Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

An Italian to Catalan RBMT system reusing data from existing language pairs

This paper presents an Italian! Catalan RBMT system automatically built by combining the linguistic data of the existing pairs Spanish–Catalan and Spanish–Italian. A lightweight manual postprocessing is carried out in order to fix inconsistencies in the automatically derived dictionaries and to add very frequent words that are missing according to a corpus analysis. The system is evaluated on the KDE4 corpus and outperforms Google Translate by approximately ten absolute points in terms of both TER and GTM

Affects and the creative process: intra-views with cultural workers

Delving deeper into the creative process becomes quite a challenge, which goes beyond following some guidelines and at the end of these creating something. The creative process is a complex process resulting from the dialogue that occurs with everything that surrounds us and the connection with human, non-human and more than human forces, the affects, can allow us to access even the most intangible but often of great relevance in this process. Therefore, the creative affective process aims to awaken the importance of connection with the forces that cross us, as well as sharing attitudes and skills that facilitate our connection with what moves us

Characteritzation of protein-protein interfaces and identification of transient cavities for its modulation

Protein-protein interactions (PPIs) play an essential role in many biological processes, including disease conditions. Strategies to modulate PPIs with small molecules have therefore attracted increasing interest over the last few years, where successful PPI inhibitors have been reported into transient cavities from previously flat PPIfs. Recent studies emphasize on hot-spots (those residues contribute for most of the energy of binding) as promising targets for the modulation of PPI. PyDock is the only computational method that uses docking to predict PPIfs and hot-spots (HS) residues. Using Normalized Interface Propensity (NIP) values derived from rigid-body protein docking simulation, we are able to predict the PPIfs and HS residues without any prior structural knowledge of the complex. We benchmarked the protocol in a small set of protein-protein complexes for which both structural data and PPI inhibitors are known. We present an approach aimed at identifying HS and transient pockets from predicted PPIfs in order to find potential small molecules capable of modulating PPIs. The method uses pyDock to identify PPIfs and HS and molecular dynamics (MD) techniques to describe the possible fluctuations of the interacting proteins in order to suggest transient pockets. Afterwards, we evaluated the validity of predicted HS and pockets for in silico drug design by using ligand docking. We present a strategy based on MD and NIP which allows to identify cavities as potentially good targets to bind inhibitors when there is no information at all about the protein-protein complex structure

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

La traducció automàtica en la pràctica: aplicacions, dificultats i estratègies de desenvolupament

En aquest article es descriuen els sistemes de traducció automàtica, les seves aplicacions actuals i les principals dificultats que ha d’afrontar aquesta tecnologia lingüística. Es presenta el sistema Apertium, una plataforma de traducció automàtica de codi obert sobre la qual s’han construït diversos traductors automàtics entre diferents parells d’idiomes, en els quals està inclòs el català. Basant-se en l’experiència dels autors, es descriuen algunes tensions que es donen en el desenvolupament de les dades lingüístiques d’un traductor automàtic i les solucions de compromís a què cal arribar per a construir sistemes útils

Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces

Computational docking; Interface prediction; Protein-protein interactionsAcoblament molecular computacional; Predicció d'interfícies; Interaccions proteïna-proteïnaAcoplamiento molecular computacional; Predicción de interfaces; Interacciones proteína-proteínaOne of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments.This research was funding by the EU European Regional Development Fund (ERDF) through the Program Interreg V-A Spain-France-Andorra (POCTEFA), by the CSIC (intramural grant number 201720I031), and by the Spanish Ministry of Economy and Competitiveness (grants BIO2016-79930-R and SAF2016-80255-R). M.R. is recipient of an FPI fellowship from the Severo Ochoa program

Hormonoterapia primaria en mujeres añosas con cáncer de mama localizado hormonosensible

Se analizó de forma retrospectiva una serie de pacientes con una edad mediana de 79 años con cáncer de mama localizado, con receptor de estrógeno positivo tratadas con hormonoterapia primaria. Tras recibir el tratamiento primario las pacientes candidatas se sometían a cirugía. La respuesta clínica fue de un 63.6%. La mediana de tiempo a progresión fue de 94 meses y la mediana de la supervivencia global no se alcanzó, siendo la media de 123 meses. Se evaluó el impacto del tratamiento quirúrgico en estos resultados, no objetivándose diferencias estadísticamente significativas. La hormonoterapia exclusiva en casos seleccionados es efectiva y seguraEs va analitzar de forma retrospectiva una sèrie de pacients amb una edat mitjana de 79 anys amb càncer de mama localitzat, amb receptor d'estrogen positiu tractades amb hormonoteràpia primària. Després de rebre el tractament primari les pacients candidates es van sotmetre a cirurgia. La resposta clínica va ser del 63.6%. La mitjana de temps a progressió va ser de 94 mesos i la mitjana de la supervivència global no es va assolir, essent la mitja de 123 mesos. Es va avaluar l'impacte de la cirurgia en aquests resultats, sense trobar-se diferències estadísticament significatives. La hormonoteràpia exclusiva en casos seleccionats és efectiva i segura