Ultrafast protein structure-based virtual screening with Panther

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study

Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC. A set of comprehensive pharmacoinformatics approaches was implemented and, finally, seventeen potential molecules were found to be effective for the modulation of hRRM1 activity. Molecular docking, negative image-based ShaEP scoring, absolute binding free energy, in silico pharmacokinetics, and toxicity assessments corroborated the potentiality of the selected molecules. Almost all molecules showed higher affinity in comparison to gemcitabine and naphthyl salicylic acyl hydrazone (NSAH). On binding interaction analysis, a number of critical amino acids was found to hold the molecules at the active site cavity. The molecular dynamics (MD) simulation study also indicated the stability between protein and ligands. High negative MM-GBSA (molecular mechanics generalized Born and surface area) binding free energy indicated the potentiality of the molecules. Therefore, the proposed molecules might have the potential to be effective therapeutics for the management of BTC.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations

Despite significant studies on the COVID-19 pandemic, scientists around the world are still battling to find a definitive therapy against the ongoing severe global health crisis. In this study, advanced computational approaches have been employed to identify bioactive food constituents as potential SARS-CoV-2 PLpro inhibitors-modulators. As a validated antiviral drug target, PLpro has gained tremendous attention for therapeutics developments. Therefore, targeting the multifunctional SARS-CoV-2 PLpro protein, ∼1039 bioactive dietary compounds have been screened extensively through novel techniques like negative image-based (NIB) screening and molecular docking approaches. In particular, the three different models of NIB screening have been generated and used to re-score the dietary compounds based on the negative image which is created by reversing the shape and electrostatics features of PLpro protein's ligand-binding cavity. Further, 100 ns molecular dynamics simulation has been performed and MM-GBSA based binding free energies have been estimated for the final proposed four dietary compounds (PC000550, PC000361, PC000558, and PC000573) as potential inhibitors/modulators of SARS-CoV-2 PLpro protein. Employed computational study outcome also has been compared with respect to the earlier experimentally investigated compound GRL0617 against SARS-CoV-2 PLpro protein, which suggests much greater interaction potential in terms of binding affinity and other energetic contributions for the proposed dietary compounds. Hence, the present study suggests that proposed dietary compounds can be suitable chemical entities for modulating the activity of PLpro protein or can be further utilized for optimizing or screening of novel chemical surrogates, however also needs experimental evaluation for entry in clinical studies for better assessment.

Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening

Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein's ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.

Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents

The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein -ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.

Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.

Negative Image-Based Screening: Rigid Docking Using Cavity Information

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

Screening of Natural Products Targeting SARS-CoV-2-ACE2 Receptor Interface - A MixMD Based HTVS Pipeline

The COVID-19 pandemic, caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a severe global health crisis now. SARS-CoV-2 utilizes its Spike protein receptor-binding domain (S-protein) to invade human cell through binding to Angiotensin-Converting Enzyme 2 receptor (ACE2). S-protein is the key target for many therapeutics and vaccines. Potential S-protein-ACE2 fusion inhibitor is expected to block the virus entry into the host cell. In many countries, traditional practices, based on natural products (NPs) have been in use to slow down COVID-19 infection. In this study, a protocol was applied that combines mixed solvent molecular dynamics simulations (MixMD) with high-throughput virtual screening (HTVS) to search NPs to block SARS-CoV-2 entry into the human cell. MixMD simulations were employed to discover the most promising stable binding conformations of drug-like probes in the S-protein-ACE2 interface. Detected stable sites were used for HTVs of 612093 NPs to identify molecules that could interfere with the S-protein-ACE2 interaction. In total, 19 NPs were selected with rescoring model. These top-ranked NP-S-protein complexes were subjected to classical MD simulations for 300 ns (3 replicates of 100 ns) to estimate the stability and affinity of binding. Three compounds, ZINC000002128789, ZINC000002159944 and SN00059335, showed better stability in all MD runs, of which ZINC000002128789 was predicted to have the highest binding affinity, suggesting that it could be effective modulator in RBD-ACE2 interface to prevent SARS-CoV-2 infection. Our results support that NPs may provide tools to fight COVID-19.

Genomically Aided Diagnosis of Severe Developmental Disorders

Our ability to make accurate and specific genetic diagnoses in individuals with severe developmental disorders has been transformed by data derived from genomic sequencing technologies. These data reveal both the patterns and rates of different mutational mechanisms and identify regions of the human genome with fewer mutations than would be expected. In outbred populations, the most common identifiable cause of severe developmental disorders is de novo mutation affecting the coding region in one of approximately 500 different genes, almost universally showing constraint. Simply combining the location of a de novo genomic event with its predicted consequence on the gene product gives significant diagnostic power. Our knowledge of the diversity of phenotypic consequences associated with comparable diagnostic genotypes at each locus is improving. Computationally useful phenotype data will improve diagnostic interpretation of ultrarare genetic variants and, in the long run, indicate which specific embryonic processes have been perturbed.

A Perspective: Active Role of Lipids in Neurotransmitter Dynamics

Synaptic neurotransmission is generally considered as a function of membrane-embedded receptors and ion channels in response to the neurotransmitter (NT) release and binding. This perspective aims to widen the protein-centric view by including another vital component—the synaptic membrane—in the discussion. A vast set of atomistic molecular dynamics simulations and biophysical experiments indicate that NTs are divided into membrane-binding and membrane-nonbinding categories. The binary choice takes place at the water-membrane interface and follows closely the positioning of the receptors’ binding sites in relation to the membrane. Accordingly, when a lipophilic NT is on route to a membrane-buried binding site, it adheres on the membrane and, then, travels along its plane towards the receptor. In contrast, lipophobic NTs, which are destined to bind into receptors with extracellular binding sites, prefer the water phase. This membrane-based sorting splits the neurotransmission into membrane-independent and membrane-dependent mechanisms and should make the NT binding into the receptors more efficient than random diffusion would allow. The potential implications and notable exceptions to the mechanisms are discussed here. Importantly, maintaining specific membrane lipid compositions (MLCs) at the synapses, especially regarding anionic lipids, affect the level of NT-membrane association. These effects provide a plausible link between the MLC imbalances and neurological diseases such as depression or Parkinson’s disease. Moreover, the membrane plays a vital role in other phases of the NT life cycle, including storage and release from the synaptic vesicles, transport from the synaptic cleft, as well as their synthesis and degradation.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

Fragment- and negative image-based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.

A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein’s ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Chinese herbal medicine has recently gained worldwide attention. The curative mechanism of Chinese herbal medicine is compared with that of western medicine at the molecular level. The treatment mechanism of most Chinese herbal medicines is still not clear. How do we integrate Chinese herbal medicine compounds with modern medicine? Chinese herbal medicine drug-like prediction method is particularly important. A growing number of Chinese herbal source compounds are now widely used as drug-like compound candidates. An important way for pharmaceutical companies to develop drugs is to discover potentially active compounds from related herbs in Chinese herbs. The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method. In this paper, we focus on the prediction methods for the medicinal properties of Chinese herbal medicines. We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method. Finally, we present the prospect of the joint application of various methods.

Spresso: an ultrafast compound pre-screening method based on compound decomposition

Motivation

Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate ≥ 10 million compounds.

Results

In this article, we propose a novel, docking-based pre-screening protocol named Spresso (Speedy PRE-Screening method with Segmented cOmpounds). Partial structures (fragments) are common among many compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. Our method increases calculation speeds by ∼200-fold compared to conventional methods.

Availability and Implementation

Spresso is written in C ++ and Python, and is available as an open-source code (http://www.bi.cs.titech.ac.jp/spresso/) under the GPLv3 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Discovery of Retinoic Acid-Related Orphan Receptor γt Inverse Agonists via Docking and Negative Image-Based Screening

Retinoic acid-related orphan receptor γt (RORγt) has a vital role in the differentiation of T-helper 17 (TH17) cells. Potent and specific RORγt inverse agonists are sought for treating TH17-related diseases such as psoriasis, rheumatoid arthritis, and type 1 diabetes. Here, the aim was to discover novel RORγt ligands using both standard molecular docking and negative image-based screening. Interestingly, both of these in silico techniques put forward mostly the same compounds for experimental testing. In total, 11 of the 34 molecules purchased for testing were verified as RORγt inverse agonists, thus making the effective hit rate 32%. The pIC₅₀ values for the compounds varied from 4.9 (11 μM) to 6.2 (590 nM). Importantly, the fact that the verified hits represent four different cores highlights the structural diversity of the RORγt inverse agonism and the ability of the applied screening methodologies to facilitate much-desired scaffold hopping for drug design.

Blocking oestradiol synthesis pathways with potent and selective coumarin derivatives

A comprehensive set of 3-phenylcoumarin analogues with polar substituents was synthesised for blocking oestradiol synthesis by 17-β-hydroxysteroid dehydrogenase 1 (HSD1) in the latter part of the sulphatase pathway. Five analogues produced ≥62% HSD1 inhibition at 5 µM and, furthermore, three of them produced ≥68% inhibition at 1 µM. A docking-based structure-activity relationship analysis was done to determine the molecular basis of the inhibition and the cross-reactivity of the analogues was tested against oestrogen receptor, aromatase, cytochrome P450 1A2, and monoamine oxidases. Most of the analogues are only modestly active with 17-β-hydroxysteroid dehydrogenase 2 – a requirement for lowering effective oestradiol levels in vivo. Moreover, the analysis led to the synthesis and discovery of 3-imidazolecoumarin as a potent aromatase inhibitor. In short, coumarin core can be tailored with specific ring and polar moiety substitutions to block either the sulphatase pathway or the aromatase pathway for treating breast cancer and endometriosis.

Improving Docking Performance Using Negative Image-Based Rescoring

Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing the docking-based ligand conformers directly against the target protein's cavity shape and electrostatics. The similarity comparison of the conformers is performed without geometry optimization against the negative image of the target protein's ligand-binding cavity using the negative image-based (NIB) screening protocol. The viability of the NIB rescoring or the R-NiB, pioneered in this study, was tested with 11 target proteins using benchmark libraries. By focusing on the shape/electrostatics complementarity of the ligand-receptor association, the R-NiB is able to improve the early enrichment of docking essentially without adding to the computing cost. By implementing consensus scoring, in which the R-NiB and the original docking scoring are weighted for optimal outcome, the early enrichment is improved to a level that facilitates effective drug discovery. Moreover, the use of equal weight from the original docking scoring and the R-NiB scoring improves the yield in most cases.

Structure-Activity Relationship Analysis of 3-Phenylcoumarin-Based Monoamine Oxidase B Inhibitors

Monoamine oxidase B (MAO-B) catalyzes deamination of monoamines such as neurotransmitters dopamine and norepinephrine. Accordingly, small-molecule MAO-B inhibitors potentially alleviate the symptoms of dopamine-linked neuropathologies such as depression or Parkinson's disease. Coumarin with a functionalized 3-phenyl ring system is a promising scaffold for building potent MAO-B inhibitors. Here, a vast set of 3-phenylcoumarin derivatives was designed using virtual combinatorial chemistry or rationally de novo and synthesized using microwave chemistry. The derivatives inhibited the MAO-B at 100 nM−1 μM. The IC₅₀ value of the most potent derivative 1 was 56 nM. A docking-based structure-activity relationship analysis summarizes the atom-level determinants of the MAO-B inhibition by the derivatives. Finally, the cross-reactivity of the derivatives was tested against monoamine oxidase A and a specific subset of enzymes linked to estradiol metabolism, known to have coumarin-based inhibitors. Overall, the results indicate that the 3-phenylcoumarins, especially derivative 1, present unique pharmacological features worth considering in future drug development.

Molecular Docking-Based Design and Development of a Highly Selective Probe Substrate for UDP-glucuronosyltransferase 1A10

Intestinal and hepatic glucuronidation by the UDP-glucuronosyltransferases (UGTs) greatly affect the bioavailability of phenolic compounds. UGT1A10 catalyzes glucuronidation reactions in the intestine, but not in the liver. Here, our aim was to develop selective, fluorescent substrates to easily elucidate UGT1A10 function. To this end, homology models were constructed and used to design new substrates, and subsequently, six novel C3-substituted (4-fluorophenyl, 4-hydroxyphenyl, 4-methoxyphenyl, 4-(dimethylamino)phenyl, 4-methylphenyl, or triazole) 7-hydroxycoumarin derivatives were synthesized from inexpensive starting materials. All tested compounds could be glucuronidated to nonfluorescent glucuronides by UGT1A10, four of them highly selectively by this enzyme. A new UGT1A10 mutant, 1A10-H210M, was prepared on the basis of the newly constructed model. Glucuronidation kinetics of the new compounds, in both wild-type and mutant UGT1A10 enzymes, revealed variable effects of the mutation. All six new C3-substituted 7-hydroxycoumarins were glucuronidated faster by human intestine than by liver microsomes, supporting the results obtained with recombinant UGTs. The most selective 4-(dimethylamino)phenyl and triazole C3-substituted 7-hydroxycoumarins could be very useful substrates in studying the function and expression of the human UGT1A10.

2016 EMBO Chemical Biology Conference

The full breadth of the field: The 2016 EMBO Chemical Biology Conference, covering topics from tool development to biological applications and from computational drug design to synthetic chemistry, took place in Heidelberg from 31st August to 3rd September.

Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization

Abstract

Receiver operating characteristics (ROC) curve with the calculation of area under curve (AUC) is a useful tool to evaluate the performance of biomedical and chemoinformatics data. For example, in virtual drug screening ROC curves are very often used to visualize the efficiency of the used application to separate active ligands from inactive molecules. Unfortunately, most of the available tools for ROC analysis are implemented into commercially available software packages, or are plugins in statistical software, which are not always the easiest to use. Here, we present Rocker, a simple ROC curve visualization tool that can be used for the generation of publication quality images. Rocker also includes an automatic calculation of the AUC for the ROC curve and Boltzmann-enhanced discrimination of ROC (BEDROC). Furthermore, in virtual screening campaigns it is often important to understand the early enrichment of active ligand identification, for this Rocker offers automated calculation routine. To enable further development of Rocker, it is freely available (MIT-GPL license) for use and modifications from our web-site (http://www.jyu.fi/rocker).

Graphical Abstract

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.

Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC50 value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC50 values from 5.5 to 6.5.