GNPS - Design and application of synthetic 17B-HSD13 substrates to drug discovery, and to reveal preserved catalytic activity of protective human variants
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Jeffrey Culver
PROVIDER: MSV000095037 | GNPS | Fri Jun 14 07:07:00 BST 2024
SECONDARY ACCESSION(S): PXD053125
REPOSITORIES: GNPS
Similar Datasets
Project description:Genotyped human hepatocytes for assessing levels of Hsd17b13 in 'IsoA' donors relative to 'IsoD'
2024-06-14 | MSV000095037 | MassIVE
Project description:Embryonic stem (ES) cells and their differentiated progeny offer tremendous potential for regenerative medicine, even in the field of drug discovery. There is an urgent need for clinically relevant assays that make use of ES cells because of their rich biological utility. Attention has been focused on small molecules that allow the precise manipulation of cells in vitro, which could allow researchers to obtain homogeneous cell types for cell-based therapies and discover drugs for stimulating the regeneration of endogenous cells. Such therapeutics can act on target cells or their niches in vivo to promote cell survival, proliferation, differentiation, and homing. In the present paper, we reviewed the use of ES cell models for high-throughput/content drug screening and toxicity assessment. In addition, we examined the role of stem cells in large pharmaceutical companies' R&D and discussed a novel subject, nicheology, in stem cell-related research fields.
| S-EPMC4009932 | biostudies-other
Project description:Unique features of tumours that can be exploited by targeted therapies are a key focus of current cancer research. One such approach is known as synthetic lethality screening, which involves searching for genetic interactions of two mutations whereby the presence of either mutation alone has no effect on cell viability but the combination of the two mutations results in cell death. The presence of one of these mutations in cancer cells but not in normal cells can therefore create opportunities to selectively kill cancer cells by mimicking the effect of the second genetic mutation with targeted therapy. Here, we summarize strategies that can be used to identify synthetic lethal interactions for anticancer drug discovery, describe examples of such interactions that are currently being investigated in preclinical and clinical studies of targeted anticancer therapies, and discuss the challenges of realizing the full potential of such therapies.
| S-EPMC3652585 | biostudies-literature
Project description:Atg4 cysteine proteases (autophagins) play crucial roles in autophagy by proteolytic activation of Atg8 paralogs for targeting to autophagic vesicles by lipid conjugation, as well as in subsequent deconjugation reactions. However, the means to measure the activity of autophagins is limited. Herein, we describe two novel substrates for autophagins suitable for a diversity of in vitro assays, including (i) fluorogenic tetrapeptide acetyl-Gly-L-Thr-L-Phe-Gly-AFC (Ac-GTFG-AFC) and (ii) a fusion protein comprised of the natural substrate LC3B appended to the N-terminus of phospholipase A(2) (LC3B-PLA(2)), which upon cleavage releases active PLA(2) for fluorogenic assay. To generate the synthetic tetrapeptide substrate, the preferred tetrapeptide sequence recognized by autophagin-1/Atg4B was determined using a positional scanning combinatorial fluorogenic tetrapeptide library. With the LC3B-PLA(2) substrate, we show that mutation of the glycine proximal to the scissile bond in LC3B abolishes activity. Both substrates showed high specificity for recombinant purified autophagin-1/Atg4B compared to closely related proteases and the LC3B-PLA(2) substrate afforded substantially higher catalytic rates (k(cat)/K(m) 5.26 x 10(5) M(-1)/sec(-1)) than Ac-GTFG-AFC peptide (0.92 M(-1)/sec(-1)), consistent with substrate-induced activation. Studies of autophagin-1 mutants were also performed, including the protease lacking a predicted autoinhibitory domain at residues 1 to 24 and lacking a regulatory loop at residues 259 to 262. The peptide and fusion protein substrates were also employed for measuring autophagin activity in cell lysates, showing a decrease in cells treated with autophagin-1/Atg4B siRNA or transfected with a plasmid encoding Atg4B (Cys74Ala) dominantnegative. Therefore, the synthetic substrates for autophagins reported here provide new research tools for studying autophagy.
| S-EPMC3039740 | biostudies-literature
Project description:The world has now entered into a new era of genomics because of the continued advancements in the next generation high throughput sequencing technologies, which includes sequencing by synthesis-fluorescent in situ sequencing (FISSEQ), pyrosequencing, sequencing by ligation using polony amplification, supported oligonucleotide detection (SOLiD), sequencing by hybridization along with sequencing by ligation, and nanopore technology. Great impacts of these methods can be seen for solving the genome related problems of plant and animal kingdom that will open the door of a new era of genomics. This may ultimately overcome the Sanger sequencing that ruled for 30 years. NGS is expected to advance and make the drug discovery process more rapid.
| S-EPMC3933208 | biostudies-other
Project description:Recently, next-generation sequencing has been introduced as a promising, new platform for assessing the copy number of transcripts, while the existing microarray technology is considered less reliable for absolute, quantitative expression measurements. Nonetheless, so far, results from the two technologies have only been compared based on biological data, leading to the conclusion that, although they are somewhat correlated, expression values differ significantly. Here, we use synthetic RNA samples, resembling human microRNA samples, to find that microarray expression measures actually correlate better with sample RNA content than expression measures obtained from sequencing data. In addition, microarrays appear highly sensitive and perform equivalently to next-generation sequencing in terms of reproducibility and relative ratio quantification.
2009-09-08 | GSE14511 | GEO
Project description:Diphosphoinositol pentakisphosphate kinase 2 (PPIP5K2) is one of the mammalian PPIP5K isoforms responsible for synthesis of diphosphoinositol polyphosphates (inositol pyrophosphates; PP-InsPs), regulatory molecules that function at the interface of cell signaling and organismic homeostasis. The development of drugs that inhibit PPIP5K2 could have both experimental and therapeutic applications. Here, we describe a synthetic strategy for producing naturally occurring 5-PP-InsP4, as well as several inositol polyphosphate analogs, and we study their interactions with PPIP5K2 using biochemical and structural approaches. These experiments uncover an additional ligand-binding site on the surface of PPIP5K2, adjacent to the catalytic pocket. This site facilitates substrate capture from the bulk phase, prior to transfer into the catalytic pocket. In addition to demonstrating a "catch-and-pass" reaction mechanism in a small molecule kinase, we demonstrate that binding of our analogs to the substrate capture site inhibits PPIP5K2. This work suggests that the substrate-binding site offers new opportunities for targeted drug design.
| S-EPMC4085797 | biostudies-literature
Project description:BackgroundTechnological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.ResultsWe applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets.ConclusionsThis work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.
| S-EPMC8042985 | biostudies-literature
Project description:P-Glycoprotein functions as an ATP-driven active efflux pump for many natural products and chemotherapeutic drugs. Hydrophobic peptides have been shown to block drug uptake by P-glycoprotein, indicating that they might be transport substrates. The present study examines the interaction of the synthetic peptide series NAc-LnY-amide with the multidrug transporter. Several peptides in this series caused up to 3.5-fold enhancement of colchicine accumulation in membrane vesicles from multidrug resistant (MDR) cells, which suggests the existence of novel interactions between the binding sites for peptides and drug. Peptides did not stimulate vinblastine transport, which was inhibited as expected for competing substrates. These peptides displayed modest stimulatory effects on the ATPase activity of P-glycoprotein. None blocked azidopine photoaffinity labelling, showing that they probably occupy a binding site separate from that for the drug. Studies with 125I-labelled NAc-LLY-amide showed that it was transported by P-glycoprotein in both membrane vesicles and reconstituted proteoliposomes. Uptake of the peptide was rapid, saturable, osmotically sensitive and occurred against a concentration gradient. The enhancing effect of NAc-LLY-amide on colchicine transport was reciprocated, i.e. colchicine greatly increased the transport of labelled peptide by P-glycoprotein. Peptide transport was also modulated, both positively and negatively, by other MDR spectrum drugs. It is concluded that linear hydrophobic peptides are indeed transported by P-glycoprotein, and some have interactions with drug substrates that result in mutual stimulation of transport.
| S-EPMC1217947 | biostudies-other
Project description:Fragment-based drug discovery (FBDD) is a powerful method to develop potent small-molecule compounds starting from fragments binding weakly to targets. As FBDD exhibits several advantages over high-throughput screening campaigns, it becomes an attractive strategy in target-based drug discovery. Many potent compounds/inhibitors of diverse targets have been developed using this approach. Methods used in fragment screening and understanding fragment-binding modes are critical in FBDD. This review elucidates fragment libraries, methods utilized in fragment identification/confirmation, strategies applied in growing the identified fragments into drug-like lead compounds, and applications of FBDD to different targets. As FBDD can be readily carried out through different biophysical and computer-based methods, it will play more important roles in drug discovery.
| S-EPMC7419598 | biostudies-literature