INTEGRATIVE MODELLING FOR THE CHARACTERISATION OF THE STRUCTURE AND DYNAMICS OF BIOMOLECULES
type: CreativeWork
description: This work explores the benefits of integrating computational structural biology techniques with experimental data to overcome the inherent limitations of each approach. Integrative modelling provides a more comprehensive understanding of the structure, dynamics, and function of complex biomolecular systems. Experimental data can guide computational methods, improving accuracy, reducing limits and approximations, and validating results, while computational techniques inspire new experimental designs and provide explanations for observed phenomena. In this work, I have reported on some personal contributions that exemplify the application of computational techniques in the context of integrative modelling, with a particular focus on three main manuscripts. The first paper describes the development of a small-angle scattering model for the in silico reconstruction of scattering intensities from atomic coordinates during molecular dynamics (MD) simulations. This model can be coupled with restraining strategies, such as metainference, to generate conformational ensembles at atomistic resolution in agreement with the experimental data using MD simulations. This approach was used to determine the closed state conformations of the human gelsolin, a plasma protein. The second manuscript investigates the inactivation mechanism of the human olfactory receptor OR51E2. MD simulations revealed that calcium ions play a key role in stabilising the inactive state of the protein. This study integrates different computational techniques to propose a novel molecular mechanism of receptor inactivation and provides a rationale for future experimental validation. The third manuscript explores the molecular basis of a pale green phenotype in the barley population TM2490, linked to a mutation in magnesium chelatase subunit I, an enzyme involved in the chlorophyll synthesis pathway. AI-based structural modelling and molecular docking studies elucidate how this mutation might affect ATP bindi…
abstract: This work explores the benefits of integrating computational structural biology techniques with experimental data to overcome the inherent limitations of each approach. Integrative modelling provides a more comprehensive understanding of the structure, dynamics, and function of complex biomolecular systems. Experimental data can guide computational methods, improving accuracy, reducing limits and approximations, and validating results, while computational techniques inspire new experimental designs and provide explanations for observed phenomena. In this work, I have reported on some personal contributions that exemplify the application of computational techniques in the context of integrative modelling, with a particular focus on three main manuscripts. The first paper describes the development of a small-angle scattering model for the in silico reconstruction of scattering intensities from atomic coordinates during molecular dynamics (MD) simulations. This model can be coupled with restraining strategies, such as metainference, to generate conformational ensembles at atomistic resolution in agreement with the experimental data using MD simulations. This approach was used to determine the closed state conformations of the human gelsolin, a plasma protein. The second manuscript investigates the inactivation mechanism of the human olfactory receptor OR51E2. MD simulations revealed that calcium ions play a key role in stabilising the inactive state of the protein. This study integrates different computational techniques to propose a novel molecular mechanism of receptor inactivation and provides a rationale for future experimental validation. The third manuscript explores the molecular basis of a pale green phenotype in the barley population TM2490, linked to a mutation in magnesium chelatase subunit I, an enzyme involved in the chlorophyll synthesis pathway. AI-based structural modelling and molecular docking studies elucidate how this mutation might affect ATP bindi…
author: BALLABIO, FEDERICO
language: eng
keywords: Settore PHYS-06/A - Fisica per le scienze della vita, l'ambiente e i beni culturali
date published: 2024-11-29
date modified: 2026-07-27T20:18:48Z
provider: Università degli Studi di Milano
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli Studi di Milano

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2434/1118252

PropertyValue
property ID: DOI
value: 10.13130/ballabio-federico_phd2024-11-29