H2020Индивидуална стипендия2021–2023

G4-mtQSAR · Identifying new anti-cancer drugs by computational multi-target approaches targeting the Gquadruplex DNA

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

Период: 2021-06-01 → 2023-05-31
Финансиране от ЕС: 172 932 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Специални структури в ДНК, наречени G-квадруплекси, се анализират чрез компютърни методи за откриване на молекули, които да ги стабилизират. Това помага за по-ефективно блокиране на няколко онкогена едновременно, което е важно при лечението на сложни видове рак.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Identifying new anti-cancer drugs by computational multi-target approaches targeting the Gquadruplex DNA

G-Quadruplexes (G4s) are guanine-rich four-stranded nucleic acid structures, which are abundantly found in the promoter region of various oncogenes (cMYC, cKIT, KRAS, etc.) and in the telomeric region. Overexpression of these oncogenes and telomerase-induced sustained elongation of telomeric DNA leads to deregulated cell division and cell immortalities. Such a phenomenon has been observed in many cancer pathologies. Thus Ligand-induced stabilization of G4s has been demonstrated to be efficient in targeted cancer therapy[1,2]. Simultaneous deregulation of multiple oncogenes is a major hurdle in treating a complex disease like cancer; simultaneously targeting multiple G4s associated with multiple oncogenes is beneficial. The goal of the G4-mtQSAR project is to perform computational studies in a systematic way with the help of various machine learning and pharmacoinformatic methods to identify potential small lead molecules against G4 structures from various gene areas. Thus, the study aims at ‘stabilization of G4s with multi-target directed ligands (MTDL)’. Also, another major goal is to avail the automatic screening of potential G4 modulators by means of a completely novel drug discovery technological platform at MolDrug AI Systems SL company. Thus, the study delivers the first computational tool ‘G4-QuadScreen’ derived from a robust computational methodology with the functionality to screen out a library of small ligand molecules against G4 DNAs that are associated with cancer pathology. Based on the previous background, the objectives of G4-mtQSAR were the following: 1. To identify potential MTDLs acting on various G4s associated with multiple oncogenes and thus assist in finding effective targeted anticancer therapeutic agents. 2. To accelerate the search for new leads against G4s and to reduce the false positive outcomes in the crucial early stages of drug discovery and development by promoting the use of computational advanced tools, models, and data analysis for G4-activity prediction as an alternative to traditional assays.

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

Цел на проекта

The goal of the proposed research is to develop computational methodology which can screen out small ligand molecules having potential to selectively target G-quadruplex (G4) DNA which are associated with cancer pathology. Multiple oncogenes and telomerase activity are deregulated in various types of cancers. Ligand induced stabilization of G4s associated with the oncogenes (c-myc, c-kit, k-ras, etc.) and stabilization of telomeric G4s are efficient ways in targeted cancer therapy. Here, we intend to develop multi-target QSAR models that can aid in finding potential multi-target directed ligands (MTDLs) that can stabilize multiple G4s from different oncogenes simultaneously. It is the first computational study for identifying MTDLs against multiple G4s in Cancer treatment. Different multi-target QSAR approaches will be explored including ‘multiple regression and classification’ QSAR models, multi-target QSAR using Box-Jenkins moving average approach and multi-target QSAR using Perturbation approach. Several advanced machine learning techniques will be employed. A state-of-the-art software tool will be developed where all the successful multi-target QSAR models and in-house ADMET models will be incorporated as a knowledgebase. Notably, desirability-based multi-objective optimization approach will be used for identifying drug-like molecules. The software along with in-house KNIME workflows will then be used to screen potential MTDLs against multiple G4s. The selectivity and binding characteristics of the screened MTDLs towards G4s over duplex DNA will be analysed by performing molecular docking and molecular dynamics studies. The binding capacity of the screened MTDLs with intended G4s over duplex DNAs will then be confirmed using experiments such as isothermal fluorescence, UV-Vis, CD spectroscopies and FRET melting assay. The findings will aid in identifying new leads and reducing false positive outcomes in the crucial early stages of drug discovery and development.

Оригинален текст от CORDIS (на английски).

Участници

MOLDRUG AI SYSTEMS SL · ValenciaКоординаторИспания