H2020Индивидуална стипендия2020–2022

MITiC · Molecules Inhibiting Translation in Cancer cells

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2020-11-01 → 2022-10-31
Финансиране от ЕС
184 708 €
Участници
1
Схема
MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Молекулите, които блокират взаимодействието между протеини и РНК в раковите клетки, са обект на анализ чрез компютърно моделиране и лабораторни тестове. Това помага за разработването на нови лекарства, които по-точно атакуват процесите в раковите клетки.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Molecules Inhibiting Translation in Cancer cells

While targeting Protein:Protein interactions has served as basis for the development of new drugs, RNA:Protein interactions, which are as important in human pathologies, notably cancer, is very promising but remains largely unexploited. Several challenges arise from the drug discovery process such as finding a druggable pocket in RNA-binding interfaces, the quality of the computational models, the strategies used in the in silico screening, and the lack of experimental feedback and validation of computationally predicted inhibitors essential to orient the rational drug design procedure toward the most relevant molecules. Besides the above listed issues, new experimental assays must be developed to screen molecules targeting RPIs which ideally would work in a cellular context and be amenable to high content screening (HCS). In this project, we address the lack of methods able to score RPIs in a cellular context by adapting the microtubule bench assay (MT bench) to score protein interactions with endogenous mRNAs in cells and implemented a robust HCS-based detection scheme. We have also developed a drug screening approach that integrates chemical, structural and cellular data from both advanced computational and experimental techniques for the development of small molecules that target RPIs. This is done by developing an innovative approach that would integrate a) molecular modeling data (Drug design, Molecular Dynamics and Free Energy Simulations using a sufficiently accurate computational model that is computationally efficient), b) NMR Spectroscopy data, c) together with an experimental validation in cells with a new HCS technology, “MT Bench”, that quantifies RNA:protein interactions at the single cell level. The major advantage is providing cellular and structural data to feed, with little delay, the computational approach to propose efficient and specific ligands that target translation regulation in vitro and in cancer cells. As an application, we chose to target YB-1 (YBX1 gene), a mRNA-binding protein relevant target in cancer, notably owing to its role in cancer resistance and cancer cell plasticity. YB-1 has been recently considered as a therapeutic target for the treatment of cancer and drug-resistant cancer.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

YB-1 is one of the major mRNA binding proteins and a master regulator of translation in cancer cells. It has been recently considered as a therapeutic target for the treatment of cancer and drug-resistant cancer. However, no small molecules with high affinity and specificity against YB-1 have been proposed so far, and the structural data essential to interrogate their relevance are missing. Understanding the function of translation regulation systems in order to design drug candidates is a very delicate procedure that requires a high level of accuracy and state-of-the-art techniques. Based on a promising preliminary finding of an active compound, we will focus in depth on studying the inhibition mechanism of YB-1 in order to help design new anti-cancer drugs able to overcome drug resistance. The research approach developed in this context aims to use in synergy advanced computational and experimental techniques while overcoming all limitations in a concerted way. This work is concerned with providing a sufficiently accurate computational model that is computationally efficient, specific experimental data and combining them with data mining techniques, for a molecular-level characterization of the inhibition dynamics and thermodynamics. This project is based on a multidisciplinary approach that requires knowledge of biology, biochemistry, physical chemistry, theoretical chemistry and bioinformatics in order to contribute to the medicine of the future.

Оригинален текст от CORDIS (на английски).

Участници

  • UNIVERSITE D'EVRY-VAL D'ESSONE · EvryКоординаторФранция

Връзки

Данни: CORDIS, © Европейски съюз