Mariona Torrens from the PhD Programme in Biomedicine and member of GRIB will receive the Doctoral School PhD Extraordinary Award

Mariona's thesis “Making protein dynamics FAIR: Research platforms for the collection, dissemination, and analysis of molecular dynamics simulations” was supervised by Dr. Jana Selent and Dr. Ferran Sanz

Mariona’s thesis “Making protein dynamics FAIR: Research platforms for the collection, dissemination, and analysis of molecular dynamics simulations” was supervised by Dr. Jana Selent and Dr. Ferran Sanz

Press release UPF 29/06/2023

The Special Award Committee that awarded the candidates was formed by:

Dr. Rubén Vicente (Biología Molecular i Cel·lular), Dr. Robert Castelo (Informàtica Biomèdica), Dra. Ana Janic (Biologia Molecular i Cel·lular),Dr. Francesc Calafell (Biologia Evolutiva i Sistemes Complexes),Dra Sara Sdelci (Biologia Molecular i Cel·lular),Dr. Marc Güell (Biologia Evolutiva i Sistemes Complexes),Dr. Mariano Sentí (Salut Pública i Educació en Ciències de la Salut),Dr. Núria Centeno ( Informàtica Biomèdica),Dr. Fernando García Benavides (Salut Pública i Educació en Ciències de la Salut).

The awarded student defended her thesis “Making protein dynamics FAIR: Research platforms for the collection, dissemination, and analysis of molecular dynamics simulations” on February 11, 2022.

Abstract: 

Molecular dynamics (MD) simulations are a widely established method for exploring the structural motions of biological systems at atomic resolution. This technique is able to resolve dynamic molecular mechanisms at time scales, structural resolution, and conditions that are often not accessible with experimental techniques. However, accessing, viewing, and sharing MD data is typically restricted by large file sizes and the need for specialized software, which limits the audience to which this data is available.

To maximize the potential of MD research, the data generated should be “Findable, Accessible, Interoperable, and Reusable”, following the FAIR principles for scientific data management. For that, this PhD thesis was dedicated to the design and development of two open-access online resources for the collection, dissemination, and analysis of MD simulations: GPCRmd (www.gpcrmd.org) [1] and SCoV2-MD (www.scov2-md.org) [2]. These resources are focused on two groups of proteins with particular pharmacological interest: respectively, G protein-coupled receptors (GPCRs), which are a major class of drug targets, and the proteome of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of the Coronavirus disease 2019 (COVID-19) pandemic.

The simulation data accumulated in GPCRmd and SCoV2-MD, together with the implemented online tools for the analysis and visualization of this data, simplify the exploration of protein dynamics and can help to shed light on the molecular mechanisms underlying GPCR and SARS-CoV-2 biology. To showcase the capabilities of our platforms, we presented several case studies in which we explore key aspects of protein dynamics. Particularly, using the simulation data stored in GPCRmd, we were able to provide unprecedented insights into the role of ions in GPCR modulation, a mechanism that is still poorly understood. Moreover, by applying the analysis tools, we captured water-mediated interactions that could be related to distinct mechanisms of GPCR signaling. Finally, the tools implemented in SCoV2-MD allowed us to pinpoint SARS-CoV-2 variant substitutions with potential impact on the function of a viral protein, which could affect the infectivity of the virus.

With the platforms presented in this thesis, we aim to take a step forward toward reproducibility and transparent dissemination in the field of MD simulations, which are key ingredients for collaborative and multidisciplinary research.

The author of this thesis shares the co-first authorship in the publication derived from GPCRmd [1]. She was responsible for the design and development of multiple sections of this platform, with a special focus on the visualization and analysis tools. She is the first author of the publication derived from SCoV2-MD [2].