Protein Folding and Conformational Diseases
Research Lines
Our laboratory, led by Prof. Salvador Ventura, combines computational and experimental approaches to address the complexities of protein folding, misfolding and aggregation. Our multidisciplinary strategy integrates cutting-edge bioinformatics with state-of-the-art laboratory technologies to address these challenges from complementary perspectives.
Our mission goes beyond unraveling the fundamental mechanisms that drive these processes; we seek to decode how their disruption leads to human conformational diseases, with a particular focus on Parkinson's disease. By bringing together basic science and translational research, we aim to pioneer novel therapeutic strategies to combat these debilitating pathologies.
One of the strengths of our laboratory is protein design and engineering, which enables the development of next-generation protein-based pharmaceuticals that overcome the limitations of current medical solutions. In addition, we leverage the unique properties of protein self-assembly to design nanoscale materials adapted for diagnostics and therapeutics, opening new avenues in nanotechnology.
By uniting basic research, advanced protein engineering, and translational applications, we strive to push the boundaries of protein science in health and biotechnology, offering impactful solutions to improve lives and advance the frontiers of innovation.
Our main lines of research are:
Parkinson's disease (PD) is the second most common neurodegenerative disorder and is still incurable. PD is associated with the death of dopaminergic neurons in the brain. There is substantial evidence supporting the aggregation of the protein a-Synuclein (a-Syn) as a key event in the pathogenesis of PD, thus emerging as a prime therapeutic target. We have recently identified and designed small compounds and peptides that inhibit a-Syn aggregation with extremely high potency in animal models and in vitro. Our goal is to develop these molecules into lead compounds for the therapeutics of PD. Early detection of PD is a long-standing goal. A predictive test would revolutionize clinical care, research and treatment. Biochemical analysis demonstrated elevated a-Syn aggregates in patients' biofluids, suggesting that this feature could be used as a diagnostic biomarker for PD. We aim to develop an orthogonal approach to develop a sensitive automated diagnostic assay based on the specific detection of early a-Syn aggregates in biofluids.
Amyloids become infectious in prion diseases. However, not all prion proteins are disease-related; in yeast, they aid in environmental adaptation. Most yeast prions contain a low-complexity (LC) prion domain responsible for self-assembly and propagation. Proteins from other organisms, such as bacteria, plants, and humans, also carry prion-like domains, suggesting that their aggregated state may also benefit the cell. Human prion-like proteins are involved in the formation of membrane-free intracellular compartments through liquid-liquid phase separation using their LC domains to promote functional interactions between specific partners. However, these proteins are inherently prone to aggregation, and the liquid state can revert to an aberrant solid state responsible for a variety of pathologies, including inflammatory and neurodegenerative disorders and cancer. We have recently developed a set of new algorithms aimed at discovering new proteins with prion-like behavior to characterize the functional pathways in which they are involved, as well as their association with disease.
The extraordinary stability and tunable assembly of amyloid fibrils make them attractive targets for nanotechnology. To date, most efforts have focused on using short synthetic peptides as bioactive components of these materials, and much less has been explored for an analogous approach to induce globular proteins to assemble into functional nanofibers. The main limitations for the creation of mono- or multicomponent nanofibers containing functional globular proteins stem from the requirement to prevent their aggregation during expression, to maintain them in a soluble state during purification and storage, and to be able to induce their assembly at a desired time and place. We aim to exploit our combined computational/experimental expertise to design and produce novel molecules that fulfill these properties for a range of biomedical and biotechnological applications, such as enzymatic catalysis, biosensors, electronics, tissue engineering, drug delivery, and immunotherapy.
The rapid development of protein therapeutics - monoclonal antibodies, replacement enzymes and hormones - is providing improved therapies for a wide range of human diseases, taking advantage of their high specificity towards their target molecules. One of the main challenges to be faced during the development of protein-based biopharmaceuticals is their inherent propensity to aggregate. Indeed, protein therapeutic agents are stored and typically administered at very high concentrations. Under these conditions, they can easily aggregate, affecting the developability, stability, formulation and immunogenicity of the product. Traditionally, attempts to improve protein solubility have taken advantage of experimental trial and error approaches. However, they are expensive, difficult to perform and time-consuming. We have overcome these limitations by developing AGGRESCAN4D, a highly efficient computational tool for the automated design of protein structures that exhibit improved solubility without compromising their stability and thermodynamic function. We intend to use this approach to aid in the production of new and better therapeutic proteins.
Information of interest
Engineering a Probiotic for Parkinson's Disease. Convocatòria LLAVOR (Ref. 2024 LLAV 00093). AGAUR - Generalitat de Catalunya. Execution: 2024-2025.
HeliCure: Towards a new therapy for Parkinson's disease. Convocatòria PRODUCTE (Ref. 2024 PROD 00124). AGAUR - Generalitat de Catalunya. Execution: 2024-2025
NanoGlue: Next-Gen Molecular Binders for Viral Neutralization. Convocatòria INNOVADORS. (Ref. 2023 INNOV 00052). AGAUR - Generalitat de Catalunya. Execution: 2024-2025
Validation of α-synuclein Modifications in Parkinson’s dIsoRder Evolution. HEU-101156370-VaMPiRE. EC, HORIZON EUROPE-HEALTH. Execution: 2024-2029.
IDPfun2: Integrating novel data, artificial intelligence and molecular behaviour to expand functional characterization of intrinsically disordered proteins. EC, HORIZON EUROPE. HORIZON-MSCA-2023-SE-01. Execution: 2024-2027.
Engineering a Probiotic for Parkinson’s Disease. Convocatòria Prova de Concepte UAB 2023. Universitat Autònoma de Barcelona. Execution: 2024-2025.
Protein Aggregation: Implementation of New Biomedical, Structural, and Nanotechnological Strategies. Proyectos Generación del Conocimiento (Ref. PID2022-137963OB-I00). Ministerio de Ciencia e Innovación. Execution: 2023-2026.
Grup d'estructura i agregació de proteïnes. Grup Consolidació de la Generalitat de Catalunya (SGR), Ref. 2022 SGR 00635. Execucution: 2022-2025.
COST action Non-Globular Proteins in the era of machine Learning. EU, COST CA21160 / Execution: 2022-2026.
HeliCure: Towards a new therapy for Parkinson's disease. Convocatòria LLAVOR (Ref. 2023 LLAV 00027). AGAUR - Generalitat de Catalunya. Execution: 2024.
Gonzalez-Artero A, Pujols J, Ventura S*. Harnessing the Immune System: Vaccines to Fight Neurodegenerative Diseases. Trends in Molecular Medicine. S1471-4914(24)00219-3 (2024)
Fornt-Suñé M, Puertas MC, Martinez-Picado J, García-Pardo J, Ventura S*. Protein Nanoparticles for Targeted SARS-CoV-2 Trapping and Neutralization. Adv Healthc Mater. e2402744 (2024)
Santos J, Cuellar J, Pallarès I, Byrd EJ, Lends A, Moro F, Abdul-Shukkoor MB, Pujols J, Velasco-Carneros L, Sobott F, Otzen DE, Calabrese AN, Muga A, Pedersen JS, Loquet A, Valpuesta JM, Radford SE, Ventura S*. A Targetable N-Terminal Motif Orchestrates α-Synuclein Oligomer-to-Fibril Conversion. J Am Chem Soc. 146(18):12702-12711. (2024)
M. Fornt-Suñé, G. L. Bermejo, M. Gil-Garcia, A. Aran, J., M. Martí, Ventura, S. Protein-only Nanoparticles for T Cell Expansion and Activation. ACS Applied Nano Materials. 7:6669-6680 (2024)
Badaczewska-Dawid AE, Kuriata A, Pintado-Grima C, Garcia-Pardo J, Burdukiewicz M, Iglesias V, Kmiecik S, Ventura S*. A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms. Nucleic Acids Res. 52(D1):D360-D367 (2024).
Garcia-Pardo J, Ventura S*. Cryo-EM structures of functional and pathological amyloid ribonucleoprotein assemblies. Trends Biochem Sci. 49(2):119-133 (2024)
Garcia-Pardo J, Ventura S*. Chemical targeting of amyloids. Nat Chem Biol. 19(10):1176-1177. (2023)
Garcia-Pardo J, Bartolomé-Nafría A, Chaves-Sanjuan A, Gil-Garcia M, Visentin C, Bolognesi M, Ricagno S, Ventura S*. Cryo-EM structure of hnRNPDL-2 fibrils, a functional amyloid associated with limb-girdle muscular dystrophy D3. Nat Commun. 14(1):239. (2023).
Santos J, Pallarès I, Ventura S*. Is a cure for Parkinson's disease hiding inside us? Trends Biochem Sci. 47(8):641-644. (2022).
Santos J, Gracia P, Navarro S, Peña-Díaz S, Pujols J, Cremades N, Pallarès I, Ventura S.* α-Helical peptidic scaffolds to target α-synuclein toxic species with nanomolar affinity. Nat Commun. 12(1):3752. (2021).
- AGGRESCAN: prediction of aggregation "hot spots" in polypeptides (in collaboration with X. Daura, IBB-UAB)
- PrionScan: an online database of predicted prion domains in complete proteomes (in collaboration with J. Sancho, UNIZAR)
- PrionW: identification of prion-type protein domains (in collaboration with X. Daura, IBB-UAB)
- Aggrescan3D (A3D): a server for the prediction of aggregation propensity in protein structures and rational design of protein solubility (in collaboration with S. Kmiecik, Univ. of Warsaw)
- AMYCO: a server for predicting the impact of mutations on the propensity for aggregation of prion-like proteins.
- DispHred: a server for predicting pH-dependent order-disorder transitions in intrinsically disordered proteins.
- SolupHred: a server for predicting pH-dependent aggregation of intrinsically disordered proteins.
- SGnn: a server for predicting the recruitment of prion-like domains to heat stress granules.
- DispHScan : a multi-sequence web tool to predict protein disorder as a function of pH.
- A3D database: structure-based predictions of protein aggregation for the human proteome (in collaboration with S. Kmiecik, Univ. of Warsaw).
- CARs-DB : a database of cryptic amyloidogenic regions in intrinsically disordered proteins.
- A3DyDB : a large catalog of aggregation propensity predictions for the S. cerevisiae proteome (in collaboration with S. Kmiecik, Univ. of Warsaw).
- A3D-MODB: a database for proteome aggregation predictions in model organisms (in collaboration with S. Kmiecik, Univ. of Warsaw).
- aSynPEP-DB : a database of biogenic peptides to inhibit α-synuclein aggregation
- Aggrescan4D: informed analysis of the structure of pH-dependent protein aggregation (in collaboration with S. Kmiecik, Univ. of Warsaw).
- Reliable protein datasets for liquid-liquid phase separation studies
Funding
Additional information
