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Louros N, Rousseau F, Schymkowitz J. CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences. Bioinformatics 2024; 40:btae279. [PMID: 38662570 PMCID: PMC11078773 DOI: 10.1093/bioinformatics/btae279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/09/2024] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
MOTIVATION Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications. RESULTS We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity. AVAILABILITY AND IMPLEMENTATION The Cordax webserver is freely accessible at https://cordax.switchlab.org/.
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Affiliation(s)
- Nikolaos Louros
- Switch Laboratory, VIB Center for Brain and Disease Research, VIB, 3000 Leuven, Belgium
- Department of Cellular and Molecular Medicine, Switch Laboratory, KU Leuven, 3000 Leuven, Belgium
- Switch Laboratory, VIB Center for AI & Computational Biology, VIB, 3000 Leuven, Belgium
| | - Frederic Rousseau
- Switch Laboratory, VIB Center for Brain and Disease Research, VIB, 3000 Leuven, Belgium
- Department of Cellular and Molecular Medicine, Switch Laboratory, KU Leuven, 3000 Leuven, Belgium
- Switch Laboratory, VIB Center for AI & Computational Biology, VIB, 3000 Leuven, Belgium
| | - Joost Schymkowitz
- Switch Laboratory, VIB Center for Brain and Disease Research, VIB, 3000 Leuven, Belgium
- Department of Cellular and Molecular Medicine, Switch Laboratory, KU Leuven, 3000 Leuven, Belgium
- Switch Laboratory, VIB Center for AI & Computational Biology, VIB, 3000 Leuven, Belgium
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Duran-Romaña R, Houben B, De Vleeschouwer M, Louros N, Wilson MP, Matthijs G, Schymkowitz J, Rousseau F. N-glycosylation as a eukaryotic protective mechanism against protein aggregation. SCIENCE ADVANCES 2024; 10:eadk8173. [PMID: 38295165 PMCID: PMC10830103 DOI: 10.1126/sciadv.adk8173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/28/2023] [Indexed: 02/02/2024]
Abstract
The tendency for proteins to form aggregates is an inherent part of every proteome and arises from the self-assembly of short protein segments called aggregation-prone regions (APRs). While posttranslational modifications (PTMs) have been implicated in modulating protein aggregation, their direct role in APRs remains poorly understood. In this study, we used a combination of proteome-wide computational analyses and biophysical techniques to investigate the potential involvement of PTMs in aggregation regulation. Our findings reveal that while most PTM types are disfavored near APRs, N-glycosylation is enriched and evolutionarily selected, especially in proteins prone to misfolding. Experimentally, we show that N-glycosylation inhibits the aggregation of peptides in vitro through steric hindrance. Moreover, mining existing proteomics data, we find that the loss of N-glycans at the flanks of APRs leads to specific protein aggregation in Neuro2a cells. Our findings indicate that, among its many molecular functions, N-glycosylation directly prevents protein aggregation in higher eukaryotes.
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Affiliation(s)
- Ramon Duran-Romaña
- Switch Laboratory, VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Bert Houben
- Switch Laboratory, VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Matthias De Vleeschouwer
- Switch Laboratory, VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Nikolaos Louros
- Switch Laboratory, VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Matthew P. Wilson
- Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Gert Matthijs
- Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Joost Schymkowitz
- Switch Laboratory, VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Frederic Rousseau
- Switch Laboratory, VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- Switch Laboratory, Department of Cellular and Molecular Medicine, KU Leuven, 3000 Leuven, Belgium
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Pintado-Grima C, Bárcenas O, Ventura S. Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins. Methods Mol Biol 2024; 2714:171-185. [PMID: 37676599 DOI: 10.1007/978-1-0716-3441-7_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein-protein interactions. In addition, we provide study cases to illustrate the database's features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/ .
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Affiliation(s)
- Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain.
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Louros N, Schymkowitz J, Rousseau F. Mechanisms and pathology of protein misfolding and aggregation. Nat Rev Mol Cell Biol 2023; 24:912-933. [PMID: 37684425 DOI: 10.1038/s41580-023-00647-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 09/10/2023]
Abstract
Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.
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Affiliation(s)
- Nikolaos Louros
- Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Joost Schymkowitz
- Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium.
- Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium.
| | - Frederic Rousseau
- Switch Laboratory, VIB-KU Leuven Center for Brain & Disease Research, Leuven, Belgium.
- Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium.
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Falgarone T, Villain E, Richard F, Osmanli Z, Kajava AV. Census of exposed aggregation-prone regions in proteomes. Brief Bioinform 2023; 24:bbad183. [PMID: 37200152 DOI: 10.1093/bib/bbad183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/20/2023] Open
Abstract
Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.
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Affiliation(s)
- Théo Falgarone
- Centre de Recherche en Biologie cellulaire de Montpellier, CNRS, Université Montpellier, Montpellier, 34293, France
| | - Etienne Villain
- Centre de Recherche en Biologie cellulaire de Montpellier, CNRS, Université Montpellier, Montpellier, 34293, France
| | - Francois Richard
- Centre de Recherche en Biologie cellulaire de Montpellier, CNRS, Université Montpellier, Montpellier, 34293, France
| | - Zarifa Osmanli
- Centre de Recherche en Biologie cellulaire de Montpellier, CNRS, Université Montpellier, Montpellier, 34293, France
- Biophysics Institute, Ministry of Science and Education of Azerbaijan Republic, Az1141, Baku, Azerbaijan
| | - Andrey V Kajava
- Centre de Recherche en Biologie cellulaire de Montpellier, CNRS, Université Montpellier, Montpellier, 34293, France
- Institut de Biologie Computationnelle, Université Montpellier, 34095 Montpellier, France
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Pintado-Grima C, Santos J, Iglesias V, Manglano-Artuñedo Z, Pallarès I, Ventura S. Exploring cryptic amyloidogenic regions in prion-like proteins from plants. FRONTIERS IN PLANT SCIENCE 2023; 13:1060410. [PMID: 36726678 PMCID: PMC9885169 DOI: 10.3389/fpls.2022.1060410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
Prion-like domains (PrLDs) are intrinsically disordered regions (IDRs) of low sequence complexity with a similar composition to yeast prion domains. PrLDs-containing proteins have been involved in different organisms' regulatory processes. Regions of moderate amyloid propensity within IDRs have been shown to assemble autonomously into amyloid fibrils. These sequences tend to be rich in polar amino acids and often escape from the detection of classical bioinformatics screenings that look for highly aggregation-prone hydrophobic sequence stretches. We defined them as cryptic amyloidogenic regions (CARs) and recently developed an integrated database that collects thousands of predicted CARs in IDRs. CARs seem to be evolutionary conserved among disordered regions because of their potential to stablish functional contacts with other biomolecules. Here we have focused on identifying and characterizing CARs in prion-like proteins (pCARs) from plants, a lineage that has been poorly studied in comparison with other prionomes. We confirmed the intrinsic amyloid potential for a selected pCAR from Arabidopsis thaliana and explored functional enrichments and compositional bias of pCARs in plant prion-like proteins.
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Affiliation(s)
- Carlos Pintado-Grima
- Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaime Santos
- Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Valentín Iglesias
- Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
- Barcelona Institute for Global Health, Barcelona Centre for International Health Research (ISGlobal, Hospital Clínic-Universitat de Barcelona), Barcelona, Spain
- Nanomalaria Group, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Zoe Manglano-Artuñedo
- Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Irantzu Pallarès
- Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Salvador Ventura
- Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Barcelona, Spain
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Pintado-Grima C, Bárcenas O, Manglano-Artuñedo Z, Vilaça R, Macedo-Ribeiro S, Pallarès I, Santos J, Ventura S. CARs-DB: A Database of Cryptic Amyloidogenic Regions in Intrinsically Disordered Proteins. Front Mol Biosci 2022; 9:882160. [PMID: 35898309 PMCID: PMC9309178 DOI: 10.3389/fmolb.2022.882160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/15/2022] [Indexed: 12/20/2022] Open
Abstract
Proteome-wide analyses suggest that most globular proteins contain at least one amyloidogenic region, whereas these aggregation-prone segments are thought to be underrepresented in intrinsically disordered proteins (IDPs). In recent work, we reported that intrinsically disordered regions (IDRs) indeed sustain a significant amyloid load in the form of cryptic amyloidogenic regions (CARs). CARs are widespread in IDRs, but they are necessarily exposed to solvent, and thus they should be more polar and have a milder aggregation potential than conventional amyloid regions protected inside globular proteins. CARs are connected with IDPs function and, in particular, with the establishment of protein-protein interactions through their IDRs. However, their presence also appears associated with pathologies like cancer or Alzheimer’s disease. Given the relevance of CARs for both IDPs function and malfunction, we developed CARs-DB, a database containing precomputed predictions for all CARs present in the IDPs deposited in the DisProt database. This web tool allows for the fast and comprehensive exploration of previously unnoticed amyloidogenic regions embedded within IDRs sequences and might turn helpful in identifying disordered interacting regions. It contains >8,900 unique CARs identified in a total of 1711 IDRs. CARs-DB is freely available for users and can be accessed at http://carsdb.ppmclab.com. To validate CARs-DB, we demonstrate that two previously undescribed CARs selected from the database display full amyloidogenic potential. Overall, CARs-DB allows easy access to a previously unexplored amyloid sequence space.
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Affiliation(s)
- Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Zoe Manglano-Artuñedo
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rita Vilaça
- Instituto de Biologia Molecular e Celular and Instituto de Investigação e Inovação Em Saúde, Universidade Do Porto, Porto, Portugal
| | - Sandra Macedo-Ribeiro
- Instituto de Biologia Molecular e Celular and Instituto de Investigação e Inovação Em Saúde, Universidade Do Porto, Porto, Portugal
| | - Irantzu Pallarès
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jaime Santos
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain
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Ogt Demonstrated Conspicuous Clinical Significance in Cancers, from Pan-Cancer to Small-Cell Lung Cancer. JOURNAL OF ONCOLOGY 2022; 2022:2010341. [PMID: 35356257 PMCID: PMC8959957 DOI: 10.1155/2022/2010341] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/18/2022] [Indexed: 11/25/2022]
Abstract
The clinical progression of small-cell lung cancer (SCLC) remains pessimistic. The aim of the present study was to promote the understanding of the clinical significance and mechanism of O-linked N-acetylglucosamine (GlcNAc) transferase (OGT) in SCLC. Wilcoxon tests, standardized mean difference (SMD), and Kruskal–Wallis tests were utilized to compare OGT level differences among the experimental and control groups. The univariate Cox regression analysis, Kaplan–Meier curves, and receiver operating characteristic curves were applied to determine OGT's clinical relevance in cancers. The Spearman correlation analysis and enrichment analysis were utilized to explore the underlying mechanisms of OGT in cancers. For the first time in the field, we provide an overview of OGT in 32 cancers using a large number of samples (n = 21,196), determining distinct OGT expression in 25 cancers and its prognosis effects in 12 cancers. Furthermore, using 950 samples from multiple sources, upregulated OGT was found in both mRNA and protein levels in SCLC (SMD = 0.93, 95% CI [0.24, 1.63]). Higher OGT levels represented a more unfavorable disease-free interval for SCLC patients (p < 0.001). The research also identified OGT expression as a potential marker for SCLC prediction (sensitivity = 0.79, specificity = 0.86, and AUC = 0.88). The high expression of OGT in SCLC may result from the positive regulation of two transcription factors—DEK and XRN2. We primarily investigated the underlying mechanisms of OGT in SCLC. Herein, based on the analyses from pan-cancer to SCLC, OGT demonstrated conspicuous clinical significance. OGT may be an underlying biomarker for the treatment and identification of some cancers, including SCLC.
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Computational methods to predict protein aggregation. Curr Opin Struct Biol 2022; 73:102343. [PMID: 35240456 DOI: 10.1016/j.sbi.2022.102343] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/20/2021] [Accepted: 01/17/2022] [Indexed: 01/13/2023]
Abstract
In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.
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