Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q

CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS. Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q. Basic Protocol 2: Exploring conformational diversity in a protein family. Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family. Basic Protocol 3: Representing conformational diversity in a phylogenetic context.Fil: Escobedo, Nahuel Abel. Universidad Nacional de Quilmes; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Fornasari, Maria Silvina. Universidad Nacional de Quilmes; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Palopoli, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; ArgentinaFil: Parisi, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; Argentin

Ensembles from Ordered and Disordered Proteins Reveal Similar Structural Constraints during Evolution

The conformations accessible to proteins are determined by the inter-residue interactions between amino acid residues. During evolution, structural constraints that are required for protein function providing biologically relevant information can exist. Here, we studied the proportion of sites evolving under structural constraints in two very different types of ensembles, those coming from ordered and disordered proteins. Using a structurally constrained model of protein evolution, we found that both types of ensembles show comparable, near 40%, number of positions evolving under structural constraints. Among these sites, ~ 68% are in disordered regions and ~ 57% of them show long-range inter-residue contacts. Also, we found that disordered ensembles are redundant in reference to their structurally constrained evolutionary information and could be described on average with ~ 11 conformers. Despite the different complexity of the studied ensembles and proteins, the similar constraints reveal a comparable level of selective pressure to maintain their biological functions. These results highlight the importance of the evolutionary information to recover meaningful biological information to further characterize conformational ensembles.Fil: Marchetti, Julia. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Monzón, Alexander. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Università di Padova; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tosatto, Silvio C.E.. Università di Padova; ItaliaFil: Parisi, Gustavo Daniel. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fornasari, Maria Silvina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

MobiDB: Intrinsically disordered proteins in 2021

The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.Fil: Piovesan, Damiano. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Escobedo, Nahuel Abel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Viczián, András. Università di Padova; ItaliaFil: Mičetić, Ivan. Università di Padova; ItaliaFil: Quaglia, Federica. Università di Padova; ItaliaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Ramasamy, Pathmanaban. Vrije Unviversiteit Brussel; Bélgica. University of Ghent; Bélgica. Interuniversity Institute of Bioinformatics in Brussels; BélgicaFil: Dosztányi, Zsuzsanna. Eötvös Loránd University; HungríaFil: Vranken, Wim F.. Vrije Unviversiteit Brussel; Bélgica. Interuniversity Institute of Bioinformatics in Brussels; BélgicaFil: Davey, Norman E.. The Institute Of Cancer Research; Reino UnidoFil: Parisi, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Fuxreiter, Monika. Università di Padova; ItaliaFil: Tosatto, Silvio C. E.. Università di Padova; Itali

RepeatsDB in 2021: Improved data and extended classification for protein tandem repeat structures

The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures.Fil: Paladin, Lisanna. Università di Padova; ItaliaFil: Bevilacqua, Martina. Università di Padova; ItaliaFil: Errigo, Sara. Università di Padova; ItaliaFil: Piovesan, Damiano. Università di Padova; ItaliaFil: Mičetić, Ivan. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Fabre, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: López, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Nilsson, Juliet Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Ríos, Javier Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Lorenzano Menna, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Cabrera, Maia Diana Eliana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: González Buitrón, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Gonçalves Kulik, Mariane. Johannes Gutenberg Universitat Mainz; AlemaniaFil: Fernández Alberti, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Fornasari, Maria Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Parisi, Gustavo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Lagares, Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Biotecnología y Biología Molecular. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Biotecnología y Biología Molecular; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Biológicas; ArgentinaFil: Hirsh, Layla. Pontificia Universidad Católica de Perú; PerúFil: Andrade Navarro, Miguel A.. Johannes Gutenberg Universitat Mainz; AlemaniaFil: Kajava, Andrey V. Centre National de la Recherche Scientifique; FranciaFil: Tosatto, Silvio C E. Università di Padova; Itali

Global network of computational biology communities: ISCB's regional student groups breaking barriers [version 1; peer review: Not peer reviewed]

Regional Student Groups (RSGs) of the International Society for Computational Biology Student Council (ISCB-SC) have been instrumental to connect computational biologists globally and to create more awareness about bioinformatics education. This article highlights the initiatives carried out by the RSGs both nationally and internationally to strengthen the present and future of the bioinformatics community. Moreover, we discuss the future directions the organization will take and the challenges to advance further in the ISCB-SC main mission: “Nurture the new generation of computational biologists”.Fil: Shome, Sayane. University of Iowa; Estados UnidosFil: Parra, Rodrigo Gonzalo. European Molecular Biology Laboratory; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fatima, Nazeefa. Uppsala Universitet; SueciaFil: Monzon, Alexander Miguel. Università di Padova; ItaliaFil: Cuypers, Bart. Universiteit Antwerp; BélgicaFil: Moosa, Yumna. University of KwaZulu Natal; SudáfricaFil: Da Rocha Coimbra, Nilson. Universidade Federal de Minas Gerais; BrasilFil: Assis, Juliana. Universidade Federal de Minas Gerais; BrasilFil: Giner Delgado, Carla. Universitat Autònoma de Barcelona; EspañaFil: Dönertaş, Handan Melike. European Molecular Biology Laboratory. European Bioinformatics Institute; Reino UnidoFil: Cuesta Astroz, Yesid. Universidad de Antioquia; Colombia. Universidad Ces. Facultad de Medicina.; ColombiaFil: Saarunya, Geetha. University of South Carolina; Estados UnidosFil: Allali, Imane. Universite Mohammed V. Rabat; Otros paises de África. University of Cape Town; SudáfricaFil: Gupta, Shruti. Jawaharlal Nehru University; IndiaFil: Srivastava, Ambuj. Indian Institute of Technology Madras; IndiaFil: Kalsan, Manisha. Jawaharlal Nehru University; IndiaFil: Valdivia, Catalina. Universidad Andrés Bello; ChileFil: Olguín Orellana, Gabriel José. Universidad de Talca; ChileFil: Papadimitriou, Sofia. Vrije Unviversiteit Brussel; Bélgica. Université Libre de Bruxelles; BélgicaFil: Parisi, Daniele. Katholikie Universiteit Leuven; BélgicaFil: Kristensen, Nikolaj Pagh. Technical University of Denmark; DinamarcaFil: Rib, Leonor. Universidad de Copenhagen; DinamarcaFil: Guebila, Marouen Ben. University of Luxembourg; LuxemburgoFil: Bauer, Eugen. University of Luxembourg; LuxemburgoFil: Zaffaroni, Gaia. University of Luxembourg; LuxemburgoFil: Bekkar, Amel. Universite de Lausanne; SuizaFil: Ashano, Efejiro. APIN Public Health Initiatives; NigeriaFil: Paladin, Lisanna. Università di Padova; ItaliaFil: Necci, Marco. Università di Padova; ItaliaFil: Moreyra, Nicolás Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures

The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures.Facultad de Ciencias ExactasInstituto de Biotecnologia y Biologia Molecula

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe