Membrane proteins – development of new computational approaches and its application to G-Protein Coupled Receptors

Project Information

MEMBRANEPROT

Grant agreement ID: 659826

DOI

10.3030/659826

Project closed

EC signature date 23 March 2015

Start date 1 March 2016

End date 28 February 2018

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 177 598,80

EU contribution

€ 177 598,80

Coordinated by

UNIVERSITEIT UTRECHT
Netherlands

Periodic Reporting for period 1 - MEMBRANEPROT (Membrane proteins – development of new computational approaches and its application to G-Protein Coupled Receptors)

Reporting period: 2016-03-01 to 2018-02-28

Summary of the context and overall objectives of the project

The identification of protein complexes and interactions is key for the understanding of cellular organization and machinery. Due to the challenges of obtaining sufficient experimental data about those interactions, computational tools and methodologies are emerging as reliable alternatives. It is especially true that machine-learning algorithms hold an astonishing potential for studying protein interactions by enabling the identification of biologically relevant patterns, which accelerates our knowledge of the functional mechanism of proteins within cells. My Marie Sklodowska-Curie Individual Fellowship have been focused on the development and application of computer modelling techniques that went beyond the current state-of-the-art, leading to quantitative and reliable molecular-level predictions of hot-spots at protein-protein complexes, protein-protein interfaces and membrane protein interface. These new predictors and tools were applied to the understanding of the physicochemical, structural and dynamic properties of GPCR/G-proteins and GPCR/Arrestin complexes. The results illuminated essential mechanisms of GPCR selectivity from a new perspective including dynamic mechanisms and yielded novel methods and approaches to serve in the study of membrane protein systems and their functional mechanisms.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During these 2 years of project, various results were obtained and disseminated.

I proposed a new hot-spot predictor for protein-protein complexes that outperforms any predictor propose to date (Melo et al., IJMS, 2016; Moreira et al., Sci Reports 2017). This new predictor was implemented at a web-server (https://milou.science.uu.nl/ services/SPOTON) that already counts more than 140 users and 5923 runs since its publication. Through a big-data approach I provided also insights into PPIs chemical, physical and structural properties, which was assembled in a new web-platform.

Our proposed ground-breaking protocols were made (and will be made) freely available to the scientific community through my source control repositories (e.g. GitHub). Our user-centric web applications and software pipeline are also open to any interested party upon request. Besides publication in specialized renowned scientific journals (6 peer-reviewed SCI-indexed publications with 3 more in submission and 5 more in preparation), publication of 3 book chapters and 7 conference procedings, as well as 3 web-servers/platforms, dissemination activities also involved editorial activities of two special issues: i) “GPCR Mechanism and Drug Design” for Molecules Journal and ii) “Modulation of protein-protein Interactions for Development of Effective Therapeutics – from a Joint Perspective of Experiment and Computation” for Current Topics of Medicinal Chemistry. I had personally continued to organize EJIBCE - “Encontro de Jovens Investigadores de Biologia Computacional e Estrutural”, a Portuguese national meeting whose principal objective is to foster collaboration in computational bioinformatics and is especially directed towards younger generations of researchers and their interaction with established and renowned field experts. Apart from the meeting’s merits, I had also pursued this communication channel as another avenue to advertise our project to the broader scientific community. A few conference proceedings were published under these initiative at MOL2NET-03 International Conference Series on Multidisciplinary Sciences. I had also attended other major international scientific conferences/meetings/workshops (e.g. Biophysical Meeting, EMBO workshop) as an invited speaker. Such events provided opportunities for consulting with renowned experts face-to-face. I had continued to interact with the general public through web and social media presence (e.g. LinkedIn, Researchgate, Twitter etc.), and our up-to-date of personal website. I have also supervised master, PhD and Postdoctoral researchers, gave 3 workshops and lectures at 2 Master and 1 PhD programs, sucessufly applied to 3 grants calls and reviewed manuscripts for a variety of journals.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

During this period, we have used a multidisciplinary approach combining information from genomic (sequence) to chemistry (structure) data sources about properties and characteristics of protein-protein interfaces (PPIs), powered by artificial intelligence-based prediction algorithms. This resulted in a new top-performance predictor for the most crucial residues at a PPI: http://milou.science.uu.nl/services/SPOTON. We also provided insights into PPIs chemical, physical and structural properties, which were assembled in an interactive platform and applied a large scale study to understand the specificity at dopamine family of receptors. All curated datasets and predictions achieved during project execution are or will be made available to the general public; all delivered software solutions will be regularly updated to include new data, algorithms and features. This project results will undoubtedly impact Biotechnology/Pharmaceutical industry as it provided new methodologies and new dynamical and structure understanding of key biological targets: G-protein coupled receptors.

HS detection at GPCRs interfaces