Rapid Overlay Of Chemical Structures Research Articles

Discovery of Small-Molecule VapC1 Nuclease Inhibitors by Virtual Screening and Scaffold Hopping from an Atomic Structure Revealing Protein-Protein Interactions with a Native VapB1 Inhibitor.

Nontypeable Haemophilus influenzae (NTHi) are clinically important Gram-negative bacteria that are responsible for various human mucosal diseases, including otitis media (OM). Recurrent OM caused by NTHi is common, and infections that recur less than 2 weeks following antimicrobial therapy are largely attributable to the recurrence of the same strain of bacteria. Toxin-antitoxin (TA) modules encoded by bacteria enable rapid responses to environmental stresses and are thought to facilitate growth arrest, persistence, and tolerance to antibiotics. The vapBC-1 locus of NTHi encodes a type II TA system, comprising the ribonuclease toxin VapC1 and its cognate antitoxin VapB1. The activity of VapC1 has been linked to the survival of NTHi during antibiotic treatment both in vivo and ex vivo. Therefore, inhibitors of VapC1 might serve as adjuvants to antibiotics, preventing NTHi from entering growth arrest and surviving; however, none have been reported to date. A truncated VapB1 peptide from a crystal structure of the VapBC-1 complex was used to generate pharmacophore queries to facilitate a scaffold hopping approach for the identification of small-molecule VapC1 inhibitors. The National Center for Advancing Translational Sciences small-molecule library was virtually screened using the shape-based method rapid overlay of chemical structures (ROCS), and the top-ranking hits were docked into the VapB1 binding pocket of VapC1. Two hundred virtual screening hits with the best docking scores were selected and tested in a biochemical VapC1 activity assay, which confirmed eight compounds as VapC1 inhibitors. An additional 60 compounds were selected with structural similarities to the confirmed VapC1 inhibitors, of which 20 inhibited VapC1 activity. Intracellular target engagement of five inhibitors was indicated by the destabilization of VapC1 within bacterial cells from a cellular thermal shift assay; however, no impact on bacterial growth was observed. Thus, this virtual screening and scaffold hopping approach enabled the discovery of VapC1 ribonuclease inhibitors that might serve as starting points for preclinical development.

A Targeted Computational Screen of the SWEETLEAD Database Reveals FDA-Approved Compounds with Anti-Dengue Viral Activity.

Affordable and effective antiviral therapies are needed worldwide, especially against agents such as dengue virus that are endemic in underserved regions. Many antiviral compounds have been studied in cultured cells but are unsuitable for clinical applications due to pharmacokinetic profiles, side effects, or inconsistent efficacy across dengue serotypes. Such tool compounds can, however, aid in identifying clinically useful treatments. Here, computational screening (Rapid Overlay of Chemical Structures) was used to identify entries in an in silico database of safe-in-human compounds (SWEETLEAD) that display high chemical similarities to known inhibitors of dengue virus. Inhibitors of the dengue proteinase NS2B/3, the dengue capsid, and the host autophagy pathway were used as query compounds. Three FDA-approved compounds that resemble the tool molecules structurally, cause little toxicity, and display strong antiviral activity in cultured cells were selected for further analysis. Pyrimethamine (50% inhibitory concentration [IC50] = 1.2 μM), like the dengue proteinase inhibitor ARDP0006 to which it shows structural similarity, inhibited intramolecular NS2B/3 cleavage. Lack of toxicity early in infection allowed testing in mice, in which pyrimethamine also reduced viral loads. Niclosamide (IC50 = 0.28 μM), like dengue core inhibitor ST-148, affected structural components of the virion and inhibited early processes during infection. Vandetanib (IC50 = 1.6 μM), like cellular autophagy inhibitor spautin-1, blocked viral exit from cells and could be shown to extend survival in vivo Thus, three FDA-approved compounds with promising utility for repurposing to treat dengue virus infections and their potential mechanisms were identified using computational tools and minimal phenotypic screening.IMPORTANCE No antiviral therapeutics are currently available for dengue virus infections. By computationally overlaying the three-dimensional (3D) chemical structures of compounds known to inhibit dengue virus over those of compounds known to be safe in humans, we identified three FDA-approved compounds that are attractive candidates for repurposing as antivirals. We identified targets for two previously identified antiviral compounds and revealed a previously unknown potential anti-dengue compound, vandetanib. This computational approach to analyze a highly curated library of structures has the benefits of speed and cost efficiency. It also leverages mechanistic work with query compounds used in biomedical research to provide strong hypotheses for the antiviral mechanisms of the safer hit compounds. This workflow to identify compounds with known safety profiles can be expanded to any biological activity for which a small-molecule query compound has been identified, potentially expediting the translation of basic research to clinical interventions.

Identification of New Rofecoxib-Based Cyclooxygenase-2 Inhibitors: A Bioinformatics Approach.

The cyclooxygenase-2 receptor is a therapeutic target for planning potential drugs with anti-inflammatory activity. The selective cyclooxygenase-2 (COX-2) inhibitor rofecoxib was selected as a pivot molecule to perform virtual ligand-based screening from six commercial databases. We performed the search for similarly shaped Rapid Overlay of Chemical Structures (ROCS) and electrostatic (EON) compounds. After, we used pharmacokinetic and toxicological parameters to determine the best potential compounds, obtained through the softwares QikProp and Derek, respectively. Then, the compounds proceeded to the molecular anchorage study, which showed promising results of binding affinity with the hCOX-2 receptor: LMQC72 (∆G = −11.0 kcal/mol), LMQC36 (∆G = −10.6 kcal/mol), and LMQC50 (∆G = −10.2 kcal/mol). LMQC72 and LMQC36 showed higher binding affinity compared to rofecoxib (∆G = −10.4 kcal/mol). Finally, molecular dynamics (MD) simulations were used to evaluate the interaction of the compounds with the target hCOX-2 during 150 ns. In all MD simulation trajectories, the ligands remained interacting with the protein until the end of the simulation. The compounds were also complexing with hCOX-2 favorably. The compounds obtained the following affinity energy values: rofecoxib: ΔGbind = −45.31 kcal/mol; LMQC72: ΔGbind = −38.58 kcal/mol; LMQC36: ΔGbind = −36.10 kcal/mol; and LMQC50: ΔGbind = −39.40 kcal/mol. The selected LMQC72, LMQC50, and LMQC36 structures showed satisfactory pharmacokinetic results related to absorption and distribution. The toxicological predictions of these compounds did not display alerts for possible toxic groups and lower risk of cardiotoxicity compared to rofecoxib. Therefore, future in vitro and in vivo studies are needed to confirm the anti-inflammatory potential of the compounds selected here with bioinformatics approaches based on rofecoxib ligand.

Identification of Potential Inhibitors from Pyriproxyfen with Insecticidal Activity by Virtual Screening.

Aedes aegypti is the main vector of dengue fever transmission, yellow fever, Zika, and chikungunya in tropical and subtropical regions and it is considered to cause health risks to millions of people in the world. In this study, we search to obtain new molecules with insecticidal potential against Ae. aegypti via virtual screening. Pyriproxyfen was chosen as a template compound to search molecules in the database Zinc_Natural_Stock (ZNSt) with structural similarity using ROCS (rapid overlay of chemical structures) and EON (electrostatic similarity) software, and in the final search, the top 100 were selected. Subsequently, in silico pharmacokinetic and toxicological properties were determined resulting in a total of 14 molecules, and these were submitted to the PASS online server for the prediction of biological insecticide and acetylcholinesterase activities, and only two selected molecules followed for the molecular docking study to evaluate the binding free energy and interaction mode. After these procedures were performed, toxicity risk assessment such as LD50 values in mg/kg and toxicity class using the PROTOX online server, were undertaken. Molecule ZINC00001624 presented potential for inhibition for the acetylcholinesterase enzyme (insect and human) with a binding affinity value of −10.5 and −10.3 kcal/mol, respectively. The interaction with the juvenile hormone was −11.4 kcal/mol for the molecule ZINC00001021. Molecules ZINC00001021 and ZINC00001624 had excellent predictions in all the steps of the study and may be indicated as the most promising molecules resulting from the virtual screening of new insecticidal agents.

Using Chemoinformatics, Bioinformatics, and Bioassay to Predict and Explain the Antibacterial Activity of Nonantibiotic Food and Drug Administration Drugs.

Discovering of new and effective antibiotics is a major issue facing scientists today. Luckily, the development of computer science offers new methods to overcome this issue. In this study, a set of computer software was used to predict the antibacterial activity of nonantibiotic Food and Drug Administration (FDA)-approved drugs, and to explain their action by possible binding to well-known bacterial protein targets, along with testing their antibacterial activity against Gram-positive and Gram-negative bacteria. A three-dimensional virtual screening method that relies on chemical and shape similarity was applied using rapid overlay of chemical structures (ROCS) software to select candidate compounds from the FDA-approved drugs database that share similarity with 17 known antibiotics. Then, to check their antibacterial activity, disk diffusion test was applied on Staphylococcus aureus and Escherichia coli. Finally, a protein docking method was applied using HYBRID software to predict the binding of the active candidate to the target receptor of its similar antibiotic. Of the 1,991 drugs that were screened, 34 had been selected and among them 10 drugs showed antibacterial activity, whereby drotaverine and metoclopramide activities were without precedent reports. Furthermore, the docking process predicted that diclofenac, drotaverine, (S)-flurbiprofen, (S)-ibuprofen, and indomethacin could bind to the protein target of their similar antibiotics. Nevertheless, their antibacterial activities are weak compared with those of their similar antibiotics, which can be potentiated further by performing chemical modifications on their structure.

ROCS-derived features for virtual screening.

Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into color components and color atom overlaps, novel color similarity features that can be weighted in a system-specific manner by machine learning algorithms. In cross-validation experiments, these additional features significantly improve virtual screening performance relative to standard ROCS.

Identification and optimization of novel Hsp90 inhibitors with tetrahydropyrido[4,3-d]pyrimidines core through shape-based screening

Rapid Overlay of Chemical Structures (ROCS), which can rapidly identify potentially active compounds by shape comparison, is recognized as a powerful virtual screening tool. By ROCS, a class of novel Hsp90 inhibitors was identified. The calculated binding mode of the most potent hit 36 guided us to design and synthesize a series of analogs (57a–57h). Over 100-fold improvement was achieved in the target-based assay. The most potent compound 57h inhibited Hsp90 with IC50 0.10 ± 0.01 μM. It also showed much improved cell potency and ligand efficiency. Our study showed that ROCS is efficient in the identification of novel cores of Hsp90 inhibitors. 57h can be ideal leads for further optimization.

How Diverse Are Diversity Assessment Methods? A Comparative Analysis and Benchmarking of Molecular Descriptor Space

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.

Development of robust QSAR model using rapid overlay of crystal structures (ROCS) based alignment: a test case of Tubulin inhibitors

Conformational alignment is the most important step in 3D-QSAR methodology. There are various methods available to create an alignment hypothesis for the compounds being studied in 3D-QSAR. Most famous alignment methods include: alignment on a suitable template conformation, field fit alignment, database alignment, pharmacophore-based alignment, atom fit, and docking based alignment. All of the above-mentioned methods were explored previously to sample the conformational space in 3D-QSAR. Current study is dealing with the situation where most of the alignment methods were failed to produce a statistically robust 3D-QSAR model. Therefore, a relatively new alignment scheme was employed using ROCS (Rapid Overlay of Chemical Structures) method from OpenEyes. In the present study, Combretastatin A-4 based Tubulin inhibitors were used as case study. Co-crystal ligand conformation (i.e., Colchicine) in the Tubulin complex structure (PDB ID: 1SA0) was utilized as a template molecule. To date, very few applications of ROCS-based alignments in 3D-QSAR are reported in literature. Therefore, current study will serve as an addition to the existing status of using ROCS in 3D-QSAR. Genetic algorithm was utilized to build final 3D-QSAR model. Correlation obtained from final model suggesting that robust 3D-QSAR model was constructed.

MARS: Computing Three-Dimensional Alignments for Multiple Ligands Using Pairwise Similarities

The three-dimensional (3D) superimposition of molecules of one biological target reflecting their relative bioactive orientation is key for several ligand-based drug design studies (e.g., QSAR studies, pharmacophore modeling). However, with the lack of sufficient ligand-protein complex structures, an experimental alignment is difficult or often impossible to obtain. Several computational 3D alignment tools have been developed by academic or commercial groups to address this challenge. Here, we present a new approach, MARS (Multiple Alignments by ROCS-based Similarity), that is based on the pairwise alignment of all molecules within the data set using the tool ROCS (Rapid Overlay of Chemical Structures). Each pairwise alignment is scored, and the results are captured in a score matrix. The ideal superimposition of the compounds in the set is then identified by the analysis of the score matrix building stepwise a superimposition of all molecules. The algorithm exploits similarities among all molecules in the data set to compute an optimal 3D alignment. This alignment tool presented here can be used for several applications, including pharmacophore model generation, 3D QSAR modeling, 3D clustering, identification of structural outliers, and addition of compounds to an already existing alignment. Case studies are shown, validating the 3D alignments for six different data sets.

Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach

We report the first application of ligand-based virtual screening (VS) methods for discovering new compounds able to inhibit both human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT)-associated functions, DNA polymerase and ribonuclease H (RNase H) activities. The overall VS campaign consisted of two consecutive screening processes. In the first, the VS platform Rapid Overlay of Chemical Structures (ROCS) was used to perform in silico shape-based similarity screening on the NCI compounds database in which a hydrazone derivative, previously shown to inhibit the HIV-1 RT, was chosen. As a result, 34 hit molecules were selected and assayed on both RT-associated functions. In the second, the 4 most potent RT inhibitors identified were selected as queries for parallel VS performed by combining shape-based, 2D-fingerprint and 3D-pharmacophore VS methods. Overall, a set of molecules characterized by new different scaffolds were identified as novel inhibitors of both HIV-1 RT-associated activities in the low micromolar range.

Exploring Polypharmacology Using a ROCS-Based Target Fishing Approach

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

Computation of 3D queries for ROCS based virtual screens

Rapid overlay of chemical structures (ROCS) is a method that aligns molecules based on shape and/or chemical similarity. It is often used in 3D ligand-based virtual screening. Given a query consisting of a single conformation of an active molecule ROCS can generate highly enriched hit lists. Typically the chosen query conformation is a minimum energy structure. Can better enrichment be obtained using conformations other than the minimum energy structure? To answer this question a methodology has been developed called CORAL (COnformational analysis, Rocs ALignment). For a given set of molecule conformations it computes optimized conformations for ROCS screening. It does so by clustering all conformations of a chosen molecule set using pairwise ROCS combo scores. The best representative conformation is that which has the highest average overlap with the rest of the conformations in the cluster. It is these best representative conformations that are then used for virtual screening. CORAL was tested by performing virtual screening experiments with the 40 DUD (Directory of Useful Decoys) data sets. Both CORAL and minimum energy queries were used. The recognition capability of each query was quantified as the area under the ROC curve (AUC). Results show that the CORAL AUC values are on average larger than the minimum energy AUC values. This demonstrates that one can indeed obtain better ROCS enrichments with conformations other than the minimum energy structure. As a result, CORAL analysis can be a valuable first step in virtual screening workflows using ROCS.

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.

Hybrid Structure-Based Virtual Screening Protocol for the Identification of Novel BACE1 Inhibitors

BACE1, also called beta-secretase or memapsin 2, is an extensively studied aspartic protease, involved in etiopathogenesis and progression of Alzheimer's disease (AD). We report herein a modified structure-based virtual screening protocol that augments the lead identification process against BACE1 during virtual screening endeavors. A hybrid structure-based virtual screening protocol that incorporates elements from both ligand-based and structure-based techniques was used for the identification of prospective small molecule inhibitors. Virtual screening, using an active-site-derived pharmacophore, followed by ROCS (rapid overlay of chemical structures)-based GOLD (genetic optimization in ligand docking) docking was used to identify a library of focused candidates. The efficacy of the ROCS-based GOLD docking method together with our customized weighted consensus scoring function was evaluated against conventional docking methods for its ability to discern true positives from a screening library. An in-depth structural analysis of the binding mode of the top-ranking molecules reveals that emulation of the curial interaction patterns deemed necessary for BACE1 inhibition. The results obtained from our validation study ensure the superiority of our docking methodology over conventional docking methods in yielding higher enrichment rates.

Application of Belief Theory to Similarity Data Fusion for Use in Analog Searching and Lead Hopping

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.

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.

2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L

Hologram quantitative structure–activity relationships (HQSAR) were applied to a data set of 41 cruzain inhibitors. The best HQSAR model ( Q 2 = 0.77; R 2 = 0.90) employing Surflex-Sim, as training and test sets generator, was obtained using atoms, bonds, and connections as fragment distinctions and 4–7 as fragment size. This model was then used to predict the potencies of 12 test set compounds, giving satisfactory predictive R 2 value of 0.88. The contribution maps obtained from the best HQSAR model are in agreement with the biological activities of the study compounds. The Trypanosoma cruzi cruzain shares high similarity with the mammalian homolog cathepsin L. The selectivity toward cruzain was checked by a database of 123 compounds, which corresponds to the 41 cruzain inhibitors used in the HQSAR model development plus 82 cathepsin L inhibitors. We screened these compounds by ROCS (Rapid Overlay of Chemical Structures), a Gaussian-shape volume overlap filter that can rapidly identify shapes that match the query molecule. Remarkably, ROCS was able to rank the first 37 hits as being only cruzain inhibitors. In addition, the area under the curve (AUC) obtained with ROCS was 0.96, indicating that the method was very efficient to distinguishing between cruzain and cathepsin L inhibitors.

A Shape-Based 3-D Scaffold Hopping Method and Its Application to a Bacterial Protein−Protein Interaction

In this paper, we describe the first prospective application of the shape-comparison program ROCS (Rapid Overlay of Chemical Structures) to find new scaffolds for small molecule inhibitors of the ZipA-FtsZ protein-protein interaction, a proposed antibacterial target. The shape comparisons are made relative to the crystallographically determined, bioactive conformation of a high-throughput screening (HTS) hit. The use of ROCS led to the identification of a set of novel, weakly binding inhibitors with scaffolds presenting synthetic opportunities to further optimize biological affinity and lacking development issues associated with the HTS lead. These ROCS-identified scaffolds would have been missed using other structural similarity approaches such as ISIS 2D fingerprints. X-ray crystallographic analysis of one of the new inhibitors bound to ZipA reveals that the shape comparison approach very accurately predicted the binding mode. These experimental results validate this use of ROCS for chemotype switching or "lead hopping" and suggest that it is of general interest for lead identification in drug discovery endeavors.

Assessing the performance of OMEGA with respect to retrieving bioactive conformations

OMEGA is a rule-based program which rapidly generates conformational ensembles of small molecules. We have varied the parameters which control the nature of the ensembles generated by OMEGA in a statistical fashion (D-optimal) with the aim of increasing the probability of generating bioactive conformations. Thirty-six drug-like ligands from different ligand–protein complexes determined by high-resolution (≤2.0 Å) X-ray crystallography have been analyzed. Statistically significant models ( Q 2≥0.75) confirm that one can increase the performance of OMEGA by modifying the parameters. Twenty-eight of the bioactive conformations were retrieved when using a low-energy cut-off (5 kcal/mol), a low RMSD value (0.6 Å) for duplicate removal, and a maximum of 1000 output conformations. All of those that were not retrieved had eight or more rotatable bonds. The duplicate removal parameter was found to have the largest impact on retrieval of bioactive conformations, and the maximum number of conformations also affected the results considerably. The input conformation was found to influence the results largely because certain bond angles can prevent the bioactive conformation from being generated as a low-energy conformation. Pre-optimizing the input structures with MMFF94s improved the results significantly. We also investigated the performance of OMEGA in connection with database searching. The shape-matching program Rapid Overlay of Chemical Structures (ROCS) was used as search tool. Two multi-conformational databases were built from the MDDR database plus the 36 compounds; one large (maximum 1000 conformations/mol) and one small (maximum 100 conformations/mol). Both databases provided satisfactory results in terms of retrieval. ROCS was able to rank 35 out of 36 X-ray structures among the top 500 hits from the large database.