articles containing a large diversity in graphical representations. In total, 88% of 3249 ligand structures present in the test set were identified as chemical diagrams. Of these, about half were interpreted correctly as 3D structures, and a further one-third required only minor manual corrections. It is principally impossible to always correctly reconstruct 3D coordinates from pictures because there are many different protocols for drawing a 2D image of a ligand, but more importantly a wide variety of semantic annotations are possible. The AsteriX Web server therefore includes facilities that allow the users to augment partial or partially correct 3D reconstructions. All 3D reconstructions are submitted, checked, and corrected by the users domain at the server and are freely available for everybody. The coordinates of the reconstructed ligands are made available in a series of formats commonly used in drug design research. The AsteriX Web server is freely available at http://swift.cmbi.ru.nl/bitmapb/.     

Assay Related Target Similarity (ARTS) - chemogenomics approach for quantitative comparison of biological targets

Computer-based chemogenomics approaches compare macromolecular drug targets based on their amino acid sequences or derived properties, by similarity of their ligands, or according to ligand-target interaction models. Here we present ARTS (Assay Related Target Similarity) as a quantitative index that estimates target similarity directly from measured affinities of a set of probe compounds. This approach reduces the risk of deducing artificial target relationships from mutually inactive compounds. ARTS implements a scoring scheme that matches intertarget similarity based on dose-response measurements. While all experimentally derived target similarities have a tendency to be data set-dependent, we demonstrate that ARTS depends less on the used data set than the commonly used Pearson correlation or Tanimoto index.     

Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015

High affinity ligands for a given target tend to share key molecular interactions with important anchoring amino acids and therefore often present quite conserved interaction patterns. This simple concept was formalized in a topological knowledge-based scoring function (GRIM) for selecting the most appropriate docking poses from previously X-rayed interaction patterns. GRIM first converts protein–ligand atomic coordinates (docking poses) into a simple 3D graph describing the corresponding interaction pattern. In a second step, proposed graphs are compared to that found from template structures in the Protein Data Bank. Last, all docking poses are rescored according to an empirical score (GRIMscore) accounting for overlap of maximum common subgraphs. Taking the opportunity of the public D3R Grand Challenge 2015, GRIM was used to rescore docking poses for 36 ligands (6 HSP90α inhibitors, 30 MAP4K4 inhibitors) prior to the release of the corresponding protein–ligand X-ray structures. When applied to the HSP90α dataset, for which many protein–ligand X-ray structures are already available, GRIM provided very high quality solutions (mean rmsd = 1.06 Å, n = 6) as top-ranked poses, and significantly outperformed a state-of-the-art scoring function. In the case of MAP4K4 inhibitors, for which preexisting 3D knowledge is scarce and chemical diversity is much larger, the accuracy of GRIM poses decays (mean rmsd = 3.18 Å, n = 30) although GRIM still outperforms an energy-based scoring function. GRIM rescoring appears to be quite robust with comparison to the other approaches competing for the same challenge (42 submissions for the HSP90 dataset, 27 for the MAP4K4 dataset) as it ranked 3rd and 2nd respectively, for the two investigated datasets. The rescoring method is quite simple to implement, independent on a docking engine, and applicable to any target for which at least one holo X-ray structure is available.     

Large-scale similarity search profiling of ChEMBL compound data sets

A large-scale similarity search investigation has been carried out on 266 well-defined compound activity classes extracted from the ChEMBL database. The analysis was performed using two widely applied two-dimensional (2D) fingerprints that mark opposite ends of the current performance spectrum of these types of fingerprints, i.e., MACCS structural keys and the extended connectivity fingerprint with bond diameter four (ECFP4). For each fingerprint, three nearest neighbor search strategies were applied. On the basis of these search calculations, a similarity search profile of the ChEMBL database was generated. Overall, the fingerprint search campaign was surprisingly successful. In 203 of 266 test cases (～76%), a compound recovery rate of at least 50% was observed with at least the better performing fingerprint and one search strategy. The similarity search profile also revealed several general trends. For example, fingerprint searching was often characterized by an early enrichment of active compounds in database selection sets. In addition, compound activity classes have been categorized according to different similarity search performance levels, which helps to put the results of benchmark calculations into perspective. Therefore, a compendium of activity classes falling into different search performance categories is provided. On the basis of our large-scale investigation, the performance range of state-of-the-art 2D fingerprinting has been delineated for compound data sets directed against a wide spectrum of pharmaceutical targets.     

New insights from molecular dynamic simulation studies of the multiple binding modes of a ligand with G-quadruplex DNA

G-quadruplexes are higher-order DNA and RNA structures formed from guanine-rich sequences. These structures have recently emerged as a new class of potential molecular targets for anticancer drugs. An understanding of the three-dimensional interactions between small molecular ligands and their G-quadruplex targets in solution is crucial for rational drug design and the effective optimization of G-quadruplex ligands. Thus far, rational ligand design has been focused mainly on the G-quartet platform. It should be noted that small molecules can also bind to loop nucleotides, as observed in crystallography studies. Hence, it would be interesting to elucidate the mechanism underlying how ligands in distinct binding modes influence the flexibility of G-quadruplex. In the present study, based on a crystal structure analysis, the models of a tetra-substituted naphthalene diimide ligand bound to a telomeric G-quadruplex with different modes were built and simulated with a molecular dynamics simulation method. Based on a series of computational analyses, the structures, dynamics, and interactions of ligand-quadruplex complexes were studied. Our results suggest that the binding of the ligand to the loop is viable in aqueous solutions but dependent on the particular arrangement of the loop. The binding of the ligand to the loop enhances the flexibility of the G-quadruplex, while the binding of the ligand simultaneously to both the quartet and the loop diminishes its flexibility. These results add to our understanding of the effect of a ligand with different binding modes on G-quadruplex flexibility. Such an understanding will aid in the rational design of more selective and effective G-quadruplex binding ligands.     

Assembly of ligands interaction models for glutathione-S-transferases from Plasmodium falciparum,human and mouse using enzyme kinetics and molecular docking

BackgroundGlutathione-s-transferases (GSTs) are enzymes that principally catalyze the conjugation of electrophilic compounds to the endogenous nucleophilic glutathione substrate, besides, they have other non-catalytic functions. The Plasmodium falciparum genome encodes a single isoform of GST (PfGST) which is involved in buffering the toxic heme, thus considered a potential anti-malarial target. In mammals several classes of GSTs are available, each of various isoforms. The human (human GST Pi-1 or hGSTP1) and mouse (murine GST Mu-1 or mGSTM1) GST isoforms control cellular apoptosis by interaction with signaling proteins, thus considered as potential anti-cancer targets. In the course of GSTs inhibitors development, the models of ligands interactions with GSTs are used to guide rational molecular modification. In the absence of X-ray crystallographic data, enzyme kinetics and molecular docking experiments can aid in addressing ligands binding modes to the enzymes.MethodsKinetic studies were used to investigate the interactions between the three GSTs and each of glutathione, 1-chloro-2,4-dinitrobenzene, cibacron blue, ethacrynic acid, S-hexyl glutathione, hemin and protoporphyrin IX. Since hemin displacement is intended for PfGST inhibitors, the interactions between hemin and other ligands at PfGST binding sites were studied kinetically. Computationally determined binding modes and energies were interlinked with the kinetic results to resolve enzymes-ligands interaction models at atomic level.ResultsThe results showed that hemin and cibacron blue have different binding modes in the three GSTs. Hemin has two binding sites (A and B) with two binding modes at site-A depending on presence of GSH. None of the ligands were able to compete hemin binding to PfGST except ethacrynic acid. Besides bind differently in GSTs, the isolated anthraquinone moiety of cibacron blue is not maintaining sufficient interactions with GSTs to be used as a lead. Similarly, the ethacrynic acid uses water bridges to mediate interactions with GSTs and at least the conjugated form of EA is the true hemin inhibitor, thus EA may not be a suitable lead.ConclusionsGlutathione analogues with bulky substitution at thiol of cysteine moiety or at γ-amino group of γ-glutamine moiety may be the most suitable to provide GST inhibitors with hemin competition.     

Temperature dependence of the crystal lattice organization of coordination compounds involving nitronyl nitroxide radicals: a magnetic and structural investigation

Four new mononuclear complexes of formula Cd(PN)(4)(NCS)(2) (A), Cd(PNN)(4)(N(3))(2) (B), Zn(PNN)(4)(N(3))(2) (C), and Zn(PNN)(2)(NCS)(2) (D), where PNN stands for 2-(4-pyridyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide and PN for 2-(4-pyridyl)-4,4,5,5-tetramethylimidazoline-1-oxyl, were synthesized and structurally and magnetically characterized. The X-ray structures of compounds B and C were also determined at 90 K. Compounds A[bond]C crystallize in the triclinic space group P 1 macro (No. 2), and D crystallizes in the monoclinic space group P2(1)/m (No. 11). A[bond]C adopt a centrosymmetric distorted octahedral geometry in which the metal ions are bonded to four radical ligands through the nitrogen atom of the pyridyl rings and the azido or thiocyanato ligands occupy the apical positions. Compound D adopts a distorted tetrahedral geometry in which the zinc ion is bonded to two radicals and two thiocyanato ligands. As suggested by their magnetic behavior, the low-temperature X-ray structures of B and C show that these compounds undergo a clear structural change with respect to the room-temperature structures. The experimental magnetic behaviors were perfectly reproduced by a dimer model for A[bond]C and an alternating chain model for D while the sudden breaks observed in the chi(M)T versus T curves for B and C were well accounted for by the high- and low-temperature X-ray structures. For all these complexes the crystal structures favor significant overlap between molecular magnetic orbitals leading to rather strong intermolecular antiferromagnetic interactions.     

Similarity metrics for ligands reflecting the similarity of the target proteins

In this study we evaluate how far the scope of similarity searching can be extended to identify not only ligands binding to the same target as the reference ligand(s) but also ligands of other homologous targets without initially known ligands. This "homology-based similarity searching" requires molecular representations reflecting the ability of a molecule to interact with target proteins. The Similog keys, which are introduced here as a new molecular representation, were designed to fulfill such requirements. They are based only on the molecular constitution and are counts of atom triplets. Each triplet is characterized by the graph distances and the types of its atoms. The atom-typing scheme classifies each atom by its function as H-bond donor or acceptor and by its electronegativity and bulkiness. In this study the Similog keys are investigated in retrospective in silico screening experiments and compared with other conformation independent molecular representations. Studied were molecules of the MDDR database for which the activity data was augmented by standardized target classification information from public protein classification databases. The MDDR molecule set was split randomly into two halves. The first half formed the candidate set. Ligands of four targets (dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease, and progesterone receptor) were taken from the second half to form the respective reference sets. Different similarity calculation methods are used to rank the molecules of the candidate set by their similarity to each of the four reference sets. The accumulated counts of molecules binding to the reference target and groups of targets with decreasing homology to it were examined as a function of the similarity rank for each reference set and similarity method. In summary, similarity searching based on Unity 2D-fingerprints or Similog keys are found to be equally effective in the identification of molecules binding to the same target as the reference set. However, the application of the Similog keys is more effective in comparison with the other investigated methods in the identification of ligands binding to any target belonging to the same family as the reference target. We attribute this superiority to the fact that the Similog keys provide a generalization of the chemical elements and that the keys are counted instead of merely noting their presence or absence in a binary form. The second most effective molecular representation are the occurrence counts of the public ISIS key fragments, which like the Similog method, incorporates key counting as well as a generalization of the chemical elements. The results obtained suggest that ligands for a new target can be identified by the following three-step procedure: 1. Select at least one target with known ligands which is homologous to the new target. 2. Combine the known ligands of the selected target(s) to a reference set. 3. Search candidate ligands for the new targets by their similarity to the reference set using the Similog method. This clearly enlarges the scope of similarity searching from the classical application for a single target to the identification of candidate ligands for whole target families and is expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

A pose prediction approach based on ligand 3D shape similarity

Molecular docking predicts the best pose of a ligand in the target protein binding site by sampling and scoring numerous conformations and orientations of the ligand. Failures in pose prediction are often due to either insufficient sampling or scoring function errors. To improve the accuracy of pose prediction by tackling the sampling problem, we have developed a method of pose prediction using shape similarity. It first places a ligand conformation of the highest 3D shape similarity with known crystal structure ligands into protein binding site and then refines the pose by repacking the side-chains and performing energy minimization with a Monte Carlo algorithm. We have assessed our method utilizing CSARdock 2012 and 2014 benchmark exercise datasets consisting of co-crystal structures from eight proteins. Our results revealed that ligand 3D shape similarity could substitute conformational and orientational sampling if at least one suitable co-crystal structure is available. Our method identified poses within 2 Å RMSD as the top-ranking pose for 85.7 % of the test cases. The median RMSD for our pose prediction method was found to be 0.81 Å and was better than methods performing extensive conformational and orientational sampling within target protein binding sites. Furthermore, our method was better than similar methods utilizing ligand 3D shape similarity for pose prediction.     

Surflex-Dock: Docking benchmarks and real-world application

Benchmarks for molecular docking have historically focused on re-docking the cognate ligand of a well-determined protein-ligand complex to measure geometric pose prediction accuracy, and measurement of virtual screening performance has been focused on increasingly large and diverse sets of target protein structures, cognate ligands, and various types of decoy sets. Here, pose prediction is reported on the Astex Diverse set of 85 protein ligand complexes, and virtual screening performance is reported on the DUD set of 40 protein targets. In both cases, prepared structures of targets and ligands were provided by symposium organizers. The re-prepared data sets yielded results not significantly different than previous reports of Surflex-Dock on the two benchmarks. Minor changes to protein coordinates resulting from complex pre-optimization had large effects on observed performance, highlighting the limitations of cognate ligand re-docking for pose prediction assessment. Docking protocols developed for cross-docking, which address protein flexibility and produce discrete families of predicted poses, produced substantially better performance for pose prediction. Performance on virtual screening performance was shown to benefit by employing and combining multiple screening methods: docking, 2D molecular similarity, and 3D molecular similarity. In addition, use of multiple protein conformations significantly improved screening enrichment.     

An ontology for pharmaceutical ligands and its application for in silico screening and library design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.