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PirePred is a genetic interpretation tool used for a variety of medical conditions investigated in newborn screening programs. The PirePred server retrieves, analyzes, and displays in real time genetic and structural data on 58 genes/proteins associated with medical conditions frequently investigated in the newborn. PirePred analyzes the predictions generated by 15 pathogenicity predictors and applies an optimized majority vote algorithm to classify any possible nonsynonymous single-nucleotide variant as pathogenic, benign, or of uncertain significance. PirePred predictions for variants of clear clinical significance are better than those of any of the individual predictors considered (based on accuracy, sensitivity, and negative predictive value) or are among the best ones (for positive predictive value and Matthews correlation coefficient). PirePred predictions also outperform the comparable in silico predictions offered as supporting evidence, according to American College of Medical Genetics and Genomics guidelines, by VarSome and Franklin. Also, PirePred has very high prediction coverage. To facilitate the molecular interpretation of the missense, nonsense, and frameshift variants in ClinVar, the changing amino acid residue is displayed in its structural context, which is analyzed to provide functional clues. PirePred is an accurate, robust, and easy-to-use tool for clinicians involved in neonatal screening programs and for researchers of related diseases. The server is freely accessible and provides a user-friendly gateway into the structural/functional consequences of genetic variants at the protein level., Supported by 2014–2020 Interreg V-A Spain-France-Andorra (POCTEFA) grant EFA086/15, Mineco grant PID2019-107293GB-I00, and Gobierno de Aragón grants E45_17R and LMP30_18. H.G.-C. is the recipient of an FPU16/04232 doctoral contract from MCIN.

Trabajo presentado en el 3D-Bioinformatics 2020 Annual Workshop, celebrado online del 24 al 26 de noviembre de 2020, We describe here the application of pyDock [1] methodology for the scoring of protein-protein interfaces, which can be relevant for the Activity II of the ELIXIR 3D-BioInfo Community "Open resources for sharing, integrating and benchmarking software tools for modelling the proteome in 3D". A major goal of this 3D-BioInfo activity is the application of computational tools for the identification of physiological dimer interfaces and the correct description of functional oligomerization states of proteins and protein assemblies. Indeed, this is an important problem that needs to be targeted before attempting more ambitious structural interactomics projects, such as the structure- and docking-based impact of interface mutations in genes related to diseases detected by newborn screening programs [2]. The capabilities of pyDock for the scoring of modelled assemblies, as recently shown in the recent CASP13-CAPRI and 7th CAPRI rounds, prompted us to explore its application to the characterization of biologically relevant assembly modes. The scoring function of pyDock is composed of electrostatics, desolvation, and van der Waals energy terms (the latter weighted by 10%). This combination was optimized for the scoring of poses from ab initio docking, and seems also suitable for models generated by template-based docking. Here we will apply this function to the proposed benchmark of dimer interfaces, and we will also discuss the performance of each individual pyDock energy term and their different combinations on the discrimination of physiological and non-physiological dimer interfaces. In addition, pyDock binding energy can be easily partitioned per residue, as implemented in the pyDockEneRes web server (https://life.bsc.es/pid/pydockeneres/) [3], which can provide additional information to further characterize the benchmark of dimer interfaces. [1] Cheng TM, Blundell TL, Fernandez-Recio J (2007) Proteins 68, 503¿515. [2] Navío D, Rosell M, Aguirre J, de la Cruz X, Fernánde-Recio J (2019) Int J Mol Sci 20, 1583. [3] Romero-Durana M, Jiménez-García B, Fernández-Recio J (2020) Bioinformatics 36, 2284-2285.

Mutation of a single amino acid in a protein often has consequences on the interaction with other proteins, which may affect other interaction networks and pathways and ultimately lead to pathological phenotypes. A detailed structural analysis of these altered protein–protein complexes is essential to interpret the impact of a given mutation at the molecular level, which may facilitate intervention with therapeutic purposes. Given current limitations in the structural coverage of the human interactome, computational docking is emerging as a complementary source of information. Structural analysis can help to locate a given mutation at a protein–protein interface, but further characterisation of its impact on binding affinity is needed for a full interpretation. The integration of computational docking methods and energy‐based descriptors is facilitating the characterisation of an increasing number of disease‐related mutations, thus improving our understanding of the consequences of such mutations at the phenotypic level.

Trabajo presentado en 14th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction CASP14, celebrado online entre los meses de mayo y septiembre de 2020, In the past 3rd common CASP-CAPRI Assembly Prediction challenge, our modeling approach, integrating ab initio docking, template-based modeling, distance-based restraints, low-resolution structural data and symmetry constraints, yielded excellent performance, ranking 2nd among CAPRI predictors, and 1st among CAPRI scorers1. Here we describe our participation in the CASP14 Assembly category, as part of the 4th common CASP-CAPRI Assembly Prediction challenge (CAPRI Round 50). We have participated as human predictors, human scorers, and server scorers, in all the 18 proposed targets, consisting in four hetero-dimers (A1B1), six homodimers (A2), two homo-trimers (A3), two homo-tetramers (A4), one hetero-nonamer (A3B3C3), one homo-20mer (A20), one hetero-27mer (A6B3C12D6), and one homo-240mer (A240).

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.

L'ictus representa una de les principals causes de discapacitat en l'adult, comportant una disminució del nivell d'independència de la persona i la qualitat de vida. La Realitat Virtual (RV) és una teràpia que utilitza la tecnologia per a simular un entorn real generat per un ordinador, permetent la interacció i immersió de la persona dins un ambient simulat, mentre es rep un feedback sensorial multimodal. L'objectiu d'aquesta revisió sistemàtica és comprovar l'evidència de la realitat virtual en la rehabilitació funcional de l'extremitat superior en pacients que han patit un ictus, en la fase crònica de la malaltia Stroke is one of the leading causes of disability in adults, decreasing independence levels and quality of life. Virtual Reality (VR) is a therapy that uses technology to simulate a real environment created by a computer, allowing interaction and immersion of the person into a virtual simulated environment, getting a sensorial and multimodal feedback. The aim of this systematic review is to check the evidence of the virtual reality therapy for the functional rehabilitation of the upper limb in chronic stroke patients.

This paper presents an Italian to 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. Aquest article presenta un sistema de traducció automàtica basat en regles de l'italià al català construït automàticament combinant les dades lingüístiques dels parells espanyol-català i espanyol-italià existents. Es duu a terme un postprocessament manual superficial per a corregir incoherències en els diccionaris derivats automàticament i per a afegir-hi paraules molt freqüents que no hi són d'acord amb una anàlisi del corpus. El sistema s'avalua en el corpus KDE4 i supera Google Translate aproximadament per deu punts absoluts tant pel que fa al TER (índex d'edició de traducció) com pel que fa al GTM (mètode de traducció gramàtica). Este artículo presenta un sistema de traducción automática basado en reglas del italiano al catalán construido mediante la combinación de datos lingüísticos de los pares existentes español-catalán y español-italiano. Se lleva a cabo un postprocesamiento manual superficial para corregir incoherencias en los diccionarios derivados automáticamente y para añadir palabras muy frecuentes que no están en ellos según un análisis del corpus. El sistema se evalúa en el corpus KDE4 y supera a Google Translate aproximadamente por diez puntos absolutos tanto por lo que respecta al TER (índice de edición de traducción) como por lo que respecta al GTM (método de traducción gramática).

Este artículo presenta un sistema de traducción automática libre (de código abierto) basado en reglas entre euskera y castellano, construido sobre la plataforma de traducción automática Apertium y pensado para la asimilación, es decir, como ayuda a la comprensión de textos escritos en euskera. Se describe el desarrollo y la situación actual y se muestra una evaluación de la calidad de las traducciones. This paper presents a free (or open-source) rule-based machine translation system between Basque and Spanish, based on the Apertium machine translation platform aimed at assimilation, that is, as a help for the understanding of texts written in Basque. The development process and current status are described and an evaluation is given of the utility of the output. Development was supported and funded by Prompsit Language Engineering S.L. and the Universitat d’Alacant.

Uno de los principales retos de la informática para las próximas décadas es el desarrollo de sistemas capaces de procesar eficazmente el lenguaje natural (o lenguaje humano). Dentro de este campo, los sistemas de traducción automática, encargados de traducir un texto escrito en un idioma a una versión equivalente en otro idioma, reciben especial atención dado, por ejemplo, el carácter multilingüe de sociedades como la europea. La automatización de dicho proceso es particularmente compleja porque los programas han de enfrentarse a características del lenguaje natural, como la ambigüedad, cuyo tratamiento algorítmico no es factible, de modo que una mera aproximación o automatización parcial del proceso ya se considera un éxito. Los programas de traducción automática han sido tradicionalmente sistemas cerrados, pero en los últimos tiempos la tendencia marcada por el software libre ha llegado también a este campo. En este artículo describimos Apertium, apertium.org, una plataforma avanzada de código abierto, con licencia GNU GPL, que, gracias al desacoplamiento que ofrece entre datos y programas permite desarrollar cómodamente nuevos traductores automáticos. La plataforma Apertium ha sido desarrollada por el grupo de investigación Transducens de la Universitat d’Alacant en el marco de varios proyectos de colaboración con universidades y empresas de España en los que, además de los programas que conforman el motor de traducción, se han confeccionado datos lingüísticos abiertos para la traducción automática catalán–español, gallego–español, portugués–español, francés–catalán, inglés–catalán y occitano–catalán. Tanto la plataforma en la que se integra el motor de traducción como los datos para estos pares de lenguas están disponibles para su descarga en sf.net/projects/apertium/ y para su evaluación en línea en xixona.dlsi.ua.es/prototype/. Este trabajo ha sido parcialmente subvencionado por el Ministerio de Industria, Comercio y Turismo a través de los proyectos FIT-340101-2004-3, FIT-340001-2005-2 y FIT-350401-2006-5, por el Ministerio de Educación y Ciencia a través de los proyectos TIC2003-08681-C02-01 y TIN2006-15071-C03-01, y por la Generalitat de Catalunya a través del proyecto DURSI1-05I. Felipe Sánchez-Martínez disfruta de la ayuda para la formación de personal investigador BES-2004-4711, financiada por el Fondo Social Europeo y el Ministerio de Educación y Ciencia.

By the time Machine Translation Summit X is held in September 2005, our group will have released an open-source machine translation toolbox as part of a large government-funded project involving four universities and three linguistic technology companies from Spain. The machine translation toolbox, which will most likely be released under a GPL-like license includes (a) the open-source engine itself, a modular shallow-transfer machine translation engine suitable for related languages and largely based upon that of systems we have already developed, such as interNOSTRUM for Spanish—Catalan and Traductor Universia for Spanish—Portuguese, (b) extensive documentation (including document type declarations) specifying the XML format of all linguistic (dictionaries, rules) and document format management files, (c) compilers converting these data into the high-speed (tens of thousands of words a second) format used by the engine, and (d) pilot linguistic data for Spanish—Catalan and Spanish—Galician and format management specifications for the HTML, RTF and plain text formats. After describing very briefly this toolbox, this paper aims at exploring possible consequences of the availability of this architecture, including the community-driven development of machine translation systems for languages lacking this kind of linguistic technology. The development of the toolbox is funded by project FIT-340101-2004-3 (Spanish Ministry of Industry, Commerce and Tourism).