The strain-dependent cytostatic activity of Lactococcus lactis on CRC cell lines is mediated through the release of arginine deiminase.

BACKGROUND: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers, posing a serious public health challenge that necessitates the development of new therapeutics, therapies, and prevention methods. Among the various therapeutic approaches, interventions involving lactic acid bacteria (LAB) as probiotics and postbiotics have emerged as promising candidates for treating and preventing CRC. While human-isolated LAB strains are considered highly favorable, those sourced from environmental reservoirs such as dairy and fermented foods are also being recognized as potential sources for future therapeutics. RESULTS: In this study, we present a novel and therapeutically promising strain, Lactococcus lactis ssp. lactis Lc4, isolated from dairy sources. Lc4 demonstrated the ability to release the cytostatic agent - arginine deiminase (ADI) - into the post-cultivation supernatant when cultured under conditions mimicking the human gut environment. Released arginine deiminase was able to significantly reduce the growth of HT-29 and HCT116 cells due to the depletion of arginine, which led to decreased levels of c-Myc, reduced phosphorylation of p70-S6 kinase, and cell cycle arrest. The ADI release and cytostatic properties were strain-dependent, as was evident from comparison to other L. lactis ssp. lactis strains. CONCLUSION: For the first time, we unveil the anti-proliferative properties of the L. lactis cell-free supernatant (CFS), which are independent of bacteriocins or other small molecules. We demonstrate that ADI, derived from a dairy-Generally Recognized As Safe (GRAS) strain of L. lactis, exhibits anti-proliferative activity on cell lines with different levels of argininosuccinate synthetase 1 (ASS1) expression. A unique feature of the Lc4 strain is also its capability to release ADI into the extracellular space. Taken together, we showcase L. lactis ADI and the Lc4 strain as promising, potential therapeutic agents with broad applicability.

A first insight into the genome of Prototheca wickerhamii, a major causative agent of human protothecosis.

BACKGROUND: Colourless microalgae of the Prototheca genus are the only known plants that have consistently been implicated in a range of clinically relevant opportunistic infections in both animals and humans. The Prototheca algae are emerging pathogens, whose incidence has increased importantly over the past two decades. Prototheca wickerhamii is a major human pathogen, responsible for at least 115 cases worldwide. Although the algae are receiving more attention nowadays, there is still a substantial knowledge gap regarding their biology, and pathogenicity in particular. Here we report, for the first time, the complete nuclear genome, organelle genomes, and transcriptome of the P. wickerhamii type strain ATCC 16529. RESULTS: The assembled genome size was of 16.7 Mbp, making it the smallest and most compact genome sequenced so far among the protothecans. Key features of the genome included a high overall GC content (64.5%), a high number (6081) and proportion (45.9%) of protein-coding genes, and a low repetitive sequence content (2.2%). The vast majority (90.6%) of the predicted genes were confirmed with the corresponding transcripts upon RNA-sequencing analysis. Most (93.2%) of the genes had their putative function assigned when searched against the InterProScan database. A fourth (23.3%) of the genes were annotated with an enzymatic activity possibly associated with the adaptation to the human host environment. The P. wickerhamii genome encoded a wide array of possible virulence factors, including those already identified in two model opportunistic fungal pathogens, i.e. Candida albicans and Trichophyton rubrum, and thought to be involved in invasion of the host or elicitation of the adaptive stress response. Approximately 6% of the P. wickerhamii genes matched a Pathogen-Host Interaction Database entry and had a previously experimentally proven role in the disease development. Furthermore, genes coding for proteins (e.g. ATPase, malate dehydrogenase) hitherto considered as potential virulence factors of Prototheca spp. were demonstrated in the P. wickerhamii genome. CONCLUSIONS: Overall, this study is the first to describe the genetic make-up of P. wickerhamii and discovers proteins possibly involved in the development of protothecosis.

Design and in Vitro Characterization of Tricyclic Benzodiazepine Derivatives as Potent and Selective Antileukemic Agents.

Currently available chemotherapeutic treatments for blood cancers (leukemia) usually have strong side effects. More selective, efficient, and less toxic anticancer agents are needed. We synthesized seven, new, optically pure (12aS)-1,3,4,12a-tetrahydropyrazino[2,1-c][1,4],12(2H,11H)-dione derivatives and examined their cytotoxicity towards eight cancer cell lines, including urinary bladder (TCC-SUP, UM-UC-3, KU-19-9), colon (LoVo), and breast (MCF-7, MDA-MB-231) cancer representatives, as well as two leukemic cell lines (MV-4-11, CCRF-CEM) and normal murine fibroblasts (Balb/3T3) as reference cell line. Three of the seven newly-obtained compounds ((12aS)-8-bromo-2-(3-phenylbenzoyl)-1,3,4,12a-tetrahydropyrazino[2,1-c][1,4],12(2H,11H)-dione, (12aS)-8,9-dimethoxy-2-(4-phenylbenzoyl)-1,3,4,12a-tetrahydropyrazino[2,1-c][1,4],12(2H,11H)-dione and (12aS)-8-nitro-2-(4-phenylbenzoyl)-1,3,4,12a-tetrahydropyrazino[2,1-c][1,4],12(2H,11H)-dione, showed enhanced activity and selectivity toward the leukemic MV-4-11 cell lines when compared to our previously reported compounds, with IC50 values in the range of 2.9-5.6 µM. Additionally, (12aS)-9-nitro-2-(4-phenylbenzoyl)-1,3,4,12a-tetrahydropyrazino[2,1-c][1,4],12(2H,11H)-dione exhibited a strong cytotoxic effect against the leukemic CCRF-CEM (IC50 =6.1 µM) and MV-4-11 (IC50 =11.0 µM) cell lines, a moderate cytotoxic effect toward other tumor lines (IC50 =31.8-55.0 µM) and very weak cytotoxic effect toward the Balb/3T3 reference cell lines. Selected compounds were further evaluated for their potential to induce apoptotic cell death in MV-4-11 cells by measuring caspase-3 activity. We also established the crystal structure of three products and investigated the effect of 22 derivatives of 1,3,4,12a-tetrahydropyrazino[2,1-c][1,4],12(2H,11H)-dione on the activity of the cancer-associated enzyme autotaxin. All compounds proved to be weak inhibitors of autotaxin, although some (R) and (S) enantiomers had Ki values of 10-19 µM. The obtained results showed that the tested compounds exhibited a selective antileukemic effect, which appeared not to be related directly to autotaxin. Molecular targets responsible for this effect remain to be identified. The newly obtained compounds can be used in the search for new, selective anticancer therapies.

Molecular and Cellular Mechanisms Influenced by Postbiotics.

In recent years, commensal bacteria colonizing the human body have been recognized as important determinants of health and multiple pathologic conditions. Among the most extensively studied commensal bacteria are the gut microbiota, which perform a plethora of functions, including the synthesis of bioactive products, metabolism of dietary compounds, and immunomodulation, both through attenuation and immunostimulation. An imbalance in the microbiota population, i.e., dysbiosis, has been linked to many human pathologies, including various cancer types and neurodegenerative diseases. Targeting gut microbiota and microbiome-host interactions resulting from probiotics, prebiotics, and postbiotics is a growing opportunity for the effective treatment of various diseases. As more research is being conducted, the microbiome field is shifting from simple descriptive analysis of commensal compositions to more molecular, cellular, and functional studies. Insight into these mechanisms is of paramount importance for understanding and modulating the effects that microbiota, probiotics, and their derivatives exert on host health.

Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.

MOTIVATION: Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions. RESULTS: Here, we present a Protein-Ligand Extended Connectivity (PLEC) FP that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC FPs were used to construct different machine learning models tailored for predicting protein-ligand affinities (pKi∕d). Even the simplest linear model built on the PLEC FP achieved Rp = 0.817 on the Protein Databank (PDB) bind v2016 'core set', demonstrating its descriptive power. AVAILABILITY AND IMPLEMENTATION: The PLEC FP has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development and evaluation of a deep learning model for protein-ligand binding affinity prediction.

Motivation: Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to 'learn' to extract features that are relevant for the task at hand. Results: We have developed a novel deep neural network estimating the binding affinity of ligand-receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF-2013 'scoring power' benchmark and Astex Diverse Set and outperformed classical scoring functions. Availability and implementation: The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy. Supplementary information: Supplementary data are available at Bioinformatics online.

Targeting peroxiredoxin 1 impairs growth of breast cancer cells and potently sensitises these cells to prooxidant agents.

BACKGROUND: Our previous work has shown peroxiredoxin-1 (PRDX1), one of major antioxidant enzymes, to be a biomarker in human breast cancer. Hereby, we further investigate the role of PRDX1, compared to its close homolog PRDX2, in mammary malignant cells. METHODS: CRISPR/Cas9- or RNAi-based methods were used for genetic targeting PRDX1/2. Cell growth was assessed by crystal violet, EdU incorporation or colony formation assays. In vivo growth was assessed by a xenotransplantation model. Adenanthin was used to inhibit the thioredoxin-dependent antioxidant defense system. The prooxidant agents used were hydrogen peroxide, glucose oxidase and sodium L-ascorbate. A PY1 probe or HyPer-3 biosensor were used to detect hydrogen peroxide content in samples. RESULTS: PRDX1 downregulation significantly impaired the growth rate of MCF-7 and ZR-75-1 breast cancer cells. Likewise, xenotransplanted PRDX1-deficient MCF-7 cells presented a retarded tumour growth. Furthermore, genetic targeting of PRDX1 or adenanthin, but not PRDX2, potently sensitised all six cancer cell lines studied, but not the non-cancerous cells, to glucose oxidase and ascorbate. CONCLUSIONS: Our study pinpoints the dominant role for PRDX1 in management of exogeneous oxidative stress by breast cancer cells and substantiates further exploration of PRDX1 as a target in this disease, especially when combined with prooxidant agents.

DeCAF-Discrimination, Comparison, Alignment Tool for 2D PHarmacophores.

Comparison of small molecules is a common component of many cheminformatics workflows, including the design of new compounds and libraries as well as side-effect predictions and drug repurposing. Currently, large-scale comparison methods rely mostly on simple fingerprint representation of molecules, which take into account the structural similarities of compounds. Methods that utilize 3D information depend on multiple conformer generation steps, which are computationally expensive and can greatly influence their results. The aim of this study was to augment molecule representation with spatial and physicochemical properties while simultaneously avoiding conformer generation. To achieve this goal, we describe a molecule as an undirected graph in which the nodes correspond to atoms with pharmacophoric properties and the edges of the graph represent the distances between features. This approach combines the benefits of a conformation-free representation of a molecule with additional spatial information. We implemented our approach as an open-source Python module called DeCAF (Discrimination, Comparison, Alignment tool for 2D PHarmacophores), freely available at http://bitbucket.org/marta-sd/decaf. We show DeCAF's strengths and weaknesses with usage examples and thorough statistical evaluation. Additionally, we show that our method can be manually tweaked to further improve the results for specific tasks. The full dataset on which DeCAF was evaluated and all scripts used to calculate and analyze the results are also provided.

A Specialized Histone H1 Variant Is Required for Adaptive Responses to Complex Abiotic Stress and Related DNA Methylation in Arabidopsis.

Linker (H1) histones play critical roles in chromatin compaction in higher eukaryotes. They are also the most variable of the histones, with numerous nonallelic variants cooccurring in the same cell. Plants contain a distinct subclass of minor H1 variants that are induced by drought and abscisic acid and have been implicated in mediating adaptive responses to stress. However, how these variants facilitate adaptation remains poorly understood. Here, we show that the single Arabidopsis (Arabidopsis thaliana) stress-inducible variant H1.3 occurs in plants in two separate and most likely autonomous pools: a constitutive guard cell-specific pool and a facultative environmentally controlled pool localized in other tissues. Physiological and transcriptomic analyses of h1.3 null mutants demonstrate that H1.3 is required for both proper stomatal functioning under normal growth conditions and adaptive developmental responses to combined light and water deficiency. Using fluorescence recovery after photobleaching analysis, we show that H1.3 has superfast chromatin dynamics, and in contrast to the main Arabidopsis H1 variants H1.1 and H1.2, it has no stable bound fraction. The results of global occupancy studies demonstrate that, while H1.3 has the same overall binding properties as the main H1 variants, including predominant heterochromatin localization, it differs from them in its preferences for chromatin regions with epigenetic signatures of active and repressed transcription. We also show that H1.3 is required for a substantial part of DNA methylation associated with environmental stress, suggesting that the likely mechanism underlying H1.3 function may be the facilitation of chromatin accessibility by direct competition with the main H1 variants.

DiSCuS: an open platform for (not only) virtual screening results management.

DiSCuS, a "Database System for Compound Selection", has been developed. The primary goal of DiSCuS is to aid researchers in the steps subsequent to generating high-throughput virtual screening (HTVS) results, such as selection of compounds for further study, purchase, or synthesis. To do so, DiSCuS provides (1) a storage facility for ligand-receptor complexes (generated with external programs), (2) a number of tools for validating these complexes, such as scoring functions, potential energy contributions, and med-chem features with ligand similarity estimates, and (3) powerful searching and filtering options with logical operators. DiSCuS supports multiple receptor targets for a single ligand, so it can be used either to evaluate different variants of an active site or for selectivity studies. DiSCuS documentation, installation instructions, and source code can be found at http://discus.ibb.waw.pl .

Tumor Necrosis Factor Receptor-Associated Periodic Syndrome (TRAPS) with a New Pathogenic Variant in TNFRSF1A Gene in a Family of the Adult Male with Renal AA Amyloidosis-Diagnostic and Therapeutic Challenge for Clinicians.

Tumor necrosis factor receptor-associated periodic syndrome (TRAPS) belongs to systemic autoinflammatory diseases (AIDs). Many of these syndromes are genetically conditioned and can be inherited. Diagnosis relies on clinical symptoms and should be confirmed by genetic testing. One of the most serious complications is AA amyloidosis. We present the diagnostic route of a 33-year-old male with AA amyloidosis and his children, leading to diagnosis of monogenic autoinflammatory syndrome, confirmed by genetic analysis. A novel variant of the in-frame insertion type in one allele of TNFRSF1A gene was found by whole exome sequencing and confirmed by Sanger sequencing, which allowed a diagnosis of TRAPS. Three-dimensional modeling was used to assess the structural changes introduced into TNFR1 molecule by the insertion. The analysis of the 3D model revealed that accommodation of the 4AA insert induces misalignment of three cysteine bridges (especially the C70-C96 bridge) in the extracellular domain, leading to putatively misfolded and improperly functioning TNFR1. Three of the patient's daughters inherited the same variant of the TNFRSF1A gene and presented TRAPS symptoms. TRAPS is a very rare disease, but in the presence of suggestive symptoms the genetic diagnostic workout should be undertaken. Early diagnosis followed by appropriate clinical management can prevent irreversible complications.

Improving detection of protein-ligand binding sites with 3D segmentation.

In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at http://gitlab.com/cheminfIBB/kalasanty.

Sequencing and Analysis of the Complete Organellar Genomes of Prototheca wickerhamii.

Of the Prototheca genus, Prototheca wickerhamii has the highest clinical significance in humans. However, neither nuclear nor organellar genomes of this species were sequenced until now. The hitherto determined and analyzed mitochondrial and plastid genomes of the alleged P. wickerhamii species belong in fact to another species, recently named Prototheca xanthoriae. This study provides a first insight into the organellar genomes of a true P. wickerhamii (type strain ATCC 16529). The P. wickerhamii mitochondrion had a 53.8-kb genome, which was considerably larger than that of Prototheca ciferrii (formerly Prototheca zopfii gen. 1) and Prototheca bovis (formerly Prototheca zopfii gen. 2), yet similarly functional, with the differences in size attributable to a higher number of introns and the presence of extra unique putative genes. The 48-kb plastid genome of P. wickerhamii, compared to autotrophic Trebouxiophyceae, was highly reduced due to the elimination of the photosynthesis-related genes. The gene content of the plastid genome of P. wickerhamii was, however, very similar to other colorless Prototheca species. Plastid genome-based phylogeny reinforced the polyphyly of the genus Prototheca, with Helicosporidium and Auxenochlorella branching within clades of Prototheca species. Phylogenetic reconstruction also confirmed the close relationship of P. wickerhamii and P. xanthoriae, which is reflected in the synteny of their organellar genomes. Interestingly, the entire set of atp genes was lost in P. wickerhamii plastid genome while being preserved in P. xanthoriae.

The High Throughput Sequence Annotation Service (HT-SAS) - the shortcut from sequence to true Medline words.

BACKGROUND: Advances in high-throughput technologies available to modern biology have created an increasing flood of experimentally determined facts. Ordering, managing and describing these raw results is the first step which allows facts to become knowledge. Currently there are limited ways to automatically annotate such data, especially utilizing information deposited in published literature. RESULTS: To aid researchers in describing results from high-throughput experiments we developed HT-SAS, a web service for automatic annotation of proteins using general English words. For each protein a poll of Medline abstracts connected to homologous proteins is gathered using the UniProt-Medline link. Overrepresented words are detected using binomial statistics approximation. We tested our automatic approach with a protein test set from SGD to determine the accuracy and usefulness of our approach. We also applied the automatic annotation service to improve annotations of proteins from Plasmodium bergei expressed exclusively during the blood stage. CONCLUSION: Using HT-SAS we created new, or enriched already established annotations for over 20% of proteins from Plasmodium bergei expressed in the blood stage, deposited in PlasmoDB. Our tests show this approach to information extraction provides highly specific keywords, often also when the number of abstracts is limited. Our service should be useful for manual curators, as a complement to manually curated information sources and for researchers working with protein datasets, especially from poorly characterized organisms.

e-LiSe--an online tool for finding needles in the '(Medline) haystack'.

UNLABELLED: Using literature databases one can find not only known and true relations between processes but also less studied, non-obvious associations. The main problem with discovering such type of relevant biological information is 'selection'. The ability to distinguish between a true correlation (e.g. between different types of biological processes) and random chance that this correlation is statistically significant is crucial for any bio-medical research, literature mining being no exception. This problem is especially visible when searching for information which has not been studied and described in many publications. Therefore, a novel bio-linguistic statistical method is required, capable of 'selecting' true correlations, even when they are low-frequency associations. In this article, we present such statistical approach based on Z-score and implemented in a web-based application 'e-LiSe'. AVAILABILITY: The software is available at http://miron.ibb.waw.pl/elise/

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.

Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to build RF-Score, a scoring function utilizing the machine-learning technique known as Random Forest (RF). We also point out how to use different data, features, and regression models using either R or Python programming languages.

Epigenetic reactivation of tumor suppressor genes by a novel small-molecule inhibitor of human DNA methyltransferases.

DNA methylation regulates gene expression in normal and malignant cells. The possibility to reactivate epigenetically silenced genes has generated considerable interest in the development of DNA methyltransferase inhibitors. Here, we provide a detailed characterization of RG108, a novel small molecule that effectively blocked DNA methyltransferases in vitro and did not cause covalent enzyme trapping in human cell lines. Incubation of cells with low micromolar concentrations of the compound resulted in significant demethylation of genomic DNA without any detectable toxicity. Intriguingly, RG108 caused demethylation and reactivation of tumor suppressor genes, but it did not affect the methylation of centromeric satellite sequences. These results establish RG108 as a DNA methyltransferase inhibitor with fundamentally novel characteristics that will be particularly useful for the experimental modulation of epigenetic gene regulation.

Performance of machine-learning scoring functions in structure-based virtual screening.

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and -0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).

Characterization of mAb6-9-1 monoclonal antibody against hemagglutinin of avian influenza virus H5N1 and its engineered derivative, single-chain variable fragment antibody.

Hemagglutinin (HA), as a major surface antigen of influenza virus, is widely used as a target for production of neutralizing antibodies. Monoclonal antibody, mAb6-9-1, directed against HA of highly pathogenic avian influenza virus A/swan/Poland/305-135V08/2006(H5N1) was purified from mouse hybridoma cells culture and characterized. The antigenic specificity of mAb6-9-1 was verified by testing its cross-reactivity with several variants of HA. The mimotopes recognized by mAb6-9-1 were selected from two types of phage display peptide libraries. The comparative structural model of the HA variant used for antibody generation was developed to further facilitate epitope mapping. Based on the sequences of the affinity- selected polypeptides and the structural model of HA the epitope was located to the region near the receptor binding site (RBS). Such localization of the epitope recognized by mAb6-9-1 is in concordance with its moderate hemagglutination inhibiting activity and its antigenic specificity. Additionally, total RNA isolated from the hybridoma cell line secreting mAb6-9-1 was used for obtaining two variants of cDNA encoding recombinant single-chain variable fragment (scFv) antibody. To ensure high production level and solubility in bacterial expression system, the scFv fragments were produced as chimeric proteins in fusion with thioredoxin or displayed on a phage surface after cloning into the phagemid vector. Specificity and affinity of the recombinant soluble and phage-bound scFv were assayed by suitable variants of ELISA test. The observed differences in specificity were discussed.

Discovery of two novel, small-molecule inhibitors of DNA methylation.

DNA methyltransferases are promising targets for cancer therapy. In many cancer cells promoters of tumor suppressor genes are hypermethylated, which results in gene inactivation. It has been shown that DNA methyltransferase inhibitors can suppress tumor growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. To discover novel compounds for the inhibition of human DNA methyltransferases, we have screened a set of small molecules available from the NCI database. Using a 3-dimensional model of the human DNA methyltransferase 1 and a modified docking and scoring procedure, we have identified a small list of molecules with high affinities for the active site of the enzyme. The two highest scoring structures were found to inhibit DNA methyltransferase activity in vitro and in vivo. The newly discovered inhibitors validate our screening procedure and also provide a useful basis for further rational drug development.