scholarly journals The anticancer natural product ophiobolin A induces cytotoxicity by covalent modification of phosphatidylethanolamine

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Christopher Chidley ◽  
Sunia A Trauger ◽  
Kıvanç Birsoy ◽  
Erin K O'Shea
Keyword(s):  
Small Molecules ◽  
Natural Product ◽  
Mechanism Of Action ◽  
De Novo ◽  
Covalent Modification ◽  
Human Cells ◽  
Head Group ◽  
Loss Of Function ◽  
Drug Leads

Phenotypic screens allow the identification of small molecules with promising anticancer activity, but the difficulty in characterizing the mechanism of action of these compounds in human cells often undermines their value as drug leads. Here, we used a loss-of-function genetic screen in human haploid KBM7 cells to discover the mechanism of action of the anticancer natural product ophiobolin A (OPA). We found that genetic inactivation of de novo synthesis of phosphatidylethanolamine (PE) mitigates OPA cytotoxicity by reducing cellular PE levels. OPA reacts with the ethanolamine head group of PE in human cells to form pyrrole-containing covalent cytotoxic adducts and these adducts lead to lipid bilayer destabilization. Our characterization of this unusual cytotoxicity mechanism, made possible by unbiased genetic screening in human cells, suggests that the selective antitumor activity displayed by OPA may be due to altered membrane PE levels in cancer cells.

scholarly journals De Novo Drug Design Using Artificial Intelligence Applied on SARS-CoV-2 Viral Proteins ASYNT-GAN

BioChem ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 36-48
Author(s):  
Ivan Jacobs ◽  
Manolis Maragoudakis
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Natural Product ◽  
De Novo ◽  
Viral Proteins ◽  
Computer Assisted ◽  
De Novo Drug Design ◽  
Learning Techniques ◽  
Latent Space ◽  
Synthetic Molecule

Computer-assisted de novo design of natural product mimetics offers a viable strategy to reduce synthetic efforts and obtain natural-product-inspired bioactive small molecules, but suffers from several limitations. Deep learning techniques can help address these shortcomings. We propose the generation of synthetic molecule structures that optimizes the binding affinity to a target. To achieve this, we leverage important advancements in deep learning. Our approach generalizes to systems beyond the source system and achieves the generation of complete structures that optimize the binding to a target unseen during training. Translating the input sub-systems into the latent space permits the ability to search for similar structures, and the sampling from the latent space for generation.

Clinical and molecular characterization of de novo loss of function variants inHNRNPU

2017 ◽  
Vol 173 (10) ◽  
pp. 2680-2689 ◽  
Author(s):  
Magalie S. Leduc ◽  
Hsiao-Tuan Chao ◽  
Chunjing Qu ◽  
Magdalena Walkiewicz ◽  
Rui Xiao ◽  
...  
Keyword(s):  
Molecular Characterization ◽  
De Novo ◽  
Loss Of Function

scholarly journals Recent Developments of Useful MALDI Matrices for the Mass Spectrometric Characterization of Lipids

Biomolecules ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
Jenny Leopold ◽  
Yulia Popkova ◽  
Kathrin Engel ◽  
Jürgen Schiller
Keyword(s):  
Small Molecules ◽  
Liquid Crystalline ◽  
De Novo ◽  
Organic Matrix ◽  
Laser Wavelength ◽  
Benzoic Acid Derivatives ◽  
The Matrix ◽  
Recent Developments ◽  
Ionization Methods

Matrix-assisted laser desorption/ionization (MALDI) is one of the most successful “soft” ionization methods in the field of mass spectrometry and enables the analysis of a broad range of molecules, including lipids. Although the details of the ionization process are still unknown, the importance of the matrix is commonly accepted. Both, the development of and the search for useful matrices was, and still is, an empirical process, since properties like vacuum stability, high absorption at the laser wavelength, etc. have to be fulfilled by a compound to become a useful matrix. This review provides a survey of successfully used MALDI matrices for the lipid analyses of complex biological samples. The advantages and drawbacks of the established organic matrix molecules (cinnamic or benzoic acid derivatives), liquid crystalline matrices, and mixtures of common matrices will be discussed. Furthermore, we will deal with nanocrystalline matrices, which are most suitable to analyze small molecules, such as free fatty acids. It will be shown that the analysis of mixtures and the quantitative analysis of small molecules can be easily performed if the matrix is carefully selected. Finally, some basic principles of how useful matrix compounds can be “designed” de novo will be introduced.

scholarly journals A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Brent Townshend ◽  
Joy S. Xiang ◽  
Gabriel Manzanarez ◽  
Eric J. Hayden ◽  
Christina D. Smolke
Keyword(s):  
Small Molecules ◽  
Biosynthetic Pathway ◽  
De Novo ◽  
Living Cells ◽  
Regulate Gene Expression ◽  
Liquid Handling ◽  
Regulate Gene

AbstractBiosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Here, using aptamer-coupled ribozyme libraries and a ribozyme regeneration method, de novo rapid in vitro evolution of RNA biosensors (DRIVER) enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identify and validate biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors are applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors are also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

scholarly journals A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors

2020 ◽  
Author(s):  
Brent Townshend ◽  
Joy Xiang ◽  
Gabriel Manzanarez ◽  
Eric Hayden ◽  
Christina Smolke
Keyword(s):  
Small Molecules ◽  
Biosynthetic Pathway ◽  
De Novo ◽  
Living Cells ◽  
Regulate Gene Expression ◽  
Liquid Handling ◽  
Regulate Gene

AbstractBiosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Using aptamer-coupled ribozyme libraries and a novel ribozyme regeneration method, we developed de novo rapid in vitro evolution of RNA biosensors (DRIVER) that enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identified and validated biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors were applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors were also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

scholarly journals Topologically Dependent Abundance of Spontaneous DNA Damage in Single Human Cells

2019 ◽  
Author(s):  
Qiangyuan Zhu ◽  
Yichi Niu ◽  
Michael Gundry ◽  
Kuanwei Sheng ◽  
Muchun Niu ◽  
...  
Keyword(s):  
Dna Damage ◽  
Single Cell ◽  
Genome Stability ◽  
De Novo ◽  
Single Cells ◽  
Human Cells ◽  
Single Nucleotide Variants ◽  
Genome Wide ◽  
First Time

AbstractIn the studies of single-cell genomics, the large endeavor has been focused on the detection of the permanent changes in the genome. On the other hand, spontaneous DNA damage frequently occurs and results in transient single-stranded changes to the genome until they are repaired. So far, successful profiling of these dynamic changes has not been demonstrated by single-cell whole-genome amplification methods. Here we reported a novel single-cell WGA method: Linearly Produced Semiamplicon based Split Amplification Reaction (LPSSAR), which allows, for the first time, the genome-wide detection of the DNA damage associated single nucleotide variants (dSNVs) in single human cells. The sequence-based detection of dSNVs allows the direct characterization of the major damage signature that occurred in human cells. In the analysis of the abundance of dSNVs along the genome, we observed two modules of dSNV abundance, instead of a homogeneous abundance of dSNVs. Interestingly, we found that the two modules are associated with the A/B topological compartments of the genome. This result suggests that the genome topology directly influences genome stability. Furthermore, with the detection of a large number of dSNVs in single cells, we showed that only under a stringent filtering condition, can we distinguish the de novo mutations from the dSNVs and achieve a reliable estimation of the total level of de novo mutations in a single cell.

scholarly journals Dereplikace látek a de novo charakterizace malých molekul z hmotnostních spekter

2022 ◽  
Vol 116 (1) ◽  
pp. 11-19
Author(s):  
Jiří Novák ◽  
Vladimír Havlíček
Keyword(s):  
Mass Spectrometry ◽  
Small Molecules ◽  
Mass Spectra ◽  
De Novo ◽  
Imaging Mass Spectrometry ◽  
Mass Spectrometry Data ◽  
Data Sets ◽  
Liquid Chromatography Mass Spectrometry ◽  
Imaging Mass Spectrometry Data

We describe the molecular dereplication principles and de novo characterization of small molecules obtained from liquid-chromatography mass spectrometry and imaging mass spectrometry data sets. Our methodology aims at supporting chemists and computer programmers to understand the hidden computing algorithms used for metabolomics mass spectrometry data processing. The approaches have been made available in the open-source tool CycloBranch. The presented tutorial extends the interpretation of mass spectra portfolia described in a series of papers published in Chemicke Listy, issues 2/2020 and 3/2020.

scholarly journals Characterization of Loss-Of-Function KCNJ2 Mutations in Atypical Andersen Tawil Syndrome

2021 ◽  
Vol 12 ◽  
Author(s):  
Pauline Le Tanno ◽  
Mathilde Folacci ◽  
Jean Revilloud ◽  
Laurence Faivre ◽  
Gabriel Laurent ◽  
...  
Keyword(s):  
De Novo ◽  
Beta Blockers ◽  
Periodic Paralysis ◽  
Dominant Negative ◽  
Polymorphic Ventricular Tachycardia ◽  
Loss Of Function ◽  
Functional Studies ◽  
Dysmorphic Features ◽  
Therapeutic Decisions

Andersen-Tawil Syndrome (ATS) is a rare disease defined by the association of cardiac arrhythmias, periodic paralysis and dysmorphic features, and is caused by KCNJ2 loss-of-function mutations. However, when extracardiac symptoms are atypical or absent, the patient can be diagnosed with Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT), a rare arrhythmia at high risk of sudden death, mostly due to RYR2 mutations. The identification of KCNJ2 variants in CPVT suspicion is very rare but important because beta blockers, the cornerstone of CPVT therapy, could be less efficient. We report here the cases of two patients addressed for CPVT-like phenotypes. Genetic investigations led to the identification of p. Arg82Trp and p. Pro186Gln de novo variants in the KCNJ2 gene. Functional studies showed that both variants forms of Kir2.1 monomers act as dominant negative and drastically reduced the activity of the tetrameric channel. We characterize here a new pathogenic variant (p.Pro186Gln) of KCNJ2 gene and highlight the interest of accurate cardiologic evaluation and of attention to extracardiac signs to distinguish CPVT from atypical ATS, and guide therapeutic decisions. We also confirm that the KCNJ2 gene must be investigated during CPVT molecular analysis.

scholarly journals Centriole triplet microtubules are required for stable centriole formation and inheritance in human cells

2017 ◽  
Author(s):  
Jennifer T. Wang ◽  
Dong Kong ◽  
Christian R. Hoerner ◽  
Jadranka Loncarek ◽  
Tim Stearns
Keyword(s):  
Cell Cycle ◽  
De Novo ◽  
Human Cells ◽  
Futile Cycle ◽  
Unicellular Eukaryotes ◽  
Paclitaxel Treatment ◽  
Microtubule Structure ◽  
In Cells

SummaryCentrioles are composed of long-lived microtubules arranged in nine triplets. In unicellular eukaryotes, loss of the noncanonical tubulins, delta-tubulin and epsilon tubulin, result in loss of the triplet microtubule structure. However, the contribution of triplet microtubules to mammalian centriole formation and stability is unknown. Here, we report the first characterization of delta-tubulin and epsilon-tubulin null human cells. Centrioles in cells lacking either delta-tubulin or epsilon-tubulin lack triplet microtubules and fail to undergo centriole maturation. These aberrant centrioles are formed de novo each cell cycle, but are unstable and do not persist to the next cell cycle, leading to a futile cycle of centriole formation and disintegration. Disintegration can be suppressed by paclitaxel treatment. Delta-tubulin and epsilon-tubulin physically interact, indicating that these tubulins act together to maintain triplet microtubules and that these are necessary for inheritance of centrioles from one cell cycle to the next.

scholarly journals MSNovelist: De novo structure generation from mass spectra

2021 ◽  
Author(s):  
Michael A Stravs ◽  
Kai Dührkop ◽  
Sebastian Böcker ◽  
Nicola Zamboni
Keyword(s):  
Neural Network ◽  
Small Molecules ◽  
Natural Product ◽  
Structure Prediction ◽  
Mass Spectra ◽  
Unsolved Problem ◽  
De Novo ◽  
Reference Dataset ◽  
Structure Generation ◽  
Spectral Libraries

Structural elucidation of small molecules de novo from mass spectra is a longstanding, yet unsolved problem. Current methods rely on finding some similarity with spectra of known compounds deposited in spectral libraries, but do not solve the problem of predicting structures for novel or poorly represented compound classes. We present MSNovelist that combines fingerprint prediction with an encoder-decoder neural network to generate structures de novo from fragment spectra. In evaluation, MSNovelist correctly reproduced 61% of database annotations for a GNPS reference dataset. In a bryophyte MS2 dataset, our de novo structure prediction substantially outscored the best database candidate for seven features, and a potential novel natural product with a flavonoid core was identified. MSNovelist allows predicting structures solely from MS2 data, and is therefore ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.

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