Druggability Score Research Articles

Stress-Based Production, and Characterization of Glutathione Peroxidase and Glutathione S-Transferase Enzymes From Lactobacillus plantarum.

More attention has been recently directed toward glutathione peroxidase and s-transferase enzymes because of the great importance they hold with respect to their applications in the pharmaceutical field. This work was conducted to optimize the production and characterize glutathione peroxidase and glutathione s-transferase produced by Lactobacillus plantarum KU720558 using Plackett-Burman and Box-Behnken statistical designs. To assess the impact of the culture conditions on the microbial production of the enzymes, colorimetric methods were used. Following data analysis, the optimum conditions that enhanced the s-transferase yield were the De Man-Rogosa-Sharp (MRS) broth as a basal medium supplemented with 0.1% urea, 0.075% H2O2, 0.5% 1-butanol, 0.0125% amino acids, and 0.05% SDS at pH 6.0 and anaerobically incubated for 24 h at 40°C. The optimum s-transferase specific activity was 1789.5 U/mg of protein, which was ~12 times the activity of the basal medium. For peroxidase, the best medium composition was 0.17% urea, 0.025% bile salt, 7.5% Na Cl, 0.05% H2O2, 0.05% SDS, and 2% ethanol added to the MRS broth at pH 6.0 and anaerobically incubated for 24 h at 40°C. Furthermore, the optimum peroxidase specific activity was 612.5 U/mg of protein, indicating that its activity was 22 times higher than the activity recorded in the basal medium. After SDS-PAGE analysis, GST and GPx showed a single protein band of 25 and 18 kDa, respectively. They were able to retain their activities at an optimal temperature of 40°C for an hour and pH range 4–7. The 3D model of both enzymes was constructed showing helical structures, sheet and loops. Protein cavities were also detected to define druggable sites. GST model had two large pockets; 185Å3 and 71 Å3 with druggability score 0.5–0.8. For GPx, the pockets were relatively smaller, 71 Å3 and 32 Å3 with druggability score (0.65–0.66). Therefore, the present study showed that the consortium components as well as the stress-based conditions used could express both enzymes with enhanced productivity, recommending their application based on the obtained results.

Structure-Based Analysis of Cryptic-Site Opening

Many proteins in their unbound structures have cryptic sites that are not appropriately sized for drug binding. We consider here 32 proteins from the recently published CryptoSite set with validated cryptic sites, and study whether the sites remain cryptic in all available X-ray structures of the proteins solved without any ligand bound near the sites. It was shown that only few of these proteins have binding pockets that never form without ligand binding. Sites that are cryptic in some structures but spontaneously form in others are also rare. In most proteins the forming of pockets is affected by mutations or ligand binding at locations far from the cryptic site. To further explore these mechanisms, we applied adiabatic biased molecular dynamics simulations to guide the proteins from their ligand-free structures to ligand-bound conformations, and studied the distribution of druggability scores of the pockets located at the cryptic sites.

CRISPR Dropout Screens Identify DHODH, PIK3C3, and Crkl as Potential Therapeutic Targets in Acute Myeloid Leukemia

Relapsed disease is still a major challenge in AML despite recently approved targeted therapies. New therapeutic targets are needed. To identify vulnerabilities in AML, screening data from a lentiviral-based genome-wide pooled CRISPR-Cas9 library (DepMap) was analyzed by examining gene knockout (KO) dependency scores in 15 AML cell lines (HEL, MV411, OCIAML2, THP1, NOMO1, EOL1, KASUMI1, NB4, OCIAML3, MOLM13, TF1, U937, F36P, AML193, P31FUJ). 438 gene KOs were identified as essential to AML cell survival in 14/15 AML cell lines. 247/438 (56%) genes had no active compound or approved drug interacting with its gene-products (DGIdb, canSAR Black) but were tractable targets based on 3D modeling or ligand-based druggability scoring (canSAR Black). This refined list of targets made up a new screening platform termed disKO. For clinical relevance, a primary AML specimen was treated with a custom-made lentiviral-based pooled CRISPR-Cas9 library including disKO targets using guide RNA's constructed against a parsed version of the druggable genome. Following transduction, PCR and oligo-sequencing were performed on Days 0, 7, and 14. Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) was used to identify gene KOs associating with AML dropout over time. Four gene KOs from disKO were identified as essential in primary AML: NUP93, DHODH, CRKL, and PIK3C3. NUP93 loss of function mutations have been associated with steroid-resistant nephrotic syndrome in patients, suggesting inhibition may be toxic. DHODH inhibitors are currently being tested in AML. PIK3C3 codes for lipid kinases involved in autophagocytosis, and PIK3C3 inhibitors are in preclinical stage of development. CRKL is a proto-oncogene encoding src homology domains (SH2, SH3), which activate RAS and JNK signaling pathways. Although CRKL is a substrate of BCR-ABL CML, its essentiality in our study was found in AML cell lines and primary AML lacking the BCR-ABL fusion. Differential gene expression analysis showed CRKL to have lower expression in AML when compared to normal hematopoietic cells. Its druggability score based on six 3D structures and 1.3 ligand z-score indicates promising pharmacologic inhibition. In conclusion, a new CRISPR-Cas9 screening method termed disKO led to the identification of three viable targets for AML: PIK3C3, DHODH, and CRKL. Current efforts are focused on target validation followed by compound screening to find inhibitors with therapeutic potential. Disclosures No relevant conflicts of interest to declare.

Glycine-induced formation and druggability score prediction of protein surface pockets.

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.

Druggability of Coronary Artery Disease Risk Loci.

Genome-wide association studies have identified multiple loci associated with coronary artery disease and myocardial infarction, but only a few of these loci are current targets for on-market medications. To identify drugs suitable for repurposing and their targets, we created 2 unique pipelines integrating public data on 49 coronary artery disease/myocardial infarction-genome-wide association studies loci, drug-gene interactions, side effects, and chemical interactions. We first used publicly available genome-wide association studies results on all phenotypes to predict relevant side effects, identified drug-gene interactions, and prioritized candidates for repurposing among existing drugs. Second, we prioritized gene product targets by calculating a druggability score to estimate how accessible pockets of coronary artery disease/myocardial infarction-associated gene products are, then used again the genome-wide association studies results to predict side effects, excluded loci with widespread cross-tissue expression to avoid housekeeping and genes involved in vital processes and accordingly ranked the remaining gene products. These pipelines ultimately led to 3 suggestions for drug repurposing: pentolinium, adenosine triphosphate, and riociguat (to target CHRNB4, ACSS2, and GUCY1A3, respectively); and 3 proteins for drug development: LMOD1 (leiomodin 1), HIP1 (huntingtin-interacting protein 1), and PPP2R3A (protein phosphatase 2, regulatory subunit b-double prime, α). Most current therapies for coronary artery disease/myocardial infarction treatment were also rediscovered. Integration of genomic and pharmacological data may prove beneficial for drug repurposing and development, as evidence from our pipelines suggests.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction.

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

LIN28 Zinc Knuckle Domain Is Required and Sufficient to Induce let-7 Oligouridylation

LIN28 is an RNA binding protein that plays crucial roles in pluripotency, glucose metabolism, tissue regeneration, and tumorigenesis. LIN28 binds to the let-7 primary and precursor microRNAs through bipartite recognition and induces degradation of let-7 precursors (pre-let-7) by promoting oligouridylation by terminal uridylyltransferases (TUTases). Here, we report that the zinc knuckle domain (ZKD)of mouse LIN28 recruits TUT4 to initiate the oligouridylation of let-7 precursors. Our crystal structure of human LIN28 in complex with a fragment of pre-let-7f-1 determined to 2.0Å resolution shows that the interaction between ZKD and RNA is constrained to a small cavity with a high druggability score. We demonstrate that the specific interaction between ZKD and pre-let-7 is necessary and sufficient to induce oligouridylation by recruiting the N-terminal fragment of TUT4 (NTUT4) and the formation of a stable ZKD:NTUT4:pre-let-7 ternary complex is crucial for the acquired processivity of TUT4.

Druggability analysis and classification of protein tyrosine phosphatase active sites.

Protein tyrosine phosphatases (PTP) play important roles in the pathogenesis of many diseases. The fact that no PTP inhibitors have reached the market so far has raised many questions about their druggability. In this study, the active sites of 17 PTPs were characterized and assessed for its ability to bind drug-like molecules. Consequently, PTPs were classified according to their druggability scores into four main categories. Only four members showed intermediate to very druggable pocket; interestingly, the rest of them exhibited poor druggability. Particularly focusing on PTP1B, we also demonstrated the influence of several factors on the druggability of PTP active site. For instance, the open conformation showed better druggability than the closed conformation, while the tight-bound water molecules appeared to have minimal effect on the PTP1B druggability. Finally, the allosteric site of PTP1B was found to exhibit superior druggability compared to the catalytic pocket. This analysis can prove useful in the discovery of new PTP inhibitors by assisting researchers in predicting hit rates from high throughput or virtual screening and saving unnecessary cost, time, and efforts via prioritizing PTP targets according to their predicted druggability.

Target the More Druggable Protein States in a Highly Dynamic Protein--Protein Interaction System.

The proteins of the Bcl-2 family play key roles in the regulation of programmed cell death by controlling the integrity of the outer mitochondrial membrane and the initiation of the apoptosis process. We performed extensive molecular dynamics simulations to investigate the conformational flexibility of the Bcl-xL protein in both the apo and holo (with Bad peptide and ABT-737) states. The accelerated molecular dynamics method implemented in Amber 14 was used to produce broader conformational sampling of 200 ns simulations. The pocket mining method based on the variational implicit-solvent model tracks the dynamic evolution of the ligand binding site with a druggability score characterizing the maximal affinity achievable by a drug-like molecule. Major movements were observed around the α3-helical domain and the loop region connecting the α1 and α2 helices, reshaping the ligand interaction in the BH3 binding groove. Starting with the apo crystal structure, which is recognized as "closed" and undruggable, the BH3 groove transitioned between the "open" and "closed" states during equilibrium simulation. Further analysis revealed a small percentage of the trajectory frames (∼10%) with a moderate degree of druggability that mimic the ligand-bound states. The ability to attain and detect by computer simulation the most suitable conformational states for ligand binding in advance of compound synthesis and crystal structure solution is of immense value to the application and success of structure-based drug design.

Abstract B44: Druggability assessment of protein tyrosine phosphatase binding site

Abstract Protein tyrosine phosphatases play important roles in the signalling system in our bodies and were observed to be overexpressed in many diseases. For instance, PTP1B, the most famous member of this family, has a well-established role in diabetes mellitus, obesity and cancer. However, no PTP inhibitors have reached the market so far. This raised a question about the druggability of their catalytic site; which appears to be hydrophilic in nature and is believed to favour mainly polar ligands to bind with. In this study, we did an extensive data minding in the protein data bank to obtain all available crystal structures of all members in the PTP family. We analysed these crystal structures using SiteMap/Schrodinger. This software finds cavities in the examined proteins structure and evaluates these pockets according to their suitability for binding with drug-like ligands. Consequently, we classified PTPs according to their druggability scores; a representative of each category is shown and discussed accordingly. The influence of active site flexibility (open or closed) on PTPs druggability was studied. The effect of tight-bound water molecules on active site druggability was also assessed. This analysis can prove useful in the discovery of new PTP inhibitors and can help researchers to save unnecessary cost, money and efforts by prioritizing PTP targets according to which members have the most druggable catalytic pocket. Citation Format: Mohammad A. Ghattas, Noor Raslan, Asil Sadeq, Noor Atatreh. Druggability assessment of protein tyrosine phosphatase binding site. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B44.

An integrated structural proteomics approach along the druggable genome of Corynebacterium pseudotuberculosis species for putative druggable targets

BackgroundThe bacterium Corynebacterium pseudotuberculosis (Cp) causes caseous lymphadenitis (CLA), mastitis, ulcerative lymphangitis, and oedema in a number of hosts, comprising ruminants, thereby intimidating economic and dairy industries worldwide. So far there is no effective drug or vaccine available against Cp. Previously, a pan-genomic analysis was performed for both biovar equi and biovar ovis and a Pathogenicity Islands (PAIS) analysis within the strains highlighted a large set of proteins that could be relevant therapeutic targets for controlling the onset of CLA. In the present work, a structural druggability analysis pipeline was accomplished along 15 previously sequenced Cp strains from both biovar equi and biovar ovis.Methods and resultsWe computed the whole modelome of a reference strain Cp1002 (NCBI Accession: NC_017300.1) and then the homology models of proteins, of 14 different Cp strains, with high identity (≥ 85%) to the reference strain were also done. Druggability score of all proteins pockets was calculated and only those targets that have a highly druggable (HD) pocket in all strains were kept, a set of 58 proteins. Finally, this information was merged with the previous PAIS analysis giving two possible highly relevant targets to conduct drug discovery projects. Also, off-targeting information against host organisms, including Homo sapiens and a further analysis for protein essentiality provided a final set of 31 druggable, essential and non-host homologous targets, tabulated in table S4, additional file 1. Out of 31 globally druggable targets, 9 targets have already been reported in other pathogenic microorganisms, 3 of them (3-isopropylmalate dehydratase small subunit, 50S ribosomal protein L30, Chromosomal replication initiator protein DnaA) in C. pseudotuberculosis.ConclusionOverall we provide valuable information of possible targets against C. pseudotuberculosis where some of these targets have already been reported in other microorganisms for drug discovery projects, also discarding targets that might be physiologically relevant but are not amenable for drug binding. We propose that the constructed in silico dataset might serve as a guidance for the scientific community to have a better understanding while selecting putative therapeutic protein candidates as druggable ones as effective measures against C. pseudotuberculosis.

Computational and experimental prediction of human C-type lectin receptor druggability.

Mammalian C-type lectin receptors (CTLRS) are involved in many aspects of immune cell regulation such as pathogen recognition, clearance of apoptotic bodies, and lymphocyte homing. Despite a great interest in modulating CTLR recognition of carbohydrates, the number of specific molecular probes is limited. To this end, we predicted the druggability of a panel of 22 CTLRs using DoGSiteScorer. The computed druggability scores of most structures were low, characterizing this family as either challenging or even undruggable. To further explore these findings, we employed a fluorine-based nuclear magnetic resonance screening of fragment mixtures against DC-SIGN, a receptor of pharmacological interest. To our surprise, we found many fragment hits associated with the carbohydrate recognition site (hit rate = 13.5%). A surface plasmon resonance-based follow-up assay confirmed 18 of these fragments (47%) and equilibrium dissociation constants were determined. Encouraged by these findings we expanded our experimental druggability prediction to Langerin and MCL and found medium to high hit rates as well, being 15.7 and 10.0%, respectively. Our results highlight limitations of current in silico approaches to druggability assessment, in particular, with regard to carbohydrate-binding proteins. In sum, our data indicate that small molecule ligands for a larger panel of CTLRs can be developed.

Simulation of the mutation F76del on the von Hippel–Lindau tumor suppressor protein: Mechanism of the disease and implications for drug development

The von Hippel-Lindau tumor suppressor protein (pVHL) plays a central role in the oxygen-sensing pathway by regulating the degradation of the hypoxia-inducible factor (HIF-1α). The capture of HIF-1α by pVHL is regulated by an oxygen-dependent hydroxylation of a specific conserved prolyl residue. The VHL gene is mutated in the von Hippel-Lindau cancer predisposition syndrome, which is characterized by the development of highly vascularized tumors and is associated with constitutively high levels of HIF-1α. The disturbance of the dynamic coupling between HIF-1α and pVHL bearing the commonly found mutation F76del was experimentally confirmed but the mechanism of such complex disruption is still not clear. Performing unbiased molecular dynamics simulations, we show that the F76del mutation may enlarge the HIF binding pocket in pVHL and induce the formation of an internal cavity in the hydrophobic core of the β-domain, which can lead to a partial destabilization of the β-sheets S1, S4, and S7 and a consequent loss of hydrogen bonds with a conserved recognition motif in HIF. The newly formed cavity has a significant druggability score and may be a suitable target for stabilizing ligands. Studies of this nature may help to fill the information gap between genotype-phenotype correlations with details obtained at atomic level and provide basis for future development of drug candidates, such as pharmacological chaperones, with the specific aim of reverting the dysfunction of such pathological protein complexes found in patients with VHL.

Combining Global and Local Measures for Structure-Based Druggability Predictions

Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

BackgroundThe genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.ResultsIn this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.ConclusionsWe were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.