HEИндивидуална стипендия2023–2025

CryoLigate · Characterizing ligand-protein interactions with a cryo-EM data-driven modeling approach

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

Период: 2023-11-01 → 2025-10-31
Финансиране от ЕС: 222 728 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Взаимодействията между протеини и лиганди, например при мембранните протеини, се анализират чрез съчетаване на криоелектронна микроскопия и компютърно моделиране. Това помага за по-точното проектиране на нови лекарства чрез разбиране на структурата на тези молекулярни комплекси.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Characterizing ligand-protein interactions with a cryo-EM data-driven modeling approach

During the CryoLigate fellowship, I successfully visualized protein–ligand interactions at atomic detail, a key step in understanding how ligands regulate macromolecular function. The acquired knowledge has direct implications for structure-based drug discovery (SBDD) by providing accurate structural frameworks for rational drug design. Traditionally, experimental determination of protein–ligand complex structures using X-ray crystallography has been limited by the need to obtain suitable crystals. In this project, I overcame these limitations by leveraging recent advances in single-particle cryo-electron microscopy (cryo-EM), which now enables near-atomic resolution imaging of complex biomolecular assemblies. However, cryo-EM density maps often suffer from low-resolution ligand regions, which limits their direct use for SBDD. To address this, I developed and implemented a cryo-EM–guided computational modeling framework that integrates molecular dynamics (MD) simulations, machine learning, and refinement algorithms to accurately model ligand conformations in low-resolution EM densities. This novel computational approach was tested on a large dataset of protein–ligand complexes and successfully applied to membrane proteins, allowing identification of ligand binding sites and elucidation of how ligand binding regulates the functional energy landscape of target proteins. The outcomes of this work provide a new methodological pipeline that bridges cryo-EM data with computational chemistry, offering a practical route toward accurate modeling of ligand–protein complexes for drug discovery applications. The project has therefore opened new avenues for SBDD and strengthened my expertise in machine learning, molecular dynamics simulations, cryo-EM data processing, membrane protein dynamics, and drug–protein interaction modeling.

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

Цел на проекта

Visualizing protein-ligand interactions at atomic details is key to understand how ligands regulate macromolecular function. This knowledge could be leveraged to develop pharmaceutics utilizing the structure-based drug discovery platform. However, determining experimental structures of such complexes is often difficult using theVisualizing protein-ligand interactions at atomic details is key to understand how ligands regulate macromolecular function. This knowledge could be leveraged to develop pharmaceutics utilizing the structure-based drug discovery (SBDD) platform. However, determining experimental structures of such complexes is often difficult using the traditional time-consuming approach of hunting for suitable crystals for X-ray analysis. Recent breakthroughs in single-particle cryo-EM have overcome this limitation and enabled us to obtain atomic resolution structures of complex biomolecular systems. Though cryo-EM can now provide very high-resolution data of the overall system (less than 2 angstroms in many cases), unfortunately, resolutions of ligands are often significantly low to be useful for SBDD. Parallel to developments in cryo-EM, computational methods for modeling and refining structures into EM maps have been developed, but their main focus has been to build accurate protein structures. Here, I propose to exploit the increased computing power of molecular dynamics simulations offered by high-performance computing and algorithm development to develop a cryo-EM data-driven computational modeling approach to fit ligands into low-resolution EM maps. After testing in a large data set, this approach will be applied to identify ligand binding sites in new EM maps of a membrane protein and investigate how binding regulates the functional landscape of proteins. The findings of this proposal could open new avenues in the drug design platform by leveraging the power of cryo-EM and computational chemistry to accurately model ligand-protein complex structures.

Оригинален текст от CORDIS (на английски).

Участници

KUNGLIGA TEKNISKA HOEGSKOLAN · StockholmКоординаторШвеция