MED-SuMoLig: a new ligand-based screening tool for efficient scaffold hopping

The identification of small molecules with selective bioactivity, whether intended as potential therapeutics or as tools for experimental research, is central to progress in medicine and in the life sciences. To facilitate such study, we have developed a ligand-based program well-suited for effective screening of large compound collections. This package, MED-SuMoLig, combines a SMARTS-driven substructure search aiming at 3D pharmacophore profiling and computation of the local atomic density of the compared molecules. The screening utility was then investigated using 52 diverse active molecules (against CDK2, Factor Xa, HIV-1 protease, neuraminidase, ribonuclease A, and thymidine kinase) merged to a library of about 40,000 putative inactive (druglike) compounds. In all cases, the program recovered more than half of the actives in the top 3% of the screened library. We also compared the performance of MED-SuMoLig with that of ChemMine or of ROCS and found that MED-SuMoLig outperformed both methods for CDK2 and Factor Xa in terms of enrichment rates or performed equally well for the other targets.     

Identification of chiral selectors for improved enantioseparation based on molecular interaction fields

Chiral sulfoxide drugs such as omeprazole, lansoprazole and pantoprazole were chromatographed on three chiral stationary phases (CSP), using amylose tris-(phenylcarbamate) derivatives in the reversed-phase mode. The retention factors (k) and chromatographic partition coefficients (k_w), obtained by extrapolation of the first according to the linear Snyder equation, were analyzed employing molecular interaction fields (MIF) of eluted analytes. Based on the generated MIF, chiral selectors could be identified for improving enantiomeric separation performance of the respective sulfoxides. The method is useful for predicting the complementarities between CSP and analytes, and thus to help the selection of appropriate stationary phases prior to their preparation.     

Systematic assessment of scaffold distances in ChEMBL: prioritization of compound data sets for scaffold hopping analysis in virtual screening

The evaluation of the scaffold hopping potential of computational methods is of high relevance for virtual screening. For benchmark calculations, classes of known active compounds are utilized. Ideally, such classes should have a well-defined content of structurally diverse scaffolds. However, in reported benchmark investigations, the choice of activity classes is often difficult to rationalize. To provide a compendium of well-characterized test cases for the assessment of scaffold hopping potential, structural distances between scaffolds were systematically calculated for compound classes available in the ChEMBL database. Nearly seven million scaffold pairs were evaluated. On the basis of the global scaffold distance distribution, a threshold value for large scaffold distances was determined. Compound data sets were ranked based on the proportion of scaffold pairs with large distances they contained, taking additional criteria into account that are relevant for virtual screening. A set of 50 activity classes is provided that represent attractive test cases for scaffold hopping analysis and benchmark calculations.     

Optimizing fragment and scaffold docking by use of molecular interaction fingerprints

Protein-ligand interaction fingerprints have been used to postprocess docking poses of three ligand data sets: a set of 40 low-molecular-weight compounds from the Protein Data Bank, a collection of 40 scaffolds from pharmaceutically relevant protein ligands, and a database of 19 scaffolds extracted from true cdk2 inhibitors seeded in 2230 scaffold decoys. Four popular docking tools (FlexX, Glide, Gold, and Surflex) were used to generate poses for ligands of the three data sets. In all cases, scoring by the similarity of interaction fingerprints to a given reference was statistically superior to conventional scoring functions in posing low-molecular-weight fragments, predicting protein-bound scaffold coordinates according to the known binding mode of related ligands, and screening a scaffold library to enrich a hit list in true cdk2-targeted scaffolds.     

Predicting drug pharmacokinetic properties using molecular interaction fields and SIMCA

We have developed a method that combines molecular interaction fields with soft independent modeling of class analogy (SIMCA) Wold:1977 to predict pharmacokinetic drug properties. Several additional considerations to those made in traditional QSAR are required in order to develop a successful QSPR strategy that is capable of accommodating the many complex factors that contribute to key pharmacokinetic properties such as ADME (absorption, distribution, metabolism, and excretion) and toxicology. An accurate prediction of oral bioavailability, for example, requires that absorption and first-pass hepatic elimination both be taken into consideration. To accomplish this, general properties of molecules must be related to their solubility and ability to penetrate biological membranes, and specific features must be related to their particular metabolic and toxicological profiles. Here we describe a method, which is applicable to structurally diverse data sets while utilizing as much detailed structural information as possible. We address the issue of the molecular alignment of a structurally diverse set of compounds using idiotropic field orientation (IFO), a generalization of inertial field orientation Clark:1998. We have developed a second flavor of this method, which directly incorporates electrostatics into the molecular alignment. Both variations of IFO produce a characteristic orientation for each structure and the corresponding molecular fields can then be analyzed using SIMCA. Models are presented for human intestinal absorption, blood-brain barrier penetration and bioavailability to demonstrate ways in which this tool can be used early in the drug development process to identify leads likely to exhibit poor pharmacokinetic behavior in pre-clinical studies, and we have explored the influence of conformation and molecular field type on the statistical properties of the models obtained.     

Design and synthesis of screening libraries based on the muurolane natural product scaffold

The plant-derived natural product 14-hydroxy-6,12-muuroloadien-15-oic acid (1) was identified as a unique scaffold that could be chemically elaborated to generate novel lead- or drug-like screening libraries. Prior to synthesis a virtual library was generated and prioritised based on drug-like physicochemical parameters such as log P, log D(5.5), hydrogen bond donors/acceptors, and molecular weight. The natural product scaffold (1) was isolated from the endemic Australian plant Eremophila mitchellii and then utilised in the parallel solution-phase generation of two series of analogues. The first library consisted of six semi-synthetic amide derivatives, whilst the second contained six carbamate analogues. These libraries have been evaluated for antimalarial activity using a chloroquine-sensitive Plasmodium falciparum line (3D7) and several compounds displayed low to moderate activity with IC(50) values ranging from 14 to 33 μM.     

The role of molecular interaction fields on enantioselective and nonselective separation of chiral sulfoxides

The separation of a series of 23 asymmetric sulfoxides, including the three proton pump inhibitors (PPI) omeprazole, lansoprazole and pantoprazole was investigated by HPLC, under reversed-phase elution with amylose tris(3,5-dimethylphenylcarbamate), amylose tris[(S)-1-phenylethylcarbamate] and amylose tris(3,5-dimethoxyphenylcarbamate) chiral stationary phases, CSP1-3, respectively. The whole set of sulfoxides showed better enantioselectivity and enantioresolution on CSP1. However, the three PPI were enantioseparated only when using CSP1 and CSP3. It was observed an improved enantioselectivity and enantioresolution on CSP3. The mechanisms of retention were evaluated by molecular interaction fields (MIF) generated via GRID force field, which yielded the geometric reasons leading to the scenario outlined. The enantioselective and nonselective interactions are discussed in terms of the reported selectivity. The steric structural outline of the CSP nonselective interaction sites is of major importance to deliver the sulfoxides to the chiral selective sites where the enantioselective interactions take place.     

Forging of nicotine for the effective management of diabetic wounds: A hybrid of scaffold hopping and molecular dynamics simulation approaches

Diabetic wound (DW) is a huge threat to the health care community and is always challenging to treat. The main biochemical culprits in DW recalcitrance are elevated inflammatory mediators, proteases, cell proliferation and migration suppressors, anti-angiogenic factors, and bacterial infections. In this scenario, using a scaffold to target important factors at each stage of pathogenesis can accelerate the healing process. Many shreds of evidence disclosed the role of nicotine scaffold in handling inflammation, infection, proliferation, migration, and angiogenesis. All these factors made us forge nicotine by employing a scaffold hopping approach. The hops were then subjected to molecular docking and binding free energy calculations against Matrix metallopeptidase 9, Glycogen synthase kinase 3 beta, Tumor necrosis factor alpha, MurC and ParE enzymes. Gratifyingly, molecule H1 was found to possess significant inhibitory activity against the selected receptors as evidenced by their high negative glide score and binding energy. Furthermore, 100 ns of molecular dynamics simulation studies (MD) was performed for the five H1/4XCT, H1/5F95, H1/2AZ5, H1/4C13 and H1/4MOT complexes to get insight into the binding modes and stability. The MD results showed significant stability as evidenced by the low conformational changes of the H1 with the chosen receptors. Hence, H1 might be a druggable candidate in the therapeutic management of DW. However, further research is strongly recommended to advance the drug into the therapeutic pipeline.     

Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points

Pharmacophore modeling can provide valuable insight into ligand-receptor interactions. It can also be used in 3D (dimensional) database searching for potentially finding biologically active compounds and providing new research ideas and directions for drug-discovery projects. To stimulate the structure-based drug design against SARS (severe acute respiratory syndrome), a pharmacophore search was conducted over 3.6 millions of compounds based on the atomic coordinates of the complex obtained by docking KZ7088 (a derivative of AG7088) to SARS CoV M(pro) (coronavirus main proteinase), as reportedly recently (Chou, K. C.; Wei, D. Q.; Zhong, W. Z. Biochem. Biophys. Res. Commun. 2003, 308, 148-151). It has been found that, of the 3.6 millions of compounds screened, 0.07% are with the score satisfying five of the six pharmacophore points. Moreover, each of the hit compounds has been evaluated for druggability according to 13 metrics based on physical, chemical, and structural properties. Of the 0.07% compounds thus retrieved, 17% have a perfect score of 1.0; while 23% with one druggable rule violation, 13% two violations, and 47% more than two violations. If the criterion for druggability is set at a maximum allowance of two rule violations, we obtain that only about 0.03% of the compounds screened are worthy of further tests by experiments. These findings will significantly narrow down the search scope for potential compounds, saving substantial time and money. Finally, the featured templates derived from the current study will also be very useful for guiding the design and synthesis of effective drugs for SARS therapy.     

Construction of ribosome display library based on lipocalin scaffold and screening anticalins with specificity for estradiol

A new anticalin against estradiol (E(2)), a kind of endocrine disruptor, was obtained in the present study to detect E(2) levels. A member of the lipocalin family from Pieris brassicae called bilin-binding protein (BBP) was employed for the preparation of a random library to specifically complex E(2). Sixteen amino acid residues at the center of the binding site, which were formed by four loops on top of an eight-stranded β-barrel, were subjected to targeted random mutagenesis. Estradiol-binding BBP variants so-called 'anticalins', which exhibit binding activity for compounds, such as E(2), were selected from the resulting library by combining both ribosome display and screening techniques. Four variants of complex E(2) with high affinity were identified. These variants exhibited dissociation constants (KDs) as low as 54.265 nM. ELISA showed that ribosome displayed anticalin (E(2)-A) specifically bound E(2). The 50% inhibition concentration (IC(50)) for E(2) was 50 ng mL(-1) and the limit of detection (LOD:IC(10)) was 0.071 ng mL(-1). The experimental results suggest that E(2)-A can be used as a potential anticalin to detect E(2) in animals.     

Use of virtual screening, flexible docking, and molecular interaction fields to design novel HMG-CoA reductase inhibitors for the treatment of hypercholesterolemia

Dietary changes associated with drug therapy can reduce high serum cholesterol levels and dramatically decrease the risk of coronary artery disease, stroke, and overall mortality. Statins are hypolipemic drugs that are effective in the reduction of cholesterol serum levels, attenuating cholesterol synthesis in liver by competitive inhibition regarding the substrate or molecular target HMG-CoA reductase. We have herewith used computer-aided molecular design tools, i.e., flexible docking, virtual screening in large data bases, molecular interaction fields to propose novel potential HMG-CoA reductase inhibitors that are promising for the treatment of hypercholesterolemia.     

Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry

Combinatorial chemistry and high-throughput screening technologies produce huge amounts of data on a regular basis. Sieving through these libraries of compounds and their associated assay data to identify appropriate series for follow-up is a daunting task, which has created a need for computational techniques that can find coherent islands of structure-activity relationships in this sea. Structural unit analysis (SUA) examines an entire data set so as to identify the molecular substructures or fragments that distinguish compounds with high activity from those with average activity. The algorithm is iterative and follows set heuristics in order to generate the structural units. It produces graphs that represent a set of units, which become SUA rules. Finding all of the input structures that match these graphs generates clusters. The Apriori algorithm for association rule mining is adapted to explore all of the combinations of structural units that define useful series. User-defined constraints are applied toward series selection and the refinement of rules. The significance of a series is determined by applying statistical methods appropriate to each data set. Application to the NCI-H23 (DTP Human Tumor Cell Line Screen) database serves to illustrate the process by which structural series are identified. An application of the method to scaffold hopping is then discussed in connection with proprietary screening data from a lead optimization project directed toward the treatment of respiratory tract infections at Bayer Healthcare. SUA was able to successfully identify promising alternative core structures in addition to identifying compounds with above-average activity and selectivity.     

Trajectory surface hopping molecular dynamics on Chemiluminescence of cyclic peroxides

Chemiluminescence (CL) of peroxides is one of the most highly sensitive and most useful analytical techniques. Although the mechanisms of CL were studied experimentally and theoretically in the past decades, the chemiexcitation that a ground-state specie being excited from its electronic ground state to yield excited-state products by a chemical reaction is still not completely understood. Direct dynamics simulation on CL reaction which takes into account the full complexity of the relevant potential energy surface characterizes nonadiabatic processes involved in chemiexcitation and could provide access not only to the available reaction channels but also to statistical quantities such as reaction times and quantum yields. In the last decade, the trajectory surface hopping (TSH) molecular dynamics (MD) which is one of the mixed quantum-classical approaches is hence adopted to simulate the nonadiabatic process in CL of cyclic peroxides. In this article, the basic principle of TSH-MD and the successful applications on the CL reactions were shortly review.