Integrating computational techniques for enhanced understanding of protein-protein interactions and the human interactome

Protein-protein interactions (PPIs) are fundamental in driving biological processes and maintaining life’s diverse functions. They play a key role in cellular regulation, facilitating our understanding of both basic biological mechanisms and disease pathology. Researching PPIs is crucial for molecular-level insights, influencing drug development and therapeutic strategies for various diseases. In this thesis, we first present Galaxy InteractoMIX, a comprehensive computational platform that facilitates the study of PPIs. Additionally, the thesis introduces CM2D3, a method designed to enrich the human interactome with structural models of protein complexes. Finally we studied the NLRP3 complex, addressing its complex structure and providing insights into its role in immune response and showcased the adaptability of methodologies presented in the thesis for broader applications in studying protein complexes, particularly within the NLRP family.Les interaccions proteïna-proteïna (PPIs) són fonamentals per impulsar els processos biològics i mantenir les diverses funcions de la vida. Juguen un paper clau en la regulació cel·lular, facilitant la nostra comprensió tant dels mecanismes biològics bàsics com de la patologia de les malalties. La investigació de les PPIs és crucial per a obtenir una comprensió a nivell molecular, influenciant el desenvolupament de fàrmacs i les estratègies terapèutiques per a diverses malalties. En aquesta tesi, presentem primer Galaxy InteractoMIX, una plataforma computacional completa que facilita l’estudi de les PPIs. A més, la tesi introdueix CM2D3, un mètode dissenyat per enriquir l’interactoma humà amb models estructurals de complexos proteics. Finalment, hem estudiat el complex NLRP3, abordant la seva estructura complexa i proporcionant idees sobre el seu paper en la resposta immunitària. Aquest treball mostra l’adaptabilitat de les metodologies presentades en la tesi per a aplicacions més àmplies en l’estudi de complexos proteics, particularment dins de la família NLRP.Programa de Doctorat en Biomedicin

Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships

The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.Authors acknowledge support from MINECO grant numbers RYC2015-17519 and BIO2017-85329-R

CM2D3:Furnishing the Human Interactome with Structural Models of Protein Complexes Derived by Comparative Modeling and Docking

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community