Biology subjects

Oliva, B.

Publications and source records attributed to Oliva, B..

3 recordsLinked to original sources

bioRxiv2018

Alternative interaction sites in the influenza A virus nucleoprotein mediate viral escape from the importin-α7 mediated nuclear import pathway

Influenza A viruses are able to adapt to restrictive conditions due to their high mutation rates. Here, we addressed the question by which mechanisms influenza A viruses may escape restriction by the cellular importin-7 protein, a component of the nuclear import machinery required for avian-mammalian adaptation and replicative fitness in human cells. Therefore, we assessed viral evolution in mice lacking the importin-7 gene. Here, we show that particularly three mutations occur with high frequency in the viral NP protein (G102R, M105K and D375N) in a specific structural area upon in vivo adaptation. Moreover, our findings suggest that the adaptive NP mutations mediate viral escape from importin-7 requirement likely due to the utilization of alternative interaction sites in NP beyond the classical nuclear localization signal and importin- isoforms. However, viral escape from importin-7 is, at least in part, associated with reduced replicative fitness in human cells.

molecular biology↗

bioRxiv2018

RADI (Reduced Alphabet Direct Information): Improving execution time for direct-coupling analysis

MotivationDirect-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. Current algorithms for DCA, although efficient, have a high computational cost of determining Direct Information (DI) values for large proteins or domains. In this paper, we present RADI (Reduced Alphabet Direct Information), a variation of the original DCA algorithm that simplifies the computation of DI values by grouping physicochemically equivalent residues.\n\nResultsWe have compared the first top ranking 40 pairs of DI values and their closest paired contact in 3D. The ranking is also compared with results obtained using a similar but faster approach based on Mutual Information (MI). When we simplify the number of symbols used to describe a protein sequence to 9, RADI achieves similar results as the original DCA (i.e. with the classical alphabet of 21 symbols), while reducing the computation time around 30-fold on large proteins (with length around 1000 residues) and with higher accuracy than predictions based on MI. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure, having a relevant and useful predictive value, while the computation time is reduced between 100 and 2500-fold.\n\nAvailabilityRADI is available at https://github.com/structuralbioinformatics/RADI\n\nContactbaldo.oliva@upf.edu\n\nSupplementary informationSupplementary data is available in the git repository.

bioinformatics↗

bioRxiv2018

Targeting comorbid diseases via network endopharmacology

The traditional drug discovery paradigm has shaped around the idea of \"one target, one disease\". Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to \"endopharmacology\" (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimers disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.

systems biology↗