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BAP Report Summary

Project ID: 659025
Funded under: H2020-EU.1.3.2.

Periodic Reporting for period 1 - BAP (A dynamical view of binding affinity)

Reporting period: 2015-10-01 to 2017-09-30

Summary of the context and overall objectives of the project

The human body is a complex machine regulated by thousands of proteins that, like pieces of a puzzle, match together to complete the full picture. For this reason, the study of proteins properties, such as their structure and complementarity nature, is at the basis of our understanding for every biological process happening in cells. Similarly of missing or wrong pieces in a puzzle, perturbations in such precise protein matching system is often the principal cause of disease.
For all these reasons, the study and understanding of interaction processes between proteins (i.e., protein-protein complexes) is of fundamental importance for progress. Protein-protein complexes (PPC) can be studied under many different aspects, and one of the most important is their binding affinity. The binding affinity of a PPC is the quantity that define whether or not complex formation occurs, thereby determining its biological relevance. It is therefore a key quantity for understanding and predicting association and dysfunction phenomena. In this scenario, modulating the binding affinity offers great opportunities to control interactions and design innovative therapeutics.

My Marie Sklodowska-Curie Individual Fellowship has been focused on the study and prediction of binding affinities in biomolecular complexes, which accurate prediction was still a big challenge in the field. With my research, I could developed a binding affinity prediction that currently outperform any methods propose to date and successfully applying it to protein-ligand complexes as well. The outcomes of those results can be of large and important impact. Fields such as drug design, protein engineering, computational mutagenesis and docking can all benefit from a reliable method to predict binding. In particular, over the past years there is an increasing trend of the pharmaceutical companies towards rational drug design instead of random trials, to cut costs and maximize the percentage of successful drugs developed. My new and accurate approach to predict binding affinity in protein-protein and protein-ligand complexes can be of great interest for companies for the development and design of new drugs.

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, I achieved outcomes on different aspects and promoted their dissemination.

I proposed a binding affinity predictor for protein-protein complexes that currently outperforms any predictor propose to date (in Vangone and Bonvin, eLife 2015). Due to success and impact of this predictor on the field, I also worked on the implementation of the predictor in a web-server, PRODIGY, freerly available to the scientific community (published in Xue, Rodriguez, Kastriti, Bonvin, Vangone, Bioinformatics 2016).
My web-server has already produced an high impact in the field: over the first year from its publication, already +9000 jobs have been successful submitted to PRODIGY, with users spread all around the world (Data obtained through Google Analytics).
I also received an invitation to write a easy-to-use protocol of PRODIGY, published in Vangone & Bonvin, BioProtocols 2017, and an invitation from BioExcel to provide a webinar, which has been recorded and made available on their YouTube channell:

At the same time, I have been working on the application of PRODIGY to other systems then protein-protein complexes. Within the project of one of the master students I supervised, we showed that PRODIGY reaches good performance also in the prediction of binding affinity for protein-ligands. This new function, named PRODIGY-lig, has been used by our team to participate in the blind challenge D3R, placing us at the 7th place in terms of performance for the binding affinity prediction round, although our experience in the field in only at the beginning. This work has resulted in the peer-reviewed publication Kurkoglu et al. 2017.

I have been active part of the CAPRI challenge together with my research group, in which we participate with the HADDOCK software. HADDOCK had been the top-performing software for ranking over one of the latests CAPRI round, and I had been in charge of writing the paper we published in regards to our scoring function in Vangone et al. Protein 2016.

I have been collaborating with Dr. Hans der Vliet (VUmc Amsterdam), in regard to the modelling of TCR receptor and nanobodies. The collaboration has resulted in the publication: de Bruin, Stam, Vangone, van Bergen, Verheul, Bonvin, de Gruijl, van der Vliet, Journal of Immunology 2016.

I worked in the realization of a new protein-protein docking benchmark and the manuscript has been accepted in Journal of Molecular Biology as: Vangone, Vreven, Moal, [...] Fernandez-Recio, Bonvin, Weng ""Updates to the integrated protein-protein interaction benchmark: Docking benchmark version 5 and affinity benchmark version 2"", JMB 19, 3031-3041 (2015).

Being active part of the CAPRI challenge (the main challenge in the docking field) with my research group with the HADDOCK software, I have been contributed to the research article Lensink,[...], Vangone, [...], Wodak ""Prediction of homo- and hetero-protein complexes by ab-initio and template-based docking: a CASP-CAPRI experiment”, 2016. 
I have published a chapter for the 2nd edition of ""From Protein Structure to Function with Bioinformatics"" that is now in press as: Vangone, Oliva, Cavallo, Bonvin ""Prediction of Biomolecular Complexes"".

From the work of the PhD I daily supervise, we obtained interesting results about the impact of experimental methods on the quality of the data and the predictive power of those data. The manuscript has been accepted to PEDS journal as: Geng, Vangone, Bonvin ""Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes"".

Besides the dissemiation of my research outcome through peer-reviewed publications and a web-server, I actively participated to international conferences (5 conferences with oral contribution and other 5 with a poster presentations). I have also been involved in givig tutorials, reviewing manuscripts"

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)

A new and fast approach to predict the binding affinity in protein-protein complexes is the most important innovation I introduced in the field, which currently outperforms any method propose to date. Recently, I have been applied the same original approach to a different class of biological complexes, the protein-ligand ones, with immediate great results. Overall, the availability of such an approach to predict binding affinity paves the way for rational drug design. In fact, protein-ligand complexes are the main target for drug development. Companies are moving towards fast and cheap computational design and screening of drugs. In this scenario, my fast and highly performing method can soon take on a leading role.

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