Enhanced CACTVS browser of the Open NCI Database

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico, which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.

Recent advances in the self-referencing embedded strings (SELFIES) library

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencing embedded strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation called selfies. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints, and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of selfies, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of selfies (version 2.1.1) in this manuscript. Our library, selfies, is available at GitHub (https://github.com/aspuru-guzik-group/selfies).

Virtual Screening in the Identification of Sirtuins’ Activity Modulators

Sirtuins are NAD⁺-dependent deac(et)ylases with different subcellular localization. The sirtuins’ family is composed of seven members, named SIRT-1 to SIRT-7. Their substrates include histones and also an increasing number of different proteins. Sirtuins regulate a wide range of different processes, ranging from transcription to metabolism to genome stability. Thus, their dysregulation has been related to the pathogenesis of different diseases. In this review, we discussed the pharmacological approaches based on sirtuins’ modulators (both inhibitors and activators) that have been attempted in in vitro and/or in in vivo experimental settings, to highlight the therapeutic potential of targeting one/more specific sirtuin isoform(s) in cancer, neurodegenerative disorders and type 2 diabetes. Extensive research has already been performed to identify SIRT-1 and -2 modulators, while compounds targeting the other sirtuins have been less studied so far. Beside sections dedicated to each sirtuin, in the present review we also included sections dedicated to pan-sirtuins’ and to parasitic sirtuins’ modulators. A special focus is dedicated to the sirtuins’ modulators identified by the use of virtual screening.

Identification and Inhibition of the Druggable Allosteric Site of SARS-CoV-2 NSP10/NSP16 Methyltransferase through Computational Approaches

Since its emergence in early 2019, the respiratory infectious virus, SARS-CoV-2, has ravaged the health of millions of people globally and has affected almost every sphere of life. Many efforts are being made to combat the COVID-19 pandemic’s emerging and recurrent waves caused by its evolving and more infectious variants. As a result, novel and unexpected targets for SARS-CoV-2 have been considered for drug discovery. 2′-O-Methyltransferase (nsp10/nsp16) is a significant and appealing target in the SARS-CoV-2 life cycle because it protects viral RNA from the host degradative enzymes via a cap formation process. In this work, we propose prospective allosteric inhibitors that target the allosteric site, SARS-CoV-2 MTase. Four drug libraries containing ~119,483 compounds were screened against the allosteric site of SARS-CoV-2 MTase identified in our research. The identified best compounds exhibited robust molecular interactions and alloscore-score rankings with the allosteric site of SARS-CoV-2 MTase. Moreover, to further assess the dynamic stability of these compounds (CHEMBL2229121, ZINC000009464451, SPECS AK-91811684151, NCI-ID = 715319), a 100 ns molecular dynamics simulation, along with its holo-form, was performed to provide insights on the dynamic nature of these allosteric inhibitors at the allosteric site of the SARS-CoV-2 MTase. Additionally, investigations of MM-GBSA binding free energies revealed a good perspective for these allosteric inhibitor–enzyme complexes, indicating their robust antagonistic action on SARS-CoV-2 (nsp10/nsp16) methyltransferase. We conclude that these allosteric repressive agents should be further evaluated through investigational assessments in order to combat the proliferation of SARS-CoV-2.

Combined Pharmacophore and Grid-Independent Molecular Descriptors (GRIND) Analysis to Probe 3D Features of Inositol 1,4,5-Trisphosphate Receptor (IP3R) Inhibitors in Cancer

Inositol 1, 4, 5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling plays a pivotal role in different cellular processes, including cell proliferation and cell death. Remodeling Ca²⁺ signals by targeting the downstream effectors is considered an important hallmark in cancer progression. Despite recent structural analyses, no binding hypothesis for antagonists within the IP₃-binding core (IBC) has been proposed yet. Therefore, to elucidate the 3D structural features of IP₃R modulators, we used combined pharmacoinformatic approaches, including ligand-based pharmacophore models and grid-independent molecular descriptor (GRIND)-based models. Our pharmacophore model illuminates the existence of two hydrogen-bond acceptors (2.62 Å and 4.79 Å) and two hydrogen-bond donors (5.56 Å and 7.68 Å), respectively, from a hydrophobic group within the chemical scaffold, which may enhance the liability (IC₅₀) of a compound for IP₃R inhibition. Moreover, our GRIND model (PLS: Q² = 0.70 and R² = 0.72) further strengthens the identified pharmacophore features of IP₃R modulators by probing the presence of complementary hydrogen-bond donor and hydrogen-bond acceptor hotspots at a distance of 7.6-8.0 Å and 6.8-7.2 Å, respectively, from a hydrophobic hotspot at the virtual receptor site (VRS). The identified 3D structural features of IP₃R modulators were used to screen (virtual screening) 735,735 compounds from the ChemBridge database, 265,242 compounds from the National Cancer Institute (NCI) database, and 885 natural compounds from the ZINC database. After the application of filters, four compounds from ChemBridge, one compound from ZINC, and three compounds from NCI were shortlisted as potential hits (antagonists) against IP₃R. The identified hits could further assist in the design and optimization of lead structures for the targeting and remodeling of Ca²⁺ signals in cancer.

Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.

Molecular dynamics guided insight, binding free energy calculations and pharmacophore-based virtual screening for the identification of potential VEGFR2 inhibitors

Vascular endothelial growth factor-A (VEGF-A) is a crucial member of the Vascular endothelial growth factor (VEGF) family which mediates the metastasis of tumor by 'angiogenic switch'. Therefore, targeting a VEGF-A mediated VEGFR2 signaling pathway is the most promising approach to repress the angiogenesis of tumor cells. VEGFR2 inhibitors are two types: Type I and Type II. Type II inhibitors have more chemical space to exploit and have better selectivity because of allosteric binding pocket over type I inhibitors. Hence, The present study encompasses identification of potential type II VEGFR2 inhibitors employing pharmacophore based virtual screnning. In this study, ten five featured pharmacophore model were generated from a dataset of 39 biaryl urea analogs.Out of all, ADDRR_1 pharmacophore model were used to screen the library of 5.2 million compounds retrieved from NCI, Maybridge, Asinex and Zinc databases. 7000 hits were filtered out from the pharmacophore-based virtual screening based on the phase fitness score. Among all best ten hits were identified employing extra precision mode of GLIDE module. ZINC00759038 and 211246 were chosen as top hits based on docking score, free binding energy, and ADME profile. They were subjected to molecular-dynamic studies to assess the hits-VEGFR2 binding stability. It suggests that ZINC00759038-VEGFR2 and 211246-VEGFR2 complexes are quite stable for the 20 ns simulation period. The strength of hit-protein complexes were further assessed by thermodynamic analysis of MD simulation studies by MMGBSA. Interestingly, these hits retains 90% similarity with standard VEGFR2 inhibitor (Sorafenib). Hence, these identified hits may led to new lead compounda as VEGFR2 inhibitors.

Identification of a Novel Scaffold for Inhibition of Dipeptidyl Peptidase-4

Dipeptidyl peptidase-4 (DPP-4) is considered a major drug target for type 2 diabetes mellitus (T2DM). In addition to T2DM, a regulatory role of DPP-4 was also found in cardiovascular diseases. Existing DPP-4 inhibitors have been reported to have several adverse effects. In this study, a computer-aided drug design approach and its use to detect a novel class of inhibitor for DPP-4 are reported. Through structure and pharmacophore-based screening, we identified 13 hit compounds from an ∼4-million-compound library. Physical interactions of these hits with DPP-4 were studied using docking and explicit solvent molecular dynamics (MD) simulations. Later, MMPBSA binding energy was calculated for the ligand/protein simulation trajectories to determine the stability of compounds in the binding cavity. These compounds have a novel scaffold and exhibited a stable binding mode. "Best-in-screen" compounds (or their closest available analogs) were resourced and their inhibition of DPP-4 activity was experimentally validated using an in vitro enzyme activity assay in the presence of 100 and 10 μM compounds. These assays identified a compound with a spirochromanone center with 53% inhibition activity at a 100 μM concentration. A further five spirochromanone compounds were synthesized and examined in silico and in vitro; again, one compound showed 53% inhibitory activity action at 100 μM. Overall, this study identified two novel "spirochromanone" compounds that lowered DPP-4 activity by more than ∼50% at 100 μM. This study also showed the impact of fast in silico drug design techniques utilizing virtual screening and MD to identify novel scaffolds to bind and inhibit DPP-4. Spirochromanone motif identified here may be used to design molecules to achieve drug-like inhibitory action against DPP-4.

Targeting Semaphorin 3C in Prostate Cancer With Small Molecules

Despite the amenability of early-stage prostate cancer to surgery and radiation therapy, locally advanced and metastatic prostate cancer is clinically problematic. Chemical castration is often used as a first-line therapy for advanced disease, but progression to the castration-resistant prostate cancer phase occurs with dependable frequency, largely through mutations to the androgen receptor (AR), aberrant AR signaling, and AR-independent mechanisms, among other causes. Semaphorin 3C (SEMA3C) is a secreted signaling protein that is essential for cardiac and neuronal development and has been shown to be regulated by the AR, to drive epithelial-to-mesenchymal transition and stem features in prostate cells, to activate receptor tyrosine kinases, and to promote cancer progression. Given that SEMA3C is linked to several key aspects of prostate cancer progression, we set out to explore SEMA3C inhibition by small molecules as a prospective cancer therapy. A homology-based SEMA3C protein structure was created, and its interaction with the neuropilin (NRP)-1 receptor was modeled to guide the development of the corresponding disrupting compounds. Experimental screening of 146 in silico‒identified molecules from the National Cancer Institute library led to the discovery of four promising candidates that effectively bind to SEMA3C, inhibit its association with NRP1, and attenuate prostate cancer growth. These findings provide proof of concept for the feasibility of inhibiting SEMA3C with small molecules as a therapeutic approach for prostate cancer.

Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein–protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

In silico approach in reveal traditional medicine plants pharmacological material basis

In recent years, studies of traditional medicinal plants have gradually increased worldwide because the natural sources and variety of such plants allow them to complement modern pharmacological approaches. As computer technology has developed, in silico approaches such as virtual screening and network analysis have been widely utilized in efforts to elucidate the pharmacological basis of the functions of traditional medicinal plants. In the process of new drug discovery, the application of virtual screening and network pharmacology can enrich active compounds among the candidates and adequately indicate the mechanism of action of medicinal plants, reducing the cost and increasing the efficiency of the whole procedure. In this review, we first provide a detailed research routine for examining traditional medicinal plants by in silico techniques and elaborate on their theoretical principles. We also survey common databases, software programs and website tools that can be used for virtual screening and pharmacological network construction. Furthermore, we conclude with a simple example that illustrates the whole methodology, and we present perspectives on the development and application of this in silico methodology to reveal the pharmacological basis of the effects of traditional medicinal plants.

Strategies of Virtual Screening in Medicinal Chemistry

Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.

MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) for identifying protein-ligand fragment interaction, pathways and SNPs

Protein-ligand interaction analysis is an important step of drug design and protein engineering in order to predict the binding affinity and selectivity between ligands to the target proteins. To date, there are more than 100 000 structures available in the Protein Data Bank (PDB), of which ∼30% are protein-ligand (MW below 1000 Da) complexes. We have developed the integrative web server MANORAA (Mapping Analogous Nuclei Onto Residue And Affinity) with the aim of providing a user-friendly web interface to assist structural study and design of protein-ligand interactions. In brief, the server allows the users to input the chemical fragments and present all the unique molecular interactions to the target proteins with available three-dimensional structures in the PDB. The users can also link the ligands of interest to assess possible off-target proteins, human variants and pathway information using our all-in-one integrated tools. Taken together, we envisage that the server will facilitate and improve the study of protein-ligand interactions by allowing observation and comparison of ligand interactions with multiple proteins at the same time. (http://manoraa.org).

Discovery of selective inhibitors of tyrosyl-DNA phosphodiesterase 2 by targeting the enzyme DNA-binding cleft

Tyrosyl-DNA phosphodiesterase 2 (TDP2) processes protein/DNA adducts resulting from abortive DNA topoisomerase II (Top2) activity. TDP2 inhibition could provide synergism with the Top2 poison class of chemotherapeutics. By virtual screening of the NCI diversity small molecule database, we identified selective TDP2 inhibitors and experimentally verified their selective inhibitory activity. Three inhibitors exhibited low-micromolar IC50 values. Molecular dynamics simulations revealed a common binding mode for these inhibitors, involving association to the TDP2 DNA-binding cleft. MM-PBSA per-residue energy decomposition identified important interactions of the compounds with specific TDP2 residues. These interactions could provide new avenues for synthetic optimization of these scaffolds.

Identifying putative drug targets and potential drug leads: starting points for virtual screening and docking

The availability of 3D models of both drug leads (small molecule ligands) and drug targets (proteins) is essential to molecular docking and computational drug discovery. This chapter describes a simple approach that can be used to identify both drug leads and drug targets using two popular Web-accessible databases: (1) DrugBank and (2) The Human Metabolome Database. First, it is illustrated how putative drug targets and drug leads for exogenous diseases (i.e., infectious diseases) can be readily identified and their 3D structures selected using only the genomic sequences from pathogenic bacteria or viruses as input. The second part illustrates how putative drug targets and drug leads for endogenous diseases (i.e., noninfectious diseases or chronic conditions) can be identified using similar databases and similar sequence input. This chapter is intended to illustrate how bioinformatics and cheminformatics can work synergistically to help provide the necessary inputs for computer-aided drug design.

iDrug: a web-accessible and interactive drug discovery and design platform

BACKGROUND

The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed.

RESULTS

We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work.

CONCLUSIONS

iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.

An infrastructure to mine molecular descriptors for ligand selection on virtual screening

The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.

Cheminformatic analysis of high-throughput compound screens

This article gives an overview of basic computational methods that are commonly used for analyzing small molecule screening data in the chemical genomics field. First, we introduce cheminformatic concepts for analyzing drug-like small molecule structures and their properties. Second, we introduce compound selection approaches for assembling screening libraries using compound property and diversity analyses. Finally, we discuss methods for interpreting screening hits by analyzing compound structures and induced phenotypes using similarity search and clustering approaches. These are critical steps for optimizing screening hits, and relating structure to bioactivity and phenotype.

Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery

Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.

KCF-S: KEGG Chemical Function and Substructure for improved interpretability and prediction in chemical bioinformatics

Background

In order to develop hypothesis on unknown metabolic pathways, biochemists frequently rely on literature that uses a free-text format to describe functional groups or substructures. In computational chemistry or cheminformatics, molecules are typically represented by chemical descriptors, i.e., vectors that summarize information on its various properties. However, it is difficult to interpret these chemical descriptors since they are not directly linked to the terminology of functional groups or substructures that the biochemists use.

Methods

In this study, we used KEGG Chemical Function (KCF) format to computationally describe biochemical substructures in seven attributes that resemble biochemists' way of dealing with substructures.

Results

We established KCF-S (KCF-and-Substructures) format as an additional structural information of KCF. Applying KCF-S revealed the specific appearance of substructures from various datasets of molecules that describes the characteristics of the respective datasets. Structure-based clustering of molecules using KCF-S resulted the clusters in which molecular weights and structures were less diverse than those obtained by conventional chemical fingerprints. We further applied KCF-S to find the pairs of molecules that are possibly converted to each other in enzymatic reactions, and KCF-S clearly improved predictive performance than that presented previously.

Conclusions

KCF-S defines biochemical substructures with keeping interpretability, suggesting the potential to apply more studies on chemical bioinformatics. KCF and KCF-S can be automatically converted from Molfile format, enabling to deal with molecules from any data sources.

Identification of semicarbazones, thiosemicarbazones and triazine nitriles as inhibitors of Leishmania mexicana cysteine protease CPB

Cysteine proteases of the papain superfamily are present in nearly all eukaryotes. They play pivotal roles in the biology of parasites and inhibition of cysteine proteases is emerging as an important strategy to combat parasitic diseases such as sleeping sickness, Chagas' disease and leishmaniasis. Homology modeling of the mature Leishmania mexicana cysteine protease CPB2.8 suggested that it differs significantly from bovine cathepsin B and thus could be a good drug target. High throughput screening of a compound library against this enzyme and bovine cathepsin B in a counter assay identified four novel inhibitors, containing the warhead-types semicarbazone, thiosemicarbazone and triazine nitrile, that can be used as leads for antiparasite drug design. Covalent docking experiments confirmed the SARs of these lead compounds in an effort to understand the structural elements required for specific inhibition of CPB2.8. This study has provided starting points for the design of selective and highly potent inhibitors of L. mexicana cysteine protease CPB that may also have useful efficacy against other important cysteine proteases.

New anticancer agents mimicking protein recognition motifs

The novel tetrasubstituted pyrrole derivatives 8g, 8h, and 8i showed selective cytotoxicity against M14 melanoma cells at low micromolar concentration. Structure-activity relationships (SARs) indicated the presence of three aromatic substituents on the pyrrole core as necessary for biological activity. Computational studies strongly suggest that the peculiar 3D orientation of these substituents is able to reproduce the hydrophobic side chains in LxxLL-like protein recognition motifs. Biological results showed altered p53 expression and nuclear translocation in cells sensitive to the compounds, suggesting p53 involvement in their anticancer mechanism of action. Unfortunately, because of poor solubility of the active analogues, it was not possible to perform further investigation by NMR techniques. Pharmacophore models were generated and used to perform 3D searches in molecular databases. Results indicated that two compounds share the same pharmacological profile and the same pharmacophoric features with our new derivatives, and one of them inhibited MDM2-MDM4 heterodimer formation.

Discovery of a novel small molecule inhibitor targeting the frataxin/ubiquitin interaction via structure-based virtual screening and bioassays

Friedreich's ataxia (FRDA) is an autosomal recessive neuro- and cardiodegenerative disorder for which there are no proven effective treatments. FRDA is caused by decreased expression and/or function of the mitochondrial protein frataxin. Here, we report findings that frataxin is degraded via the ubiquitin-proteasomal pathway and that it is ubiquitinated at residue K(147) in Calu-6 cells. A theoretical model of the frataxin-K(147)/Ub complex, constructed by combining bioinformatics interface predictions with information-driven docking, revealed a hitherto unnoticed, potential ubiquitin-binding domain in frataxin. Through structure-based virtual screening and cell-based assays, we discovered a novel small molecule (compound (+)-11) able to prevent frataxin ubiquitination and degradation. (+)-11 was synthesized and tested for specific binding to frataxin by an UF-LC/MS based ligand-binding assay. Follow-up scaffold-based searches resulted in the identification of a lead series with micromolar activity in disrupting the frataxin/Ub interaction. This study also suggests that frataxin could be a potential target for FRDA drug development.

Antifungal activity of enynediesters and acetylenic compounds obtained by synthesis and in silico prediction pattern

OBJECTIVE

In this paper, we describe the preparation and synthesis of several enynediesters, conjugated diynes and acetylenic compounds from starting materials comprising one hydroxyl and one acetylenyl group and their antifungal activity.

MATERIALS AND METHODS

For synthesis of the compounds, a combined solution of N,N'-dicyclohexylcarbodiimide and 4-dimethylaminopyridin in CH2Cl2 was added to a solution of compound containing one hydroxyl group and propiolic acid at 0°C over a period of 1 hour. As the reaction occurs under very mild conditions this procedure offers easy access to 1,3-diynes in a very short reaction time. Some of the compounds are commercially available. The antifungal activity of these compounds against Candida albicans, Aspergillus niger and Saccharomyces cerevisiae was investigated.

RESULTS

Among the compounds tested, some showed potent activity.

CONCLUSION

The results of synthesis showed that the direct coupling of two acetylenes is suitable for the preparation of diynes. The in silico study used here was able to predict if the compound with triple bond can posses the antifungal activity with a reasonable prediction. The result of investigations at both in silico and in vitro levels confirmed that the position of the triple bond is important for antifungal activity.

Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes

BACKGROUND

The most important factor affecting metabolic excretion of compounds from the body is their half-life time. This provides an indication of compound stability of, for example, drug molecules. We report on our efforts to develop QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes.

METHOD

A variety of QSAR models generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR and StarDrop) were analyzed. The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans.

CONCLUSION

In many cases, the accuracy of prediction achieved on one external test set did not correspond to the results achieved with another test set. The most predictive models were used for predicting the metabolic stability of compounds from the open NCI database, the results of which are publicly available on the NCI/CADD Group web server ( http://cactus.nci.nih.gov ).

Web tools for predictive toxicology model building

INTRODUCTION

The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges.

AREAS COVERED

The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details.

EXPERT OPINION

The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

Discovery of new inhibitors of Cdc25B dual specificity phosphatases by structure-based virtual screening

Cell division cycle 25 (Cdc25) proteins are highly conserved dual specificity phosphatases that regulate cyclin-dependent kinases and represent attractive drug targets for anticancer therapies. To discover more potent and diverse inhibitors of Cdc25 biological activity, virtual screening was performed by docking 2.1 million compounds into the Cdc25B active site. An initial subset of top-ranked compounds was selected and assayed, and 15 were found to have enzyme inhibition activity at micromolar concentration. Among these, four structurally diverse inhibitors with a different inhibition profile were found to inhibit human MCF-7, PC-3, and K562 cancer cell proliferation and significantly affect the cell cycle progression. A subsequent hierarchical similarity search with the most active reversible Cdc25B inhibitor found led to the identification of an additional set of 19 ligands, three of which were confirmed as Cdc25B inhibitors with IC(50) values of 7.9, 4.2, and 9.9 μM, respectively.

Identification of anti-malarial compounds as novel antagonists to chemokine receptor CXCR4 in pancreatic cancer cells

Despite recent advances in targeted therapies, patients with pancreatic adenocarcinoma continue to have poor survival highlighting the urgency to identify novel therapeutic targets. Our previous investigations have implicated chemokine receptor CXCR4 and its selective ligand CXCL12 in the pathogenesis and progression of pancreatic intraepithelial neoplasia and invasive pancreatic cancer; hence, CXCR4 is a promising target for suppression of pancreatic cancer growth. Here, we combined in silico structural modeling of CXCR4 to screen for candidate anti-CXCR4 compounds with in vitro cell line assays and identified NSC56612 from the National Cancer Institute's (NCI) Open Chemical Repository Collection as an inhibitor of activated CXCR4. Next, we identified that NSC56612 is structurally similar to the established anti-malarial drugs chloroquine and hydroxychloroquine. We evaluated these compounds in pancreatic cancer cells in vitro and observed specific antagonism of CXCR4-mediated signaling and cell proliferation. Recent in vivo therapeutic applications of chloroquine in pancreatic cancer mouse models have demonstrated decreased tumor growth and improved survival. Our results thus provide a molecular target and basis for further evaluation of chloroquine and hydroxychloroquine in pancreatic cancer. Historically safe in humans, chloroquine and hydroxychloroquine appear to be promising agents to safely and effectively target CXCR4 in patients with pancreatic cancer.

Identification of lead compounds targeting the cathepsin B-like enzyme of Eimeria tenella

Cysteine peptidases have been implicated in the development and pathogenesis of Eimeria. We have identified a single-copy cathepsin B-like cysteine peptidase gene in the genome database of Eimeria tenella (EtCatB). Molecular modeling of the predicted protein suggested that it differs significantly from host enzymes and could be a good drug target. EtCatB was expressed and secreted as a soluble, active, glycosylated mature enzyme from Pichia pastoris. Biochemical characterization of the recombinant enzyme confirmed that it is cathepsin B-like. Screening of a focused library against the enzyme identified three inhibitors (a nitrile, a thiosemicarbazone, and an oxazolone) that can be used as leads for novel drug discovery against Eimeria. The oxazolone scaffold is a novel cysteine peptidase inhibitor; it may thus find widespread use.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

ChemMine tools: an online service for analyzing and clustering small molecules

ChemMine Tools is an online service for small molecule data analysis. It provides a web interface to a set of cheminformatics and data mining tools that are useful for various analysis routines performed in chemical genomics and drug discovery. The service also offers programmable access options via the R library ChemmineR. The primary functionalities of ChemMine Tools fall into five major application areas: data visualization, structure comparisons, similarity searching, compound clustering and prediction of chemical properties. First, users can upload compound data sets to the online Compound Workbench. Numerous utilities are provided for compound viewing, structure drawing and format interconversion. Second, pairwise structural similarities among compounds can be quantified. Third, interfaces to ultra-fast structure similarity search algorithms are available to efficiently mine the chemical space in the public domain. These include fingerprint and embedding/indexing algorithms. Fourth, the service includes a Clustering Toolbox that integrates cheminformatic algorithms with data mining utilities to enable systematic structure and activity based analyses of custom compound sets. Fifth, physicochemical property descriptors of custom compound sets can be calculated. These descriptors are important for assessing the bioactivity profile of compounds in silico and quantitative structure—activity relationship (QSAR) analyses. ChemMine Tools is available at: http://chemmine.ucr.edu.

Virtual screening for lead discovery

The identification of small drug-like compounds that selectively inhibit the function of biological targets has historically been a major focus in the pharmaceutical industry, and in recent years, has generated much interest in academia as well. Drug-like compounds are valuable as chemical genetics tools to probe biological pathways in a reversible, dose- and time-dependent manner for drug target identification. In addition, small molecule compounds can be used to characterize the shape and charge preferences of macromolecular binding sites, for both structure-based and ligand-based drug design. High-throughput screening is the most common experimental method used to identify lead compounds. Because of the cost, time, and resources required for performing high-throughput screening for compound libraries, the use of alternative strategies is necessary for facilitating lead discovery. Virtual screening has been successful in prioritizing large chemical libraries to identify experimentally active compounds, serving as a practical and effective alternative to high-throughput screening. Methodologies used in virtual screening such as molecular docking and scoring have advanced to the point where they can rapidly and accurately identify lead compounds in addition to predicting native binding conformations. This chapter provides instructions on how to perform a virtual screen using freely available tools for structure-based lead discovery.

Inhibition of Eimeria tenella CDK-related kinase 2: From target identification to lead compounds

Apicomplexan parasites encompass several human- and animal-pathogenic protozoans such as Plasmodium falciparum, Toxoplasma gondii, and Eimeria tenella. E. tenella causes coccidiosis, a disease that afflicts chickens, leading to tremendous economic losses to the global poultry industry. The considerable increase in drug resistance makes it necessary to develop new therapeutic strategies against this parasite. Cyclin-dependent kinases (CDKs) are key molecules in cell-cycle regulation and are therefore prominent target proteins in parasitic diseases. Bioinformatics analysis revealed four potential CDK-like proteins, of which one-E. tenella CDK-related kinase 2 (EtCRK2)-has already been characterized by gene cloning and expression.1 By using the CDK-specific inhibitor flavopiridol in EtCRK2 enzyme assays and schizont maturation assays (SMA), we could chemically validate CDK-like proteins as potential drug targets. An X-ray crystal structure of human CDK2 (HsCDK2) served as a template to build protein models of EtCRK2 by comparative homology modeling. Structural differences in the ATP binding site between EtCRK2 and HsCDK2, as well as chicken CDK3, were addressed for the optimization of selective ATP-competitive inhibitors. Virtual screening and "wet-bench" high-throughput screening campaigns on large compound libraries resulted in an initial set of hit compounds. These compounds were further analyzed and characterized, leading to a set of four promising lead compounds for development as EtCRK2 inhibitors.

Tautomerism in large databases

We have used the Chemical Structure DataBase (CSDB) of the NCI CADD Group, an aggregated collection of over 150 small-molecule databases totaling 103.5 million structure records, to conduct tautomerism analyses on one of the largest currently existing sets of real (i.e. not computer-generated) compounds. This analysis was carried out using calculable chemical structure identifiers developed by the NCI CADD Group, based on hash codes available in the chemoinformatics toolkit CACTVS and a newly developed scoring scheme to define a canonical tautomer for any encountered structure. CACTVS's tautomerism definition, a set of 21 transform rules expressed in SMIRKS line notation, was used, which takes a comprehensive stance as to the possible types of tautomeric interconversion included. Tautomerism was found to be possible for more than 2/3 of the unique structures in the CSDB. A total of 680 million tautomers were calculated from, and including, the original structure records. Tautomerism overlap within the same individual database (i.e. at least one other entry was present that was really only a different tautomeric representation of the same compound) was found at an average rate of 0.3% of the original structure records, with values as high as nearly 2% for some of the databases in CSDB. Projected onto the set of unique structures (by FICuS identifier), this still occurred in about 1.5% of the cases. Tautomeric overlap across all constituent databases in CSDB was found for nearly 10% of the records in the collection.

Accelerated similarity searching and clustering of large compound sets by geometric embedding and locality sensitive hashing

Motivation: Similarity searching and clustering of chemical compounds by structural similarities are important computational approaches for identifying drug-like small molecules. Most algorithms available for these tasks are limited by their speed and scalability, and cannot handle today's large compound databases with several million entries.

Results: In this article, we introduce a new algorithm for accelerated similarity searching and clustering of very large compound sets using embedding and indexing (EI) techniques. First, we present EI-Search as a general purpose similarity search method for finding objects with similar features in large databases and apply it here to searching and clustering of large compound sets. The method embeds the compounds in a high-dimensional Euclidean space and searches this space using an efficient index-aware nearest neighbor search method based on locality sensitive hashing (LSH). Second, to cluster large compound sets, we introduce the EI-Clustering algorithm that combines the EI-Search method with Jarvis–Patrick clustering. Both methods were tested on three large datasets with sizes ranging from about 260 000 to over 19 million compounds. In comparison to sequential search methods, the EI-Search method was 40–200 times faster, while maintaining comparable recall rates. The EI-Clustering method allowed us to significantly reduce the CPU time required to cluster these large compound libraries from several months to only a few days.

Availability: Software implementations and online services have been developed based on the methods introduced in this study. The online services provide access to the generated clustering results and ultra-fast similarity searching of the PubChem Compound database with subsecond response time.

Contact:thomas.girke@ucr.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Inhibitors of the mitochondrial citrate transport protein: validation of the role of substrate binding residues and discovery of the first purely competitive inhibitor

The mitochondrial citrate transport protein (CTP) is critical to energy metabolism in eukaryotic cells. We demonstrate that 1,2,3-benzenetricarboxylate (BTC), the classic and defining inhibitor of the mitochondrial CTP, is a mixed inhibitor of the reconstituted Cys-less CTP, with a strong competitive component [i.e., a competitive inhibition constant (K(ic)) of 0.12 +/- 0.02 mM and an uncompetitive inhibition constant (K(iu)) of 3.04 +/- 0.74 mM]. Based on docking calculations, a model for BTC binding has been developed. We then determined the K(ic) values for each of the eight substrate binding site cysteine substitution mutants and observed increases of 62- to 261-fold relative to the Cys-less control, thereby substantiating the importance of each of these residues in BTC binding. It is noteworthy that we observed parallel increases in the K(m) for citrate transport with each of these binding site mutants, thereby confirming that with these CTP variants, K(m) approximates the K(d) (for citrate) and is therefore a measure of substrate affinity. To further substantiate the importance of these binding site residues, in silico screening of a database of commercially available compounds has led to discovery of the first purely competitive inhibitor of the CTP. Docking calculations indicate that this inhibitor spans and binds to both substrate sites simultaneously. Finally, we propose a kinetic model for citrate transport in which the citrate molecule sequentially binds to the external and internal binding sites (per CTP monomer) before transport.

Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For this purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems.

wwLigCSRre: a 3D ligand-based server for hit identification and optimization

The wwLigCSRre web server performs ligand-based screening using a 3D molecular similarity engine. Its aim is to provide an online versatile facility to assist the exploration of the chemical similarity of families of compounds, or to propose some scaffold hopping from a query compound. The service allows the user to screen several chemically diversified focused banks, such as Kinase-, CNS-, GPCR-, Ion-channel-, Antibacterial-, Anticancer- and Analgesic-focused libraries. The server also provides the possibility to screen the DrugBank and DSSTOX/Carcinogenic compounds databases. User banks can also been downloaded. The 3D similarity search combines both geometrical (3D) and physicochemical information. Starting from one 3D ligand molecule as query, the screening of such databases can lead to unraveled compound scaffold as hits or help to optimize previously identified hit molecules in a SAR (Structure activity relationship) project. wwLigCSRre can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/wwLigCSRre.html.

Using surface tension data to predict differences in surface and bulk concentrations of nonelectrolytes in water

Recently, we developed a quantitative interpretation of surface tension increments (STI) of salts, acids, and bases in terms of the solute (or salt ion) partitioning model (SPM). Here, we obtain an analogous SPM-based interpretation of surface tension increments of nonelectrolytes, which yields local-bulk partition coefficients (K(p)) quantifying the accumulation or exclusion of these solutes in the local region near the air-water surface, and the amount of water per unit area of that region (b1σ). Sucrose exhibits the largest positive STI (approximately 1.4 ergs cm(-2) Osm(-1)). Assuming that K(p) = 0 for sucrose (i.e. that it is completely excluded from the surface of water), these STI provide a minimum estimate of b1σ of 0.20 H(2)O/Å(2), or a minimum thickness of the surface region of approximately two layers of water at bulk density. This is the same value as obtained previously from analysis of surface tension and hydrocarbon solubility increments of Na(2)SO(4) and also for the interaction of glycine betaine with anionic carboxylate surface, indicating that this quantity is not a function of the type of solute or surface investigated and therefore that it may represent the molecular thickness of the region. Partition coefficients of other nonelectrolytes investigated range from moderately excluded (e.g urea) to moderately accumulated (e.g. glycerol, ethylene glycol); strongly accumulated surface active solutes (e.g. mono-substituted alcohols) were not included in this analysis. Partition coefficients for many salt ions obtained from STI and hydrocarbon solubility increments fall in a rank order which corresponds to the Hofmeister series for protein folding and protein solubility, indicating a common pattern of accumulation or exclusion of salt ions at the air-water surface and nonpolar surfaces of dissolved hydrocarbons and proteins; no such patterns are observed for nonelectrolytes.