Valentín Iglesias

University of Buenos Aires

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Salvador Ventura

Universitat Autònoma de Barcelona

Jaime Santos

Universitat Autònoma de Barcelona

Carlos Pintado‐Grima

Universitat Autònoma de Barcelona

Irantzu Pallarès

Universitat Autònoma de Barcelona

Sebastian Kmiecik

University of Warsaw

Oriol Bárcenas

Universitat Autònoma de Barcelona

Aleksander Kuriata

University of Warsaw

Jordi Pujols

Universitat Autònoma de Barcelona

Michał Burdukiewicz

Medical University of Białystok

Xavier Fernández‐Busquets

Institute for Bioengineering of Catalonia

Cooperative Institutions

Universitat Autònoma de Barcelona

Universitat de Barcelona

University of Padua

Institute of Molecular Life Sciences

Hungarian Academy of Sciences

HUN-REN Research Centre for Natural Sciences

Consejo Nacional de Investigaciones Científicas y Técnicas

Barcelona Institute for Global Health

Institute for Bioengineering of Catalonia

Centre National de la Recherche Scientifique

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation

Nucleic Acids Research (2024)

Oriol Bárcenas Aleksander Kuriata M. Zalewski Valentín Iglesias Carlos Pintado‐Grima

Abstract Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D’s functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

Interface (matter)

Protein Engineering

10.1093/nar/gkae382

Cite

Citations (11)

Prion‐like proteins: from computational approaches to proteome‐wide analysis

FEBS Open Bio (2021)

Marcos Gil-García Valentín Iglesias Irantzu Pallarès Salvador Ventura

Prions are self‐perpetuating proteins able to switch between a soluble state and an aggregated‐and‐transmissible conformation. These proteinaceous entities have been widely studied in yeast, where they are involved in hereditable phenotypic adaptations. The notion that such proteins could play functional roles and be positively selected by evolution has triggered the development of computational tools to identify prion‐like proteins in different kingdoms of life. These algorithms have succeeded in screening multiple proteomes, allowing the identification of prion‐like proteins in a diversity of unrelated organisms, evidencing that the prion phenomenon is well conserved among species. Interestingly enough, prion‐like proteins are not only connected with the formation of functional membraneless protein–nucleic acid coacervates, but are also linked to human diseases. This review addresses state‐of‐the‐art computational approaches to identify prion‐like proteins, describes proteome‐wide analysis efforts, discusses these unique proteins' functional role, and illustrates recently validated examples in different domains of life.

Proteome

Functional Diversity

Three-domain system

10.1002/2211-5463.13213

Cite

Citations (22)

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation

Cells (2019)

Anita Čarija Francisca Pinheiro Valentín Iglesias Salvador Ventura

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.

Proteome

Globular protein

Sequence (biology)

10.3390/cells8080856

Cite

Citations (11)

SGnn: A Web Server for the Prediction of Prion-Like Domains Recruitment to Stress Granules Upon Heat Stress

Frontiers in Molecular Biosciences (2021)

Valentín Iglesias Jaime Santos Juan Santos-Suárez Carlos Pintado‐Grima Salvador Ventura

Proteins bearing prion-like domains (PrLDs) are essential players in stress granules (SG) assembly. Analysis of data on heat stress-induced recruitment of yeast PrLDs to SG suggests that this propensity might be connected with three defined protein biophysical features: aggregation propensity, net charge, and the presence of free cysteines. These three properties can be read directly in the PrLDs sequences, and their combination allows to predict protein recruitment to SG under heat stress. On this basis, we implemented SGnn, an online predictor of SG recruitment that exploits a feed-forward neural network for high accuracy classification of the assembly behavior of PrLDs. The simplicity and precision of our strategy should allow its implementation to identify heat stress-induced SG-forming proteins in complete proteomes.

Proteome

10.3389/fmolb.2021.718301

Cite

Citations (19)

DispHred: A Server to Predict pH-Dependent Order–Disorder Transitions in Intrinsically Disordered Proteins

International Journal of Molecular Sciences (2020)

Jaime Santos Valentín Iglesias Carlos Pintado‐Grima Juan Santos-Suárez Salvador Ventura

The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge–hydropathy (C–H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C–H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C–H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms.

Intrinsically Disordered Proteins

Sequence (biology)

10.3390/ijms21165814

Cite

Citations (15)

Confident protein datasets for liquid-liquid phase separation studies

Research Square (Research Square) (2024)

Carlos Pintado‐Grima Oriol Bárcenas Valentín Iglesias Eva Arribas-Ruiz Michał Burdukiewicz

Abstract Background Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles in the ensemble’s structural and functional integrity. Driver proteins form condensates autonomously, while client proteins just localize within them. Although several databases exist to catalog proteins undergoing LLPS, they often contain divergent data that impedes interoperability between these resources. Additionally, there is a lack of consensus on selecting proteins without explicit experimental association with condensates (non-LLPS proteins or negative data). These two aspects have prevented the generation of reliable predictive models and fair benchmarks. Results In this work, we used an integrated biocuration protocol to analyze information from all relevant LLPS databases and generate confident datasets of client and driver proteins. Besides, we introduce standardized negative datasets, encompassing both globular and disordered proteins. To validate our datasets, we investigated specific physicochemical traits related to LLPS across different subsets of protein sequences. We observed significant differences not only between positive and negative instances but also among LLPS proteins themselves. The datasets from this study are publicly available as a website at https://llpsdatasets.ppmclab.com and as a data repository at https://github.com/PPMC-lab/llps-datasets. Conclusions Our datasets offer a reliable means for confidently assessing the specific roles of proteins in LLPS and identifying key differences in physicochemical properties underlying this process. These high-confidence datasets are poised to train a new generation of multilabel models, build more standardized benchmarks, and mitigate sequential biases associated with the presence of intrinsically disordered regions.

Separation (statistics)

Liquid liquid

Liquid phase

10.21203/rs.3.rs-4594179/v1

Cite

Citations (0)

In silico Characterization of Human Prion-Like Proteins: Beyond Neurological Diseases

Frontiers in Physiology (2019)

Valentín Iglesias Lisanna Paladin Teresa Juan-Blanco Irantzu Pallarès Patrick Aloy

Prion-like behavior has been in the spotlight since it was first associated with the onset of mammalian neurodegenerative diseases. However, a growing body of evidence suggests that this mechanism could be behind the regulation of processes such as transcription and translation in multiple species. Here, we perform a stringent computational survey to identify prion-like proteins in the human proteome. We detected 242 candidate polypeptides and computationally assessed their function, protein-protein interaction networks, tissular expression and their link to disease. Human prion-like proteins constitute a subset of modular polypeptides broadly expressed across different cell types and tissues, significantly associated with disease, embedded in highly connected interaction networks and involved in the flow of genetic information in the cell. Our analysis suggests that these proteins might play a relevant role not only in neurological disorders, but also in different types of cancer and viral infections.

Prion Proteins

10.3389/fphys.2019.00314

Cite

Citations (25)

Characterization of Soft Amyloid Cores in Human Prion-Like Proteins

Scientific Reports (2017)

Cristina Batlle Natalia Sánchez de Groot Valentín Iglesias Susanna Navarro Salvador Ventura

Prion-like behaviour is attracting much attention due to the growing evidences that amyloid-like self-assembly may reach beyond neurodegeneration and be a conserved functional mechanism. The best characterized functional prions correspond to a subset of yeast proteins involved in translation or transcription. Their conformational promiscuity is encoded in Prion Forming Domains (PFDs), usually long and intrinsically disordered protein segments of low complexity. The compositional bias of these regions seems to be important for the transition between soluble and amyloid-like states. We have proposed that the presence of cryptic soft amyloid cores embedded in yeast PFDs can also be important for their assembly and demonstrated their existence and self-propagating abilities. Here, we used an orthogonal approach in the search of human domains that share yeast PFDs compositional bias and exhibit a predicted nucleating core, identifying 535 prion-like candidates. We selected seven proteins involved in transcriptional or translational regulation and associated to disease to characterize the properties of their amyloid cores. All of them self-assemble spontaneously into amyloid-like structures able to propagate their polymeric state. This provides support for the presence of short sequences able to trigger conformational conversion in prion-like human proteins, potentially regulating their functionality.

Amyloid (mycology)

Fungal prion

Intrinsically Disordered Proteins

10.1038/s41598-017-09714-z

Cite

Citations (28)

Discovering Putative Prion-Like Proteins in Plasmodium falciparum: A Computational and Experimental Analysis

Frontiers in Microbiology (2018)

Irantzu Pallarès Natalia Sánchez de Groot Valentín Iglesias Ricardo Sant’Anna Arnau Biosca

Prions are a singular subset of proteins able to switch between a soluble conformation and a self-perpetuating amyloid state. Traditionally associated with neurodegenerative diseases, increasing evidence indicates that organisms exploit prion-like mechanisms for beneficial purposes. The ability to transit between conformations is encoded in the so-called prion domains, long disordered regions usually enriched in glutamine/asparagine residues. Interestingly, Plasmodium falciparum, the parasite that causes the most virulent form of malaria, is exceptionally rich in proteins bearing long Q/N-rich sequence stretches, accounting for roughly 30% of the proteome. This biased composition suggests that these protein regions might correspond to prion-like domains (PrLDs) and potentially form amyloid assemblies. To investigate this possibility, we performed a stringent computational survey for Q/N-rich PrLDs on P. falciparum. Our data indicate that ∼10% of P. falciparum protein sequences have prionic signatures, and that this subproteome is enriched in regulatory proteins, such as transcription factors and RNA-binding proteins. Furthermore, we experimentally demonstrate for several of the identified PrLDs that, despite their disordered nature, they contain inner short sequences able to spontaneously self-assemble into amyloid-like structures. Although the ability of these sequences to nucleate the conformational conversion of the respective full-length proteins should still be demonstrated, our analysis suggests that, as previously described for other organisms, prion-like proteins might also play a functional role in P. falciparum.

Proteome

Amyloid (mycology)

10.3389/fmicb.2018.01737

Cite

Citations (49)

Identimod: Modeling and Managing Brand Value Using Soft Computing

SSRN Electronic Journal (2015)

Manuel Chica Óscar Cordón Sergio Damas Valentín Iglesias José Mingot

Brands are one of the most important of a firm’s assets. Brand-managing activities are typically related to brand positioning and integration with marketing campaigns, and can involve complex decisions. The branding of an organization is indeed a dynamic system with many cause-effect relationships as well as intangible and heterogeneous variables. In order to help brand managers and marketers, we propose a decision support system, named Identimod, for modeling and evaluating branding strategies. Identimod uses non-linear dynamic modeling and soft computing to identify the branding system from different data sources through a linguistic user interface, and to provide advanced methods for diagnostics and validation. Identimod steps through a participatory, cyclic, and iterative process consisting of four different modules to increase the confidence and validity of the model, which should facilitate its acceptance by managers and stakeholders. Throughout this paper we demonstrate the modeling process and managerial benefits of Identimod by forming and answering the marketing questions for a real rebranding case of a seafood company in Spain.

Rebranding

Corporate branding

Source

Cite

Citations (2)