Virtual Combinatorial Library Research Articles

Structure-based rational design of peptide hydroxamic acid inhibitors to target tumor necrosis factor-α converting enzyme as potential therapeutics for hepatitis

The human tumor necrosis factor-α converting enzyme (TACE) has recently been raised as a new and promising therapeutic target of hepatitis and other inflammatory diseases. Here, we reported a successful application of the solved crystal structure of TACE complex with a peptide-like ligand INN for rational design of novel peptide hydroxamic acid inhibitors with high potency and selectivity to target and inhibit TACE. First, the intermolecular interactions between TACE catalytic domain and INN were characterized through an integrated bioinformatics approach, with which the key substructures of INN that dominate ligand binding were identified. Subsequently, the INN molecular structure was simplified to a chemical sketch of peptide hydroxamic acid compound, which can be regarded as a linear tripeptide capped by a N-terminal carboxybenzyl group (chemically protective group) and a C-terminal hydroxamate moiety (coordinated to the Zn2+ at TACE active site). Based on the sketch, a virtual combinatorial library containing 180 peptide hydroxamic acids was generated, from which seven samples were identified as promising candidates by using a knowledge-based protein–peptide affinity predictor and were then tested in vitro with a standard TACE activity assay protocol. Consequently, three designed peptide hydroxamic acids, i.e. Cbz-Pro-Ile-Gln-hydroxamic acid, Cbz-Leu-Ile-Val-hydroxamic acid and Cbz-Phe-Val-Met-hydroxamic acid, exhibited moderate or high inhibitory activity against TACE, with inhibition constants Ki of 36 ± 5, 510 ± 46 and 320 ± 26 nM, respectively. We also examined the structural basis and non-bonded profile of TACE interaction with a designed peptide hydroxamic acid inhibitor, and found that the inhibitor ligand is tightly buried in the active pocket of TACE, forming a number of hydrogen bonds, hydrophobic forces and van der Waals contacts at the interaction interface, conferring both stability and specificity for TACE–inhibitor complex architecture.

Chemically Advanced Template Search (CATS) for Scaffold‐Hopping and Prospective Target Prediction for ‘Orphan’ Molecules

Drug discovery is driven by the identification of new chemical entities (NCEs).1,2 Virtual screening and de novo design techniques have been proven to serve this purpose, thereby complementing experimental biochemical and biological approaches.3 Still, it remains a matter of debate, which particular molecular representation and similarity index are preferable for a given drug target in order to identify appropriate NCEs with minimal synthetic and testing effort involved.4 Ligand-based chemical similarity approaches have been effectively applied to large-scale activity and target prediction for known drugs, some of the prominent methods being PASS developed by Poroikov et al.,5 the techniques conceived by Mestres and co-workers,6 and the similarity ensemble approach (SEA) implemented by the Shoichet group.7 Here, we compared several popular two-dimensional molecular representations for their ability to retrieve actives (enrichment potential) and chemotypes (scaffold-hopping potential) from a collection of druglike bioactive compounds. Subsequently the applied chemical advanced template search (CATS)8 was applied to predicting potential drug targets for a virtually assembled combinatorial compound library, from which we synthesized and successfully tested candidate compounds. The results demonstrate that CATS is not only suited for its intended purpose of NCE retrieval by scaffold-hopping,9 but also for reliable target profiling of ‘orphan’ virtual molecules.10 It thereby complements the suite of available validated tools for target prediction.

Identification of New Potent GPR119 Agonists by Combining Virtual Screening and Combinatorial Chemistry

Virtual screening in a huge collection of virtual combinatorial libraries has led to the identification of two new structural classes of GPR119 agonists with submicromolar in vitro potencies. Herein, we describe the virtual screening process involving feature trees fragment space searches followed by a 3D postprocessing step. The in silico findings were then filtered and prioritized, and finally, combinatorial libraries of target molecules were synthesized. Furthermore the so-called "activity-anchor principle" is introduced as an element to increase the chance to generate true hits. An activity anchor is a structural element expected to provide key contributions to a certain biological activity. Application of this technique has led to the discovery of two new GPR119-agonist hit series, one of which was further optimized to progress as a novel lead class.

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0: a quantitative molecular docking study

BackgroundHLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 – 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pKa of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0.ResultsThe X-ray structure of the peptide – HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides.ConclusionsThe higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His79β. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins.

The multiobjective based design, synthesis and evaluation of the arylsulfonamide/amide derivatives of aryloxyethyl- and arylthioethyl- piperidines and pyrrolidines as a novel class of potent 5-HT7 receptor antagonists

An analysis of the virtual combinatorial library was used for refining a pilot set of 34 derivatives and designing a targeted 38-member library of the arylamide and arylsulfonamide derivatives of aryloxyethyl- and arylthioethyl- piperidines and pyrrolidines. All compounds 24–95 were synthesized according to an elaborated parallel solid-phase method and were biologically evaluated for their affinity for 5-HT7R. Additionally, the targeted library members were tested for 5-HT1A, 5-HT6, and D2 receptors. Selected compounds of particular interest were examined for their intrinsic activity at 5-HT7R in vitro employing a cAMP assay. The study allowed us to identify compound 68 (4-fluoro-N-(1-{2-[(propan-2-yl)phenoxy]ethyl}piperidin-4-yl) benzenesulfonamide) as a potent 5-HT7R ligand (Ki = 0.3 nM) with strong antagonistic properties (Kb = 1 nM) and a 1450-fold selectivity over 5-HT1ARs.

Synthesis and in silico screening of a library of β-carboline-containing compounds.

The synthesis of a library of tetrahydro-β-carboline-containing compounds in milligram quantities is described. Among the unique heterocyclic frameworks are twelve tetrahydroindolizinoindoles, six tetrahydrocyclobutanindoloquinolizinones and three tetrahydrocyclopentenoneindolizinoindolones. These compounds were selected from a virtual combinatorial library of 11,478 compounds. Physical chemical properties were calculated and most of them are in accordance with Lipinski’s rules. Virtual docking and ligand-based target evaluations were performed for the β-carboline library compounds and selected synthetic intermediates to assess the therapeutic potential of these small organic molecules. These compounds have been deposited into the NIH Molecular Repository (MLSMR) and may target proteins such as histone deacetylase 4, endothelial nitric oxide synthase, 5-hydroxytryptamine receptor 6 and mitogen-activated protein kinase 1. These in silico screening results aim to add value to the β-carboline library of compounds for those interested in probes of these targets.

CCLab—a multi-objective genetic algorithm based combinatorial library design software and an application for histone deacetylase inhibitor design

The introduction of the multi-objective optimization has dramatically changed the virtual combinatorial library design, which can consider many objectives simultaneously, such as synthesis cost and drug-likeness, thus may increase positive rates of biological active compounds. Here we described a software called CCLab (Combinatorial Chemistry Laboratory) for combinatorial library design based on the multi-objective genetic algorithm. Tests of the convergence ability and the ratio to re-take the building blocks in the reference library were conducted to assess the software in silico, and then it was applied to a real case of designing a 5×6 HDAC inhibitor library. Sixteen compounds in the resulted library were synthesized, and the histone deactetylase (HDAC) enzymatic assays proved that 14 compounds showed inhibitory ratios more than 50% against tested 3 HDAC enzymes at concentration of 20μg/mL, with IC50 values of 3 compounds comparable to SAHA. These results demonstrated that the CCLab software could enhance the hit rates of the designed library and would be beneficial for medicinal chemists to design focused library in drug development (the software can be downloaded at: http://202.127.30.184:8080/drugdesign.html).

Diversity, isomer composition, and design of combinatorial libraries of tetrapyrrole macrocycles

Combinatorial libraries of substituted tetrapyrrole macrocycles, which can now be prepared via a variety of approaches, typically are rich in isomers. Terminology for describing such isomers (due to distinct patterns of peripheral substituents) is delineated in several illustrative examples. A hierarchical relationship exists of molecular formula, condensed formula(s) of substituents, set(s) of pyrrole collocates (conveying each pair of β-pyrrolic substituents), and isomers of substituted tetrapyrrole macrocycles. Isomers with identical pyrrole collocate sets can arise owing to distinct positions or orientations of the (homo- or hetero-substituted) pyrrolic units in a macrocycle. Consideration of a handful of virtual combinatorial libraries illustrates tradeoffs of library size, chemical richness, and isomeric content. As one example, octa-derivatization of a tetrapyrrole scaffold with eight reactants A–H affords 2,099,728 members (99.7% isomers, 82,251 pyrrole collocate sets, and 6,435 condensed formulas) whereas the reversible self-condensation of four pyrroles that bear the same eight entities (AB, CD, EF, GH) affords 538 members (93.5% isomers, 35 pyrrole collocate sets, and 35 condensed formulas). Derivatization affords all combinations and permutations whereas self-condensation of substituted pyrroles carries collocational restrictions. Understanding such tradeoffs and the structural origin of isomerism are important aspects in the design of tetrapyrrole combinatorial libraries.

Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

An integrated computational workflow for efficient and quantitative modeling of renin inhibitors

A new integrated computational workflow that couples the strength of the molecular overlay methods to achieve rapid and automated alignments along with 3D-QSAR techniques like CoMFA® and CoMSIA for quantitative binding affinity prediction is presented. The results obtained from such techniques are compared with rule-based Topomer CoMFA® method, where possible. The developed 3D-QSAR models were prospectively used to predict the affinities of new compounds designed through R-group deconvolution starting from the core chemical scaffold and subsequent virtual combinatorial library enumeration. The general applicability of the seamless in silico modeling workflow is demonstrated using several datasets reported for small molecule inhibitors of renin.

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

Compound Acquisition and Prioritization Algorithm for Constructing Structurally Diverse Compound Libraries

In the present study, we report a compound acquisition and prioritization algorithm established for rational chemical library purchasing or compound synthesis to increase the diversity of an existing compound collection. This method was established based on a chemistry-space calculation using BCUT (Burden CAS University of Texas) descriptors. To identify the acquisition of compounds from candidate collections into the existing collection, a derived distance-based selection rule was applied, and the results were well supported by pairwise similarity calculations and cell-partition statistics in chemistry space. The correlation between chemistry-space distance and Tanimoto similarity index was also studied to justify the compound acquisition strategy through weighted linear regression. As a rational approach for library design, the distance-based selection rule exhibits certain advantages in prioritizing compound selection to enhance the overall structural diversity of an existing in-house compound collection or virtual combinatorial library for in silico screening, diversity oriented synthesis, and high-throughput screening.

In silico design of potent EGFR kinase inhibitors using combinatorial libraries

This paper is an attempt to design 4-anilinoquinazoline compounds having promising anticancer activities against epidermal growth factor (EGFR) kinase inhibition, using virtual combinatorial library approach. Partial least squares method has been applied for the development of a quantitative structure–activity relationship (QSAR) model based on training and test set approaches. The partial least squares model showed some interesting results in terms of internal and external predictability against EGFR kinase inhibition for such type of anilinoquinazoline derivatives. In virtual screening study, out of 4860 compounds in chemical library, 158 compounds were screened and finally, 10 compounds were selected as promising EGFR kinase inhibitors based on their predicted activities from the QSAR model. These derivatives were subjected to molecular docking study to investigate the mode of binding with the EGFR kinase, and the two compounds (ID 3639 and 3399) showing similar type of docking score and binding patterns with that of the existing drug molecules like erlotinib were finally reported.

Organic Virtual Library (ORVIL) - A Combinatorial Library Construction based on Organic Constituents and without Scaffold Hopping

Rapid construction of virtual combinatorial library is a prerequisite for in silico library enumeration and design. Organic Virtual Library (ORVIL) is a perl programme to generate a combinatorial library using most frequently observed 200 organic substituents. It is designed to explore the organic chemical space in the given query structure without affecting the entire backbone of the molecule enabling minimum molecular complexities. Its particular features are its simplicity to use, portable SMILES format and high speed of library construction. Benchmarking of Tamoxifen (drug for breast cancer) was performed which revealed a compound having similar architecture of known drug analogue, Toremifene.

Studies on two types of PTP1B inhibitors for the treatment of type 2 diabetes: Hologram QSAR for OBA and BBB analogues

Hologram quantitative structure–activity relationships (HQSAR) analysis were conducted on two series of PTP1B inhibitors, 39 2-(oxalylamino) benzoic acid (OBA) analogues and 60 benzofuran and benzothiophene biphenyls (BBB) analogues. The optimal HQSAR model of the OBA analogue has q2=0.592 and r2=0.940, while the optimal HQSAR model for the BBB analogues shows q2=0.667 and r2=0.863. Two models were employed to predict the biological activities of two test sets. For OBA analogues, the optimal model was validated by an external test set of six compounds with satisfactory predictive r2 value of 0.786. For BBB analogues, the optimal model shows satisfactory predictive r2 value of 0.866 for an external test set of 10 compounds. The contribution maps derived from the optimal HQSAR models are consistent with the biological activities of the studied compounds. Two virtual combinatorial libraries were designed and screened by the optimal HQSAR models and potential candidates with high predictive biological activities were discovered. This work may provide valuable information for future design of more promising inhibitors for PTP1B.

Editorial: Crystal‐Clear View of Chemistry

Editorial: Crystal‐Clear View of Chemistry

A virtual library of constrained cyclic tetrapeptides that mimics all four side-chain orientations for over half the reverse turns in the protein data bank

Reverse turns are often recognition sites for protein/protein interactions and, therefore, valuable potential targets for determining recognition motifs in development of potential therapeutics. A virtual combinatorial library of cyclic tetrapeptides (CTPs) was generated and the bonds in the low-energy structures were overlapped with canonical reverse-turn Calpha-Cbeta bonds (Tran et al., J Comput Aided Mol Des 19(8):551-566, 2005) to determine the utility of CTPs as reverse-turn peptidomimetics. All reverse turns in the Protein Data Bank (PDB) with a crystal structures resolution < or = 3.0 A were classified into the same known canonical reverse-turn Calpha-Cbeta bond clusters (Tran et al., J Comput Aided Mol Des 19(8):551-566, 2005). CTP reverse-turn mimics were compiled that mimicked both the relative orientations of three of the four as well as all four Calpha-Cbeta bonds in the reverse turns of the PDB. 54% of reverse turns represented in the PDB had eight or more CTPs structures that mimicked the orientation of all four of the Calpha-Cbeta bonds in the reverse turn.

ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors

In this paper we illustrate the application of the ISIDA (In SIlico design and Data Analysis) software to perform virtual screening of large databases of compounds and reactions and to assess some ADME/Tox properties. ISIDA represents an ensemble of tools allowing users to store, search and analyze the data, to perform similarity searches in large databases of molecules and reactions, to build and validate QSAR models, and to generate and screen virtual combinatorial libraries. It uses its own descriptors (substructural molecular fragments and fuzzy pharmacophore triplets). Workflow can be easily organized by combining different ISIDA modules. Several examples of ISIDA applications (similarity search of potent benzodiazepine ligands with FPT, QSAR modeling of aqueous solubility, aquatic toxicity, tissue-air partition coefficients, anti-HIV activity, and screening of the “Chimiotheque Nationale” Database), are discussed. Particular attention is paid to mining reaction databases using Condensed Reaction Graphs approach.

Prediction of biological response for large combinatorial libraries of biodegradable polymers: Polymethacrylates as a test case

A large virtual combinatorial library of polymethacrylates was, for the first time, designed for computer-aided prediction of biorelevant and material properties and focused polymer synthesis. The distinguishing features of this virtual library include its size (about 40 000 compounds), its explicit representation of relatively long polymer chains, and its accounting for different compositions in the case of copolymers and terpolymers. A subset of 79 polymers taken from a representative sub-library of 2000 polymethacrylates was employed to build initial QSPR-based polynomial neural network models, which were then deployed to predict cell attachment, cell growth, and fibrinogen adsorption on polymer surfaces for these 2000 polymethacrylates. The agreement between predicted and experimentally measured property values for the 50 polymethacrylate copolymers within this virtual polymer space encourages further pursuit of polymethacrylate-based biomaterials, and justifies more extensive deployment of computational models derived from larger experimental data sets for the rational design of biorelevant polymers endowed with targeted performance properties.

Data Mining a Small Molecule Drug Screening Representative Subset from NIH PubChem

PubChem is a scientific showcase of the NIH Roadmap Initiatives. It is a compound repository created to facilitate information exchange and data sharing among the NIH Roadmap-funded Molecular Library Screening Center Network (MLSCN) and the scientific community. However, PubChem has more than 10 million records of compound information. It will be challenging to conduct a drug screening of the whole database of millions of compounds. Thus, the purpose of the present study was to develop a data mining cheminformatics approach in order to construct a representative and structure-diverse sublibrary from the large PubChem database. In this study, a new chemical diverse representative subset, rePubChem, was selected by whole-molecule chemistry-space matrix calculation using the cell-based partition algorithm. The representative subset was generated and was then subjected to evaluations by compound property analyses based on 1D and 2D molecular descriptors. The new subset was also examined and assessed for self-similarity analysis based on 2D molecular fingerprints in comparing with the source compound library. The new subset has a much smaller library size (540K compounds) with minimum similarity and redundancy without loss of the structural diversity and basic molecular properties of its parent library (5.3 million compounds). The new representative subset library generated could be a valuable structure-diverse compound resource for in silico virtual screening and in vitro HTS drug screening. In addition, the established subset generation method of using the combined cell-based chemistry-space partition metrics with pairwised 2D fingerprint-based similarity search approaches will also be important to a broad scientific community interested in acquiring structurally diverse compounds for efficient drug screening, building representative virtual combinatorial chemistry libraries for syntheses, and data mining large compound databases like the PubChem library in general.