Dataset Information

Targeting de novo lipogenesis as a novel approach in anti-cancer therapy.

ABSTRACT: BACKGROUND:Although altered membrane physiology has been discussed within the context of cancer, targeting membrane characteristics by drugs being an attractive therapeutic strategy has received little attention so far. METHODS:Various acetyl-CoA carboxylase 1 (ACC1), and fatty acid synthase (FASN) inhibitors (like Soraphen A and Cerulenin) as well as genetic knockdown approaches were employed to study the effects of disturbed phospholipid composition on membrane properties and its functional impact on cancer progression. By using state-of-the-art methodologies such as LC-MS/MS, optical tweezers measurements of giant plasma membrane vesicles and fluorescence recovery after photobleaching analysis, membrane characteristics were examined. Confocal laser scanning microscopy, proximity ligation assays, immunoblotting as well as migration, invasion and proliferation experiments unravelled the functional relevance of membrane properties in vitro and in vivo. RESULTS:By disturbing the deformability and lateral fluidity of cellular membranes, the dimerisation, localisation and recycling of cancer-relevant transmembrane receptors is compromised. Consequently, impaired activation of growth factor receptor signalling cascades results in abrogated tumour growth and metastasis in different in vitro and in vivo models. CONCLUSIONS:This study highlights the field of membrane properties as a promising druggable cellular target representing an innovative strategy for development of anti-cancer agents.

SUBMITTER: Stoiber K

PROVIDER: S-EPMC5765225 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Targeting de novo lipogenesis as a novel approach in anti-cancer therapy.

Stoiber Katharina K Nagło Olga O Pernpeintner Carla C Zhang Siwei S Koeberle Andreas A Ulrich Melanie M Werz Oliver O Müller Rolf R Zahler Stefan S Lohmüller Theobald T Feldmann Jochen J Braig Simone S

British journal of cancer 20171107 1

<h4>Background</h4>Although altered membrane physiology has been discussed within the context of cancer, targeting membrane characteristics by drugs being an attractive therapeutic strategy has received little attention so far.<h4>Methods</h4>Various acetyl-CoA carboxylase 1 (ACC1), and fatty acid synthase (FASN) inhibitors (like Soraphen A and Cerulenin) as well as genetic knockdown approaches were employed to study the effects of disturbed phospholipid composition on membrane properties and it ...[more]

PMID: 29112683

Similar Datasets

Project description:Generative deep learning models have emerged as a powerful approach for de novo drug design as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89%, with different models having different error-type distributions. Post hoc correction of SMILES was shown to increase model validity. The SMILES corrector trained with one error per input alters 60-90% of invalid generator outputs and fixes 35-80% of them. However, a higher error detection and performance was obtained for transformer models trained with multiple errors per input. In this case, the best model was able to correct 60-95% of invalid generator outputs. Further analysis showed that these fixed molecules are comparable to the correct molecules from the de novo generators based on novelty and similarity. Additionally, the SMILES corrector can be used to expand the amount of interesting new molecules within the targeted chemical space. Introducing different errors into existing molecules yields novel analogs with a uniqueness of 39% and a novelty of approximately 20%. The results of this research demonstrate that SMILES correction is a viable post hoc extension and can enhance the search for better drug candidates.

Dataset Information

Targeting de novo lipogenesis as a novel approach in anti-cancer therapy.

Publications

Targeting de novo lipogenesis as a novel approach in anti-cancer therapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets