Computer-aided identification of Trypanosoma brucei uridine diphosphate galactose 4'-epimerase inhibitors: toward the development of novel therapies for African sleeping sickness

In Silico Investigation of Novel Compounds as Inhibitors of Acetylcholinesterase Enzyme for the Treatment of Alzheimer's Diseases

Alzheimer's disease (AD) is a "progressive, neurodegenerative disease that occurs when nerve cells in the brain die." There are only 4 drugs approved by the United States Food and Drug Administration (FDA). Three (donepezil, rivastigmine, and galantamine) out of these four drugs are anticholinesterase inhibitors, while the fourth one memantine is an N-methyl-D-aspartate (NMDA) receptor inhibitor. Currently, two immunotherapy drugs that target amyloid protein (donanemab and lecanemab) are being considered for the treatment of Alzheimer's disease at an early stage. All these drug molecules are still not the complete answer to the treatment of Alzheimer's disease. A recent report from the Office of National Statistics showed that AD is the leading cause of death in 2022. Therefore, there is an urgency to develop more drugs that can treat AD. Based on this urgency, we aim to investigate how bioactive and already approved drugs could be repurposed for inhibiting the anticholinesterase enzyme using computational studies. To achieve this, the data science tool-Python coding was compiled on Jupyter Notebook to mine bioactive compounds from the ChEMBL database. The most bioactive compounds obtained were further investigated using Molecular Operating Environment (MOE) software to carry out molecular docking and ligand analysis, and this was followed by molecular dynamics simulation production at 35 ns using GROMACS 2022.4 on Archer 2 machine. The molecular dynamic analysis was carried out using HeroMDanalysis software. Data mining of the ChEMBL database was carried out for lipase inhibitors, and this gave CHEMBL-ID 1240685, a peptide molecule, the most active compound at the time of data mining. Further literature studies gave Zoladex an FDA-approved drug for the treatment of breast cancer as another compound of interest. The in silico studies were carried out against the anticholinesterase enzyme using two FDA-approved drugs donepezil and galantamine as a template for comparing the in silico activities of the repurposed drugs. A very useful receptor for this study was PDB-1DX6, a cocrystallized galantamine inhibitor of acetylcholinesterase enzyme. The molecular docking analysis (using ligand interactions) and molecular dynamic analysis (root mean square deviation (RMSD) and root mean square fluctuation (RMSF)) showed that the two peptide molecules CHEMBL-1240685 and Zoladex gave the best binding energy and stability when compared to the FDA-approved drugs (donepezil and galantamine). Finally, further literature studies revealed that Zoladex affects memory reduction; therefore, it was dropped as a possible repurposed drug. Our research showed that CHEMBL-1240685 is a potential compound that could be investigated for the inhibition of anticholinesterase enzyme and might be another drug molecule that could be used to treat Alzheimer's disease.

Toll-like receptor-guided therapeutic intervention of human cancers: molecular and immunological perspectives

Toll-like receptors (TLRs) serve as the body's first line of defense, recognizing both pathogen-expressed molecules and host-derived molecules released from damaged or dying cells. The wide distribution of different cell types, ranging from epithelial to immune cells, highlights the crucial roles of TLRs in linking innate and adaptive immunity. Upon stimulation, TLRs binding mediates the expression of several adapter proteins and downstream kinases, that lead to the induction of several other signaling molecules such as key pro-inflammatory mediators. Indeed, extraordinary progress in immunobiological research has suggested that TLRs could represent promising targets for the therapeutic intervention of inflammation-associated diseases, autoimmune diseases, microbial infections as well as human cancers. So far, for the prevention and possible treatment of inflammatory diseases, various TLR antagonists/inhibitors have shown to be efficacious at several stages from pre-clinical evaluation to clinical trials. Therefore, the fascinating role of TLRs in modulating the human immune responses at innate as well as adaptive levels directed the scientists to opt for these immune sensor proteins as suitable targets for developing chemotherapeutics and immunotherapeutics against cancer. Hitherto, several TLR-targeting small molecules (e.g., Pam3CSK4, Poly (I:C), Poly (A:U)), chemical compounds, phytocompounds (e.g., Curcumin), peptides, and antibodies have been found to confer protection against several types of cancers. However, administration of inappropriate doses of such TLR-modulating therapeutics or a wrong infusion administration is reported to induce detrimental outcomes. This review summarizes the current findings on the molecular and structural biology of TLRs and gives an overview of the potency and promises of TLR-directed therapeutic strategies against cancers by discussing the findings from established and pipeline discoveries.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Targeted protein degradation might present a novel therapeutic approach in the fight against African trypanosomiasis

African trypanosomiasis (AT) is a hemoparasitic disease caused by infection with African trypanosomes and it is prevalent in many sub-Saharan African countries, affecting both humans and domestic animals. The disease is transmitted mostly by haematophagous insects of the genus Glossina while taking blood meal, in the process spreading the parasites from an infected animal to an uninfected animal. The disease is fatal if untreated, and the available drugs are generally ineffective and resulting in toxicities. Therefore, it is still pertinent to explore novel methods and targets for drug discovery. Proteolysis-targeting chimeras (PROTACs) present a new strategy for development of therapeutic molecules that mimic cellular proteasomal-mediated protein degradation to target proteins involved in different disease types. PROTACs have been used to degrade proteins involved in various cancers, neurodegenerative diseases, and immune disorders with remarkable success. Here, we highlight the problems associated with the current treatments for AT, discuss the concept of PROTACs and associated targeted protein degradation (TPD) approaches, and provide some insights on the future potential for the use of these emerging technologies (PROTACs and TPD) for the development of new generation of anti-Trypanosoma drugs and the first "TrypPROTACs".

Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8

TLR7 and TLR8 are key members of the Toll-like receptor family, playing crucial roles in the signaling pathways of innate immunity, and thus become attractive therapeutic targets of many diseases including infections and cancer. Although TLR7 and TLR8 show a high degree of sequence homology, their biological response to small molecule binding is very different. Aiming to understand the mechanism of selective profiles of small molecule modulators against TLR7 and TLR8, we carried out molecular dynamic simulations on three imidazoquinoline derivatives bound to the receptors separately. They are Resiquimod (R), Hybrid-2 (H), and Gardiquimod (G), selective agonists of TLR7 and TLR8. Our MD trajectories indicated that in the complex of TLR7-R and TLR7-G, the two chains forming the TLR7 dimer tended to remain “open” conformation, while the rest systems maintained in the closed format. The agonists R, H, and G developed conformational deviation mainly on the aliphatic tail. Furthermore, we attempted to quantify the selectivity between TLR7 and TLR8 by binding free energies via MM-GBSA method. It showed that the three selected modulators were more favorable for TLR7 than TLR8, and the ranking from the strongest to the weakest was H, R and G, aligning well with experimental data. In the TLR7, the flexible and hydrophobic aliphatic side chain of H has stronger van der Waals interactions with V381 and F351 but only pick up interaction with one amino acid residue i.e. Y353 of TLR8. Unsurprisingly, the positively charged side chain of G has less favorable interaction with I585 of TLR7 and V573 of TLR8 explaining G is weak agonist of both TLR7 and TLR8. All three imidazoquinoline derivatives can form stable hydrogen bonds with D555 of TLR7 and the corresponding D543 of TLR8. In brief, the set of total 400ns MD studies sheds light on the potential selectivity mechanisms of agonists towards TLR7 and TLR8, indicating the van der Waals interaction as the driving force for the agonists binding, thus provides us insights for designing more potent and selective modulators to cooperate with the hydrophobic nature of the binding pocket.

Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations

The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.

An Overview on Target-Based Drug Design against Kinetoplastid Protozoan Infections: Human African Trypanosomiasis, Chagas Disease and Leishmaniases

The protozoan diseases Human African Trypanosomiasis (HAT), Chagas disease (CD), and leishmaniases span worldwide and therefore their impact is a universal concern. The present regimen against kinetoplastid protozoan infections is poor and insufficient. Target-based design expands the horizon of drug design and development and offers novel chemical entities and potential drug candidates to the therapeutic arsenal against the aforementioned neglected diseases. In this review, we report the most promising targets of the main kinetoplastid parasites, as well as their corresponding inhibitors. This overview is part of the Special Issue, entitled "Advances of Medicinal Chemistry against Kinetoplastid Protozoa (Trypanosoma brucei, Trypanosoma cruzi and Leishmania spp.) Infections: Drug Design, Synthesis and Pharmacology".

Design, synthesis and antitrypanosomal activity of heteroaryl-based 1,2,4-triazole and 1,3,4-oxadiazole derivatives

Two series of novel 1,2,4-triazol-3-yl-thioacetamide 3a-b and 4a-b and 5-pyrazin-2-yl-3H-[1,3,4]oxadiazole-2-thiones 9a-h were designed and synthesized. The compounds prepared have been identified using ¹H NMR, ¹³C NMR and elemental analyses. The synthesized compounds 3a, 3b, 4a, 4b, 9a, 9b, 9d-e and 9f have been evaluated with α-difluoromethylornithine (DFMO) as a control drug for their in vitro antitrypanosomal activity against Trypanosoma brucei. Results showed that 3b was the most active compound in general and also more potent than control DFMO. 3b was 8 folds more potent than the reference with IC₅₀ of 0.79 μM and IC₉₀ of 1.35 μM, respectively compared to DFMO (IC₅₀ = 6.10 μM and IC₉₀ of 8.66 μM). The tested compounds showed moderate cytotoxicity with selectivity indices ranging from 12 (9d) to 102 (3b) against L6 cells. Docking study was performed into ten of T. brucei enzymes which have been identified as potential/valid targets for most of the antitrypanosomal agents. The results of the docking study revealed high binding scores toward many of the selected enzymes. A good correlation was observed only between log (IC₅₀) of antitrypanosomal activity of the new compounds and their calculated Ki values against TryR enzyme (R² = 0.726). Compound 3b, the most active as antitrypanosomal agents exhibited similar binding orientation and interaction to those of WP6 against TryR enzyme. However, in a next round of work, a complementary studies will be carried out to clarify the mechanism of action of these compounds.

Prospects of multitarget drug designing strategies by linking molecular docking and molecular dynamics to explore the protein-ligand recognition process

The designing of drugs that can simultaneously affect different protein targets is one novel and promising way to treat complex diseases. Multitarget drugs act on multiple protein receptors each implicated in the same disease state, and may be considered to be more beneficial than conventional drug therapies. For example, these drugs can have improved therapeutic potency due to synergistic effects on multiple targets, as well as improved safety and resistance profiles due to the combined regulation of potential primary therapeutic targets and compensatory elements and lower dosage typically required. This review analyzes in-silico methods that facilitate multitarget drug design that facilitate the discovery and development of novel therapeutic agents. Here presented is a summary of the progress in structure-based drug discovery techniques that study the process of molecular recognition of targets and ligands, moving from static molecular docking to improved molecular dynamics approaches in multitarget drug design, and the advantages and limitations of each.

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

Essential multimeric enzymes in kinetoplastid parasites: A host of potentially druggable protein-protein interactions

Parasitic diseases caused by kinetoplastid parasites of the genera Trypanosoma and Leishmania are an urgent public health crisis in the developing world. These closely related species possess a number of multimeric enzymes in highly conserved pathways involved in vital functions, such as redox homeostasis and nucleotide synthesis. Computational alanine scanning of these protein-protein interfaces has revealed a host of potentially ligandable sites on several established and emerging anti-parasitic drug targets. Analysis of interfaces with multiple clustered hotspots has suggested several potentially inhibitable protein-protein interactions that may have been overlooked by previous large-scale analyses focusing solely on secondary structure. These protein-protein interactions provide a promising lead for the development of new peptide and macrocycle inhibitors of these enzymes.

UDP-galactose 4'-epimerase from the liver fluke, Fasciola hepatica: biochemical characterization of the enzyme and identification of inhibitors

Leloir pathway enzyme uridine diphosphate (UDP)-galactose 4'-epimerase from the common liver fluke Fasciola hepatica (FhGALE) was identified and characterized. The enzyme can be expressed in, and purified from, Escherichia coli. The recombinant enzyme is active: the K(m) (470 μM) is higher than the corresponding human enzyme (HsGALE), whereas the k(cat) (2.3 s(-1)) is substantially lower. FhGALE binds NAD(+) and has shown to be dimeric by analytical gel filtration. Like the human and yeast GALEs, FhGALE is stabilized by the substrate UDP-galactose. Molecular modelling predicted that FhGALE adopts a similar overall fold to HsGALE and that tyrosine 155 is likely to be the catalytically critical residue in the active site. In silico screening of the National Cancer Institute Developmental Therapeutics Program library identified 40 potential inhibitors of FhGALE which were tested in vitro. Of these, 6 showed concentration-dependent inhibition of FhGALE, some with nanomolar IC50 values. Two inhibitors (5-fluoroorotate and N-[(benzyloxy)carbonyl]leucyltryptophan) demonstrated selectivity for FhGALE over HsGALE. These compounds also thermally destabilized FhGALE in a concentration-dependent manner. Interestingly, the selectivity of 5-fluoroorotate was not shown by orotic acid, which differs in structure by 1 fluorine atom. These results demonstrate that, despite the structural and biochemical similarities of FhGALE and HsGALE, it is possible to discover compounds which preferentially inhibit FhGALE.

High throughput screen identifies small molecule inhibitors specific for Mycobacterium tuberculosis phosphoserine phosphatase

The emergence of drug-resistant strains of Mycobacterium tuberculosis makes identification and validation of newer drug targets a global priority. Phosphoserine phosphatase (PSP), a key essential metabolic enzyme involved in conversion of O-phospho-l-serine to l-serine, was characterized in this study. The M. tuberculosis genome harbors all enzymes involved in l-serine biosynthesis including two PSP homologs: Rv0505c (SerB1) and Rv3042c (SerB2). In the present study, we have biochemically characterized SerB2 enzyme and developed malachite green-based high throughput assay system to identify SerB2 inhibitors. We have identified 10 compounds that were structurally different from known PSP inhibitors, and few of these scaffolds were highly specific in their ability to inhibit SerB2 enzyme, were noncytotoxic against mammalian cell lines, and inhibited M. tuberculosis growth in vitro. Surface plasmon resonance experiments demonstrated the relative binding for these inhibitors. The two best hits identified in our screen, clorobiocin and rosaniline, were bactericidal in activity and killed intracellular bacteria in a dose-dependent manner. We have also identified amino acid residues critical for these SerB2-small molecule interactions. This is the first study where we validate that M. tuberculosis SerB2 is a druggable and suitable target to pursue for further high throughput assay system screening.

Discovery of Staphylococcus aureus sortase A inhibitors using virtual screening and the relaxed complex scheme

Staphylococcus aureus is the leading cause of hospital-acquired infections in the United States. The emergence of multidrug-resistant strains of S. aureus has created an urgent need for new antibiotics. Staphylococcus aureus uses the sortase A enzyme to display surface virulence factors suggesting that compounds that inhibit its activity will function as potent anti-infective agents. Here, we report the identification of several inhibitors of sortase A using virtual screening methods that employ the relaxed complex scheme, an advanced computer-docking methodology that accounts for protein receptor flexibility. Experimental testing validates that several compounds identified in the screen inhibit the activity of sortase A. A lead compound based on the 2-phenyl-2,3-dihydro-1H-perimidine scaffold is particularly promising, and its binding mechanism was further investigated using molecular dynamics simulations and conducting preliminary structure-activity relationship studies.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Exploring the role of receptor flexibility in structure-based drug discovery

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads.

Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design

Protein flexibility plays a major role in biomolecular recognition. In many cases, it is not obvious how molecular structure will change upon association with other molecules. In proteins, these changes can be major, with large deviations in overall backbone structure, or they can be more subtle as in a side-chain rotation. Either way the algorithms that predict the favorability of biomolecular association require relatively accurate predictions of the bound structure to give an accurate assessment of the energy involved in association. Here, we review a number of techniques that have been proposed to accommodate receptor flexibility in the simulation of small molecules binding to protein receptors. We investigate modifications to standard rigid receptor docking algorithms and also explore enhanced sampling techniques, and the combination of free energy calculations and enhanced sampling techniques. The understanding and allowance for receptor flexibility are helping to make computer simulations of ligand protein binding more accurate. These developments may help improve the efficiency of drug discovery and development. Efficiency will be essential as we begin to see personalized medicine tailored to individual patients, which means specific drugs are needed for each patient's genetic makeup.

In silico prediction of the effects of mutations in the human UDP-galactose 4'-epimerase gene: towards a predictive framework for type III galactosemia

The enzyme UDP-galactose 4'-epimerase (GALE) catalyses the reversible epimerisation of both UDP-galactose and UDP-N-acetyl-galactosamine. Deficiency of the human enzyme (hGALE) is associated with type III galactosemia. The majority of known mutations in hGALE are missense and private thus making clinical guidance difficult. In this study a bioinformatics approach was employed to analyse the structural effects due to each mutation using both the UDP-glucose and UDP-N-acetylglucosamine bound structures of the wild-type protein. Changes to the enzyme's overall stability, substrate/cofactor binding and propensity to aggregate were also predicted. These predictions were found to be in good agreement with previous in vitro and in vivo studies when data was available and allowed for the differentiation of those mutants that severely impair the enzyme's activity against UDP-galactose. Next this combination of techniques were applied to another twenty-six reported variants from the NCBI dbSNP database that have yet to be studied to predict their effects. This identified p.I14T, p.R184H and p.G302R as likely severely impairing mutations. Although severely impaired mutants were predicted to decrease the protein's stability, overall predicted stability changes only weakly correlated with residual activity against UDP-galactose. This suggests other protein functions such as changes in cofactor and substrate binding may also contribute to the mechanism of impairment. Finally this investigation shows that this combination of different in silico approaches is useful in predicting the effects of mutations and that it could be the basis of an initial prediction of likely clinical severity when new hGALE mutants are discovered.

Virtual screening and biological evaluation of inhibitors targeting the XPA-ERCC1 interaction

BACKGROUND

Nucleotide excision repair (NER) removes many types of DNA lesions including those induced by UV radiation and platinum-based therapy. Resistance to platinum-based therapy correlates with high expression of ERCC1, a major element of the NER machinery. The interaction between ERCC1 and XPA is essential for a successful NER function. Therefore, one way to regulate NER is by inhibiting the activity of ERCC1 and XPA.

METHODOLOGY/PRINCIPAL FINDINGS

Here we continued our earlier efforts aimed at the identification and characterization of novel inhibitors of the ERCC1-XPA interaction. We used a refined virtual screening approach combined with a biochemical and biological evaluation of the compounds for their ability to interact with ERCC1 and to sensitize cells to UV radiation. Our findings reveal a new validated ERCC1-XPA inhibitor that significantly sensitized colon cancer cells to UV radiation indicating a strong inhibition of the ERCC1-XPA interaction.

CONCLUSIONS

NER is a major factor in acquiring resistance to platinum-based therapy. Regulating the NER pathway has the potential of improving the efficacy of platinum treatments. One approach that we followed is to inhibit the essential interaction between the two NER elements, ERCC1 and XPA. Here, we performed virtual screening against the ERCC1-XPA interaction and identified novel inhibitors that block the XPA-ERCC1 binding. The identified inhibitors significantly sensitized colon cancer cells to UV radiation indicating a strong inhibition of the ERCC1-XPA interaction.

Chemical proteomic analysis reveals the drugability of the kinome of Trypanosoma brucei

The protozoan parasite Trypanosoma brucei is the causative agent of African sleeping sickness, and there is an urgent unmet need for improved treatments. Parasite protein kinases are attractive drug targets, provided that the host and parasite kinomes are sufficiently divergent to allow specific inhibition to be achieved. Current drug discovery efforts are hampered by the fact that comprehensive assay panels for parasite targets have not yet been developed. Here, we employ a kinase-focused chemoproteomics strategy that enables the simultaneous profiling of kinase inhibitor potencies against more than 50 endogenously expressed T. brucei kinases in parasite cell extracts. The data reveal that T. brucei kinases are sensitive to typical kinase inhibitors with nanomolar potency and demonstrate the potential for the development of species-specific inhibitors.

The molecular dynamics of Trypanosoma brucei UDP-galactose 4'-epimerase: a drug target for African sleeping sickness

During the past century, several epidemics of human African trypanosomiasis, a deadly disease caused by the protist Trypanosoma brucei, have afflicted sub-Saharan Africa. Over 10 000 new victims are reported each year, with hundreds of thousands more at risk. As current drug treatments are either highly toxic or ineffective, novel trypanocides are urgently needed. The T. brucei galactose synthesis pathway is one potential therapeutic target. Although galactose is essential for T. brucei survival, the parasite lacks the transporters required to intake galactose from the environment. UDP-galactose 4'-epimerase (TbGalE) is responsible for the epimerization of UDP-glucose to UDP-galactose and is therefore of great interest to medicinal chemists. Using molecular dynamics simulations, we investigate the atomistic motions of TbGalE in both the apo and holo states. The sampled conformations and protein dynamics depend not only on the presence of a UDP-sugar ligand, but also on the chirality of the UDP-sugar C4 atom. This dependence provides important insights into TbGalE function and may help guide future computer-aided drug discovery efforts targeting this protein.

LigMerge: a fast algorithm to generate models of novel potential ligands from sets of known binders

One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically ‘swapping’ the chemical moieties of known ligands to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called ‘LigMerge’. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator–activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We hope that LigMerge will be a helpful tool for the drug design community.

Novel cruzain inhibitors for the treatment of Chagas' disease

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas’ disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas’ disease.

Altered cofactor binding affects stability and activity of human UDP-galactose 4'-epimerase: implications for type III galactosemia

Deficiency of UDP-galactose 4'-epimerase is implicated in type III galactosemia. Two variants, p.K161N-hGALE and p.D175N-hGALE, have been previously found in combination with other alleles in patients with a mild form of the disease. Both variants were studied in vivo and in vitro and showed different levels of impairment. p.K161N-hGALE was severely impaired with substantially reduced enzymatic activity, increased thermal stability, reduced cofactor binding and no ability to rescue the galactose-sensitivity of gal10-null yeast. Interestingly p.K161N-hGALE showed less impairment of activity with UDP-N-acetylgalactosamine in comparison to UDP-galactose. Differential scanning fluorimetry revealed that p.K161N-hGALE was more stable than the wild-type protein and only changed stability in the presence of UDP-N-acetylglucosamine and NAD(+). p.D175N-hGALE essentially rescued the galactose-sensitivity of gal10-null yeast, was less stable than the wild-type protein but showed increased stability in the presence of substrates and cofactor. We postulate that p.K161N-hGALE causes its effects by abolishing an important interaction between the protein and the cofactor, whereas p.D175N-hGALE is predicted to remove a stabilizing salt bridge between the ends of two α-helices that contain residues that interact with NAD(+). These results suggest that the cofactor binding is dynamic and that its loss results in significant structural changes that may be important in disease causation.

AutoClickChem: click chemistry in silico

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.

In silico identification of viper phospholipaseA2 inhibitors: validation by in vitro, in vivo studies

Snake venom, particularly of vipers from the Indian subcontinent, contains Phospholipase A2 (PLA2) as one its constituents which is widely implicated in hemorrhagic, cardiac arrest and death. Development of inhibitors of the protein can facilitate the weakening or annihilation of the venom toxicity and save many human lives. In the present communication, our studies relate to the design and development of structure-based ligands as inhibitors of PLA2 of Viper venom. The study involves the computational approach towards evaluating a library of molecules comprising of natural products, and synthetic molecules through docking studies on the venom protein PDB ID: 1OXL (a dimer, available in the literature). In silico experiments have resulted in the identification of several of them as PLA2 inhibitors. The inhibitory effect of PLA2 by these compounds is attributed to a great extent to their interaction with the residues Phe 46 and Val47 of chain B of the target protein and hence these two residues are identified as the key contributor for the said activity. In order to validate the in silico findings, a selected panel of compounds have been tested by in vitro and in vivo experiments against the venom, which has led to the observance of significant corroboration between the wet lab and in silico findings, validating thereby the in silico approach used in the present study.

Molecular recognition in the case of flexible targets

A protein's flexibility is well recognized to underlie its capacity to engage in critical functions, such as signal transduction, biomolecular transport and biochemical reactivity. Molecular recognition is also tightly linked to the dynamics of the binding partners, yet protein flexibility has largely been ignored by the growing field of structure-based drug design (SBDD). In combination with experimentally determined structures, a number of computational methods have been proposed to model protein movements, which may be important for small molecule binding. Such techniques have the ability to expose new binding site conformations, which may in turn recognize and lead to the discovery of more potent and selective drugs through molecular docking. In this article, we discuss various methods and focus on the Relaxed Complex Scheme (RCS), which uses Molecular Dynamics (MD) simulations to model full protein flexibility and enhance virtual screening programmes. We review practical applications of the RCS and use a recent study of the HIV-1 reverse transcriptase to illustrate the various phases of the scheme. We also discuss some encouraging developments, aimed at addressing current weaknesses of the RCS.

The role of long-range intermolecular interactions in discovery of new drugs

INTRODUCTION

Long-range intermolecular interactions (interactions at distances between 100 and 1000 Å) play an important role in the interaction between drugs and therapeutic targets, and design techniques based on this concept could significantly improve and accelerate new drug discovery. Understanding these long-range intermolecular interactions will also help further our understanding of the molecular mechanisms and the underlying basic biological processes.

AREAS COVERED

This article looks at the physical bases of long-range intermolecular interactions in biological systems with a brief review of the literature data to support this concept. The article also gives some examples of techniques used in drug discovery that were based on the long-range intermolecular interaction concept.

EXPERT OPINION

The electron-ion interaction potential (EIIP) and average quasivalence number (AQVN) concepts shed new light on the role of long-range intermolecular interactions in biological systems. Further research of physicochemical mechanisms underlying long-range interactions between biological molecules is necessary for a better understanding of the basic biological processes. The addition of the computer-aided design techniques based on the EIIP/AQVN concept to the research and development will lead not only to a significant reduction in cost but also to an acceleration in the development of new drugs.

NNScore 2.0: a neural-network receptor-ligand scoring function

NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pK(d). To facilitate use, NNScore 2.0 has been implemented as an open-source python script. A copy can be obtained from http://www.nbcr.net/software/nnscore/ .

Molecular dynamics simulations and drug discovery

This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.

Non-bisphosphonate inhibitors of isoprenoid biosynthesis identified via computer-aided drug design

The relaxed complex scheme, a virtual-screening methodology that accounts for protein receptor flexibility, was used to identify a low-micromolar, non-bisphosphonate inhibitor of farnesyl diphosphate synthase. Serendipitously, we also found that several predicted farnesyl diphosphate synthase inhibitors were low-micromolar inhibitors of undecaprenyl diphosphate synthase. These results are of interest because farnesyl diphosphate synthase inhibitors are being pursued as both anti-infective and anticancer agents, and undecaprenyl diphosphate synthase inhibitors are antibacterial drug leads.

Pyrone-based inhibitors of metalloproteinase types 2 and 3 may work as conformation-selective inhibitors

Matrix metalloproteinases are zinc-containing enzymes capable of degrading all components of the extracellular matrix. Owing to their role in human disease, matrix metalloproteinase have been the subject of extensive study. A bioinorganic approach was recently used to identify novel inhibitors based on a maltol zinc-binding group, but accompanying molecular-docking studies failed to explain why one of these inhibitors, AM-6, had approximately 2500-fold selectivity for MMP-3 over MMP-2. A number of studies have suggested that the matrix-metalloproteinase active site is highly flexible, leading some to speculate that differences in active-site flexibility may explain inhibitor selectivity. To extend the bioinorganic approach in a way that accounts for MMP-2 and MMP-3 dynamics, we here investigate the predicted binding modes and energies of AM-6 docked into multiple structures extracted from matrix-metalloproteinase molecular dynamics simulations. Our findings suggest that accounting for protein dynamics is essential for the accurate prediction of binding affinity and selectivity. Additionally, AM-6 and other similar inhibitors likely select for and stabilize only a subpopulation of all matrix-metalloproteinase conformations sampled by the apo protein. Consequently, when attempting to predict ligand affinity and selectivity using an ensemble of protein structures, it may be wise to disregard protein conformations that cannot accommodate the ligand.

Biosynthesis of galactofuranose in kinetoplastids: novel therapeutic targets for treating leishmaniasis and chagas' disease

Cell surface proteins of parasites play a role in pathogenesis by modulating mammalian cell recognition and cell adhesion during infection. β-Galactofuranose (Galf) is an important component of glycoproteins and glycolipids found on the cell surface of Leishmania spp. and Trypanosoma cruzi. β-Galf-containing glycans have been shown to be important in parasite-cell interaction and protection against oxidative stress. Here, we discuss the role of β-Galf in pathogenesis and recent studies on the Galf-biosynthetic enzymes: UDP-galactose 4′ epimerase (GalE), UDP-galactopyranose mutase (UGM), and UDP-galactofuranosyl transferase (GalfT). The central role in Galf formation, its unique chemical mechanism, and the absence of a homologous enzyme in humans identify UGM as the most attractive drug target in the β-Galf-biosynthetic pathway in protozoan parasites.

NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein−ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.

The first C-glycosidic analogue of a novel galactosyltransferase inhibitor

Structural analogues and mimics of the natural sugar-nucleotide UDP-galactose (UDP-Gal) are sought after as chemical tools for glycobiology and drug discovery. We have recently developed a novel class of galactosyltransferase (GalT) inhibitors derived from UDP-Gal, bearing an additional substituent at the 5-position of the uracil base. Herein we report the first C-glycosidic derivative of this new class of GalT inhibitors. We describe a practical convergent synthesis of the new UDP-C-Gal derivative, including a systematic study into the use of radical chemistry for the preparation of galactosyl ethylphosphonate, a key synthetic intermediate. The new inhibitor showed activity against a bacterial UDP-Gal 4'-epimerase at micromolar concentrations. This is the first example of a base-modified UDP-sugar as an inhibitor of a UDP-sugar-dependent enzyme which is not a glycosyltransferase, and these results may therefore have implications for the design of inhibitors of these enzymes in the future.

Computer-aided drug-discovery techniques that account for receptor flexibility

Protein flexibility plays a critical role in ligand binding to both orthosteric and allosteric sites. We here review some of the computer-aided drug-design techniques currently used to account for protein flexibility, ranging from methods that probe local receptor flexibility in the region of the protein immediately adjacent to the binding site, to those that account for general flexibility in all protein regions.