Structural and dynamics analysis of pyruvate kinase from erythrocytes: implications in pathology

Our current study revolves around the dynamic characterization of the human erythrocyte pyruvate kinase (RPYK). The deficiency of this protein is a common cause of nonspherocytic hemolytic anemia, a rare, autosomal recessive disease. We plan to perform a comprehensive set of molecular dynamics simulations of both the wildtype (WT) and mutant variants of R-PYK in different conditions, in order to explore the dynamic behavior of the enzyme, describe the function and allosteric mechanism in terms of its dynamics fingerprint and identify altered dynamic behavior of the known pathogenic variants of the enzyme

BioSWR - Semantic Web services Registry for Bioinformatics

Article About the Authors Metrics Comments Related Content Abstract Introduction Functionality Implementation Discussion Acknowledgments Author Contributions References Reader Comments (0) Figures Abstract Despite of the variety of available Web services registries specially aimed at Life Sciences, their scope is usually restricted to a limited set of well-defined types of services. While dedicated registries are generally tied to a particular format, general-purpose ones are more adherent to standards and usually rely on Web Service Definition Language (WSDL). Although WSDL is quite flexible to support common Web services types, its lack of semantic expressiveness led to various initiatives to describe Web services via ontology languages. Nevertheless, WSDL 2.0 descriptions gained a standard representation based on Web Ontology Language (OWL). BioSWR is a novel Web services registry that provides standard Resource Description Framework (RDF) based Web services descriptions along with the traditional WSDL based ones. The registry provides Web-based interface for Web services registration, querying and annotation, and is also accessible programmatically via Representational State Transfer (REST) API or using a SPARQL Protocol and RDF Query Language. BioSWR server is located at http://inb.bsc.es/BioSWR/and its code is available at https://sourceforge.net/projects/bioswr/​under the LGPL license

PMut2: a web-based tool for predicting pathological mutations on proteins

Amino acid substitutions in proteins can result in an altered phenotype which might lead to a disease. PMut2 is a method that can predict whether a mutation has a pathological effect on the protein function. It uses current machine learning algorithms based on protein sequence derived information. The accuracy of PMut2 is as high as 82%, with a Matthews correlation coefficient of 0,62. PMut2 predictions can be obtained through a modern website which also allows to apply the same machine learning methodology that is used to train PMut2 to custom training sets, allowing users to build their own tailor-made predictors

The multiple roles of waters in protein solvation

Extensive molecular dynamics (MD) simulations have been used to characterize the multiple roles of water in solvating different types of proteins under different environmental conditions. We analyzed a small set of proteins, representative of the most prevalent meta-folds under native conditions, in the presence of crowding agents, and at high temperature with or without high concentration of urea. We considered also a protein in the unfolded state as characterized by NMR and atomistic MD simulations. Our results outline the main characteristics of the hydration environment of proteins and illustrate the dramatic plasticity of water, and its chameleonic ability to stabilize proteins under a variety of conditions

Metadata to describe genomic information

Interoperable metadata is key for the management of genomic information. We propose a flexible approach that we contribute to the standardization by ISO/IEC of a new format for efficient and secure compressed storage and transmission of genomic information.Peer ReviewedPostprint (published version

Web-based tool for the annotation of pathological variants on proteins: PMut 2017 update

Assessing the impact of amino acid mutations in human health is an important challenge in biomedical research. As sequencing technologies are more available, and more individual genomes become accessible, the number of identified variants has dramatically increased. PMut, released back in 2005 [1], has been one of the popular predictors in this field. PMut was a neural-network-based classifier using sequence data to provide a pathology score for point mutations in proteins. We now release a new, revised, and much more powerful version of PMut. It features PyMut prediction engine, a Python module that includes numerous machine learning capabilities aimed at the analysis of protein variant pathology annotation. We also release PMut2017 predictor, a full update of the PMut predictor based on the SwissVar [2] variation database. It achieves an accuracy of 82% and a Matthews Correlation Coefficient (MCC) of 0.62, and matches the most popular predictors’ performance. The engine is implemented in Python using MongoDB engine for data management. It has been adapted to run at the HPC level to cover large scale annotation projects

Exploring the early stages of chemical unfolding of proteins at the proteome scale

After decades of using urea as denaturant, the kinetic role of this molecule in the unfolding process is still undefined: does urea actively induce protein unfolding or passively stabilize the unfolded state? By analyzing a set of 30 proteins (representative of all native folds) through extensive molecular dynamics simulations in denaturant (using a range of force-fields), we derived robust rules for urea unfolding that are valid at the proteome level. Irrespective of the protein fold, presence or absence of disulphide bridges, and secondary structure composition, urea concentrates in the first solvation shell of quasi-native proteins, but with a density lower than that of the fully unfolded state. The presence of urea does not alter the spontaneous vibration pattern of proteins. In fact, it reduces the magnitude of such vibrations, leading to a counterintuitive slow down of the atomic-motions that opposes unfolding. Urea stickiness and slow diffusion is, however, crucial for unfolding. Long residence urea molecules placed around the hydrophobic core are crucial to stabilize partially open structures generated by thermal fluctuations. Our simulations indicate that although urea does not favor the formation of partially open microstates, it is not a mere spectator of unfolding that simply displaces to the right of the folded←→unfolded equilibrium. On the contrary, urea actively favors unfolding: it selects and stabilizes partially unfolded microstates, slowly driving the protein conformational ensemble far from the native one and also from the conformations sampled during thermal unfolding

Bioactive conformational ensemble server and database. A public framework to speed up in silico drug discovery.

Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the Bioactive Conformational Ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all the steps to transform 1D or 2D representation of drugs into three dimensional molecules, which are then titrated, parametrized, hydrated and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data is organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BC

How B-DNA dynamics decipher sequence-selective protein recognition

The rules governing sequence-specific DNA-protein recognition are under a long-standing debate regarding the prevalence of base versus shape readout mechanisms to explain sequence specificity and of the conformational selection versus induced fit binding paradigms to explain binding-related conformational changes in DNA. Using a combination of atomistic simulations on a subset of representative sequences and mesoscopic simulations at the protein-DNA interactome level, we demonstrate the prevalence of the shape readout model in determining sequence-specificity and of the conformational selection paradigm in defining the general mechanism for binding-related conformational changes in DNA. Our results suggest that the DNA uses a double mechanism to adapt its structure to the protein: it moves along the easiest deformation modes to approach the bioactive conformation, while final adjustments require localized rearrangements at the base-pair step and backbone level. Our study highlights the large impact of B-DNA dynamics in modulating DNA-protein binding

BIGNASim: A NoSQL database structure and analysis portal for nucleic acids simulation data

Molecular dynamics simulation (MD) is, just behind genomics, the bioinformatics tool that generates the largest amounts of data, and that is using the largest amount of CPU time in supercomputing centres. MD trajectories are obtained after months of calculations, analysed in situ, and in practice forgotten. Several projects to generate stable trajectory databases have been developed for proteins, but no equivalence exists in the nucleic acids world. We present here a novel database system to store MD trajectories and analyses of nucleic acids. The initial data set available consists mainly of the benchmark of the new molecular dynamics force-field, parmBSC1. It contains 156 simulations, with over 120s of total simulation time. A deposition protocol is available to accept the submission of new trajectory data. The database is based on the combination of two NoSQL engines, Cassandra for storing trajectories and MongoDB to store analysis results and simulation metadata. The analyses available include backbone geometries, helical analysis, NMR observables and a variety of mechanical analyses. Individual trajectories and combined metatrajectories can be downloaded from the portal. The system is accessible through http://mmb.irbbarcelona.org/BIGNASim/. Supplementary Material is also available on-line at http://mmb.irbbarcelona.org/BIGNASim/SuppMaterial/