1
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Paschold A, Schäffler M, Miao X, Gardon L, Krüger S, Heise H, Röhr MIS, Ott M, Strodel B, Binder WH. Photocontrolled Reversible Amyloid Fibril Formation of Parathyroid Hormone-Derived Peptides. Bioconjug Chem 2024; 35:981-995. [PMID: 38865349 PMCID: PMC11261605 DOI: 10.1021/acs.bioconjchem.4c00188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 06/14/2024]
Abstract
Peptide fibrillization is crucial in biological processes such as amyloid-related diseases and hormone storage, involving complex transitions between folded, unfolded, and aggregated states. We here employ light to induce reversible transitions between aggregated and nonaggregated states of a peptide, linked to the parathyroid hormone (PTH). The artificial light-switch 3-{[(4-aminomethyl)phenyl]diazenyl}benzoic acid (AMPB) is embedded into a segment of PTH, the peptide PTH25-37, to control aggregation, revealing position-dependent effects. Through in silico design, synthesis, and experimental validation of 11 novel PTH25-37-derived peptides, we predict and confirm the amyloid-forming capabilities of the AMPB-containing peptides. Quantum-chemical studies shed light on the photoswitching mechanism. Solid-state NMR studies suggest that β-strands are aligned parallel in fibrils of PTH25-37, while in one of the AMPB-containing peptides, β-strands are antiparallel. Simulations further highlight the significance of π-π interactions in the latter. This multifaceted approach enabled the identification of a peptide that can undergo repeated phototriggered transitions between fibrillated and defibrillated states, as demonstrated by different spectroscopic techniques. With this strategy, we unlock the potential to manipulate PTH to reversibly switch between active and inactive aggregated states, representing the first observation of a photostimulus-responsive hormone.
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Affiliation(s)
- André Paschold
- Macromolecular
Chemistry, Institute of Chemistry, Faculty of Natural Science II, Martin Luther University Halle Wittenberg, von-Danckelmann-Platz 4, Halle 06120, Germany
| | - Moritz Schäffler
- Institute
of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute
of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, Jülich 52425, Germany
| | - Xincheng Miao
- Center
for Nanosystems Chemistry (CNC), Theodor-Boveri Weg, Universität Würzburg, Würzburg 97074, Germany
| | - Luis Gardon
- Institute
of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, Jülich 52425, Germany
- Institut
für Physikalische Biologie, Heinrich-Heine-Universität
Düsseldorf, 40225 Düsseldorf, Germany
| | - Stephanie Krüger
- Biozentrum,
Martin Luther University Halle-Wittenberg, Weinberweg 22, Halle 06120, Germany
| | - Henrike Heise
- Institute
of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, Jülich 52425, Germany
- Institut
für Physikalische Biologie, Heinrich-Heine-Universität
Düsseldorf, 40225 Düsseldorf, Germany
| | - Merle I. S. Röhr
- Center
for Nanosystems Chemistry (CNC), Theodor-Boveri Weg, Universität Würzburg, Würzburg 97074, Germany
| | - Maria Ott
- Institute
of Biophysics, Faculty of Natural Science I, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, Halle 06120, Germany
| | - Birgit Strodel
- Institute
of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany
- Institute
of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, Jülich 52425, Germany
| | - Wolfgang H. Binder
- Macromolecular
Chemistry, Institute of Chemistry, Faculty of Natural Science II, Martin Luther University Halle Wittenberg, von-Danckelmann-Platz 4, Halle 06120, Germany
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2
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Bárcenas O, Kuriata A, Zalewski M, Iglesias V, Pintado-Grima C, Firlik G, Burdukiewicz M, Kmiecik S, Ventura S. Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation. Nucleic Acids Res 2024; 52:W170-W175. [PMID: 38738618 PMCID: PMC11223845 DOI: 10.1093/nar/gkae382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/11/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024] Open
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.
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Affiliation(s)
- Oriol Bárcenas
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Aleksander Kuriata
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Mateusz Zalewski
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Valentín Iglesias
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Kilińskiego 1, 15-369 Białystok, Poland
| | - Carlos Pintado-Grima
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Grzegorz Firlik
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michał Burdukiewicz
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
- Clinical Research Centre, Medical University of Białystok, Kilińskiego 1, 15-369 Białystok, Poland
| | - Sebastian Kmiecik
- Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Salvador Ventura
- Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
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3
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Planas-Iglesias J, Borko S, Swiatkowski J, Elias M, Havlasek M, Salamon O, Grakova E, Kunka A, Martinovic T, Damborsky J, Martinovic J, Bednar D. AggreProt: a web server for predicting and engineering aggregation prone regions in proteins. Nucleic Acids Res 2024; 52:W159-W169. [PMID: 38801076 PMCID: PMC11223854 DOI: 10.1093/nar/gkae420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.
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Affiliation(s)
- Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Simeon Borko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jan Swiatkowski
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Matej Elias
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Martin Havlasek
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Ondrej Salamon
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Ekaterina Grakova
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Antonín Kunka
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Tomas Martinovic
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jan Martinovic
- IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
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4
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Ghosh D, Biswas A, Radhakrishna M. Advanced computational approaches to understand protein aggregation. BIOPHYSICS REVIEWS 2024; 5:021302. [PMID: 38681860 PMCID: PMC11045254 DOI: 10.1063/5.0180691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.
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Affiliation(s)
- Deepshikha Ghosh
- Department of Biological Sciences and Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India
| | - Anushka Biswas
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Gandhinagar, Palaj, Gujarat 382355, India
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5
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Azam HMH, Rößling RI, Geithe C, Khan MM, Dinter F, Hanack K, Prüß H, Husse B, Roggenbuck D, Schierack P, Rödiger S. MicroRNA biomarkers as next-generation diagnostic tools for neurodegenerative diseases: a comprehensive review. Front Mol Neurosci 2024; 17:1386735. [PMID: 38883980 PMCID: PMC11177777 DOI: 10.3389/fnmol.2024.1386735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/12/2024] [Indexed: 06/18/2024] Open
Abstract
Neurodegenerative diseases (NDs) are characterized by abnormalities within neurons of the brain or spinal cord that gradually lose function, eventually leading to cell death. Upon examination of affected tissue, pathological changes reveal a loss of synapses, misfolded proteins, and activation of immune cells-all indicative of disease progression-before severe clinical symptoms become apparent. Early detection of NDs is crucial for potentially administering targeted medications that may delay disease advancement. Given their complex pathophysiological features and diverse clinical symptoms, there is a pressing need for sensitive and effective diagnostic methods for NDs. Biomarkers such as microRNAs (miRNAs) have been identified as potential tools for detecting these diseases. We explore the pivotal role of miRNAs in the context of NDs, focusing on Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Huntington's disease, and Amyotrophic Lateral Sclerosis. The review delves into the intricate relationship between aging and NDs, highlighting structural and functional alterations in the aging brain and their implications for disease development. It elucidates how miRNAs and RNA-binding proteins are implicated in the pathogenesis of NDs and underscores the importance of investigating their expression and function in aging. Significantly, miRNAs exert substantial influence on post-translational modifications (PTMs), impacting not just the nervous system but a wide array of tissues and cell types as well. Specific miRNAs have been found to target proteins involved in ubiquitination or de-ubiquitination processes, which play a significant role in regulating protein function and stability. We discuss the link between miRNA, PTM, and NDs. Additionally, the review discusses the significance of miRNAs as biomarkers for early disease detection, offering insights into diagnostic strategies.
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Affiliation(s)
- Hafiz Muhammad Husnain Azam
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
| | - Rosa Ilse Rößling
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christiane Geithe
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
- Faculty of Health Sciences, Joint Faculty of the Brandenburg University of Technology Cottbus - Senftenberg, The Brandenburg Medical School Theodor Fontane and the University of Potsdam, Berlin, Germany
| | - Muhammad Moman Khan
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
| | - Franziska Dinter
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
- PolyAn GmbH, Berlin, Germany
| | - Katja Hanack
- Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Harald Prüß
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Britta Husse
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
| | - Dirk Roggenbuck
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
| | - Peter Schierack
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
| | - Stefan Rödiger
- Institute of Biotechnology, Faculty of Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany
- Faculty of Health Sciences, Joint Faculty of the Brandenburg University of Technology Cottbus - Senftenberg, The Brandenburg Medical School Theodor Fontane and the University of Potsdam, Berlin, Germany
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6
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Szulc N, Gąsior-Głogowska M, Żyłka P, Szefczyk M, Wojciechowski JW, Żak AM, Dyrka W, Kaczorowska A, Burdukiewicz M, Tarek M, Kotulska M. Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 313:124094. [PMID: 38503257 DOI: 10.1016/j.saa.2024.124094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 03/21/2024]
Abstract
The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.
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Affiliation(s)
- Natalia Szulc
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland; CNRS, University of Lorraine, F-5400 Nancy, France; Department of Physics and Biophysics, Faculty of Biotechnology and Food Science, Wrocław University of Environmental and Life Sciences, Norwida 25, 50-375 Wrocław, Poland
| | - Marlena Gąsior-Głogowska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
| | - Paweł Żyłka
- Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
| | - Monika Szefczyk
- Department of Bioorganic Chemistry, Faculty of Chemistry, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
| | - Jakub W Wojciechowski
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
| | - Andrzej M Żak
- Institute of Advanced Materials, Faculty of Chemistry, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
| | - Witold Dyrka
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
| | - Aleksandra Kaczorowska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland; Laboratory of Cytobiochemistry, Faculty of Biotechnology, University of Wroclaw, F. Joliot-Curie 14a, 50-383 Wroclaw, Poland
| | - Michał Burdukiewicz
- Institute of Biotechnology and Biomedicine, Autonomous University of Barcelona, Campus Universitat Autònoma de Barcelona Plaça Cívica Bellaterra, s/n, 08193 Cerdanyola del Vallès, Barcelona, Spain; Clinical Research Centre, Medical University of Bialystok, Jana Kilinskiego 1, 15-089 Bialystok, Poland
| | - Mounir Tarek
- CNRS, University of Lorraine, F-5400 Nancy, France.
| | - Malgorzata Kotulska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland.
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7
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Khalili K, Farzam F, Dabirmanesh B, Khajeh K. Prediction of protein aggregation. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 206:229-263. [PMID: 38811082 DOI: 10.1016/bs.pmbts.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.
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Affiliation(s)
- Kavyan Khalili
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Farnoosh Farzam
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Bahareh Dabirmanesh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Khosro Khajeh
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
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8
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Li W, Lin H, Huang Z, Xie S, Zhou Y, Gong R, Jiang Q, Xiang C, Huang J. DOTAD: A Database of Therapeutic Antibody Developability. Interdiscip Sci 2024:10.1007/s12539-024-00613-2. [PMID: 38530613 DOI: 10.1007/s12539-024-00613-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 01/25/2024] [Accepted: 01/27/2024] [Indexed: 03/28/2024]
Abstract
The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.
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Affiliation(s)
- Wenzhen Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hongyan Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shiyang Xie
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Rong Gong
- School of Computer Science and Technology, Aba Teachers University, Aba, 623002, China
| | - Qianhu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - ChangCheng Xiang
- School of Computer Science and Technology, Aba Teachers University, Aba, 623002, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China.
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9
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Kwiatkowski W, Greenwald J, Murzakhmetov L, Robinson RC, Riek R. Short Peptide Amyloids Are a Potential Sequence Pool for the Emergence of Proteins. J Mol Biol 2024; 436:168495. [PMID: 38360090 DOI: 10.1016/j.jmb.2024.168495] [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: 10/19/2023] [Revised: 01/15/2024] [Accepted: 02/10/2024] [Indexed: 02/17/2024]
Abstract
Under prebiotic conditions, peptides are capable of self-replication through a structure-based template-assisted mechanism when they form amyloids. Furthermore, peptide amyloids can spontaneously form inside fatty acid vesicles creating membrane enclosed complex structures of variable morphologies. This is possible because fatty acid vesicle membranes act as filters allowing passage of activated amino acids while some amino acids derived from the activated species become non-permeable and trapped in the vesicles. Similarly, nascent peptides derived from the condensation of the activated amino acids are also trapped in the vesicles. It is hypothesized that such preselected peptide amyloids become a sequence pool for the emergence of proteins in life and that after billions of years of cellular evolution, the sequences in the current proteome have diverged significantly from these original seed peptides. If this hypothesis is correct, it could be possible to detect the traces of these seed sequences in current proteomes. Here, we show for all possible 3, 6, 7, 8 or 9 residue sequence motifs that those motifs that are most amyloidogenic/aggregation prone are over-represented in extant proteomes compared to a sequence-randomized proteome. Furthermore, we find that there is a greater proportion of amyloidogenic sequence motifs in archaea proteomes than in the larger primate proteomes. This suggests that the evolution towards larger proteomes leads to smaller proportion of amyloidogenic sequences.
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Affiliation(s)
| | | | | | - Robert C Robinson
- School of Biomolecular Science and Engineering (BSE), Vidyasirimedhi Institute of Science and Technology (VISTEC), Thailand; Research Institute for Interdisciplinary Science, Okayama University, Japan
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10
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Yang Z, Wu Y, Liu H, He L, Deng X. AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides. J Chem Inf Model 2024; 64:1751-1762. [PMID: 38408296 DOI: 10.1021/acs.jcim.3c02035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
There has been an increasing interest in the use of amyloids for constructing various functional materials. The design of amyloid-associated functional materials requires the identification of the core peptide sequences as the fundamental building block. The existing computational methods are limited in terms of delineating polypeptides, the typical non-Euclidean structural data, and they fail to capture the dynamic interactions between amino acids due to ignoring the contextual information from surrounding amino acids. Here, we first propose the use of a state-of-the-art graph convolutional neural network for predicting the trends of amyloid formation from specific peptide sequences (AMYGNN) by abstracting each polypeptide as a graph, in which the constituting amino acids are viewed as nodes and edges characterizing the connections between pairs of amino acids are established when they meet a given distance threshold (Cα-Cα ≤ 5 Å). Our model achieves high performance with accuracy (0.9208), G-mean (0.9203), MCC (0.8417), and F1 (0.9235) in determining the characteristic peptide sequences to form amyloid. 32 of 534 crucial amino acid properties that greatly contribute to the formation of amyloids are ascertained, and the β-folding-like graph structure of a polypeptide is believed to be essential for the formation of amyloid. Our model enables the mapping of polypeptides with underlying interactions between amino acids and provides a quick and precise predictive framework for directing the construction of amyloid-associated functional materials.
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Affiliation(s)
- Zuojun Yang
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Yuhan Wu
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Hao Liu
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Li He
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Xiaoyuan Deng
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
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11
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Liao S, Zhang Y, Han X, Wang T, Wang X, Yan Q, Li Q, Qi Y, Zhang Z. A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning. Protein Sci 2024; 33:e4927. [PMID: 38380794 PMCID: PMC10880426 DOI: 10.1002/pro.4927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/22/2024]
Abstract
Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.
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Affiliation(s)
- Shaofeng Liao
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Yujun Zhang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Xinchen Han
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Tinglan Wang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Xi Wang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Qinglin Yan
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Qian Li
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Yifei Qi
- School of PharmacyFudan UniversityShanghaiChina
| | - Zhuqing Zhang
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
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12
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Ye Y, Li M, Pan Q, Fang X, Yang H, Dong B, Yang J, Zheng Y, Zhang R, Liao Z. Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer. Comput Biol Med 2024; 168:107745. [PMID: 38064851 DOI: 10.1016/j.compbiomed.2023.107745] [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: 09/17/2023] [Revised: 10/31/2023] [Accepted: 11/20/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVE We aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells. METHODS All the deubiquitinase (DUB) sequences were classified into USPs and non-USPs. Feature vectors, including 188D, n-gram, and 400D dimensions, were extracted from these sequences and subjected to binary classification via the Weka software. Next, thirty human USPs were also analyzed to identify conserved motifs and ascertained evolutionary relationships. Experimentally, more than 90 unique DUB-encoding plasmids were transfected into HeLa cell lines to assess alterations in KLF6 protein levels and to isolate a specific DUB involved in KLF6 regulation. Subsequent experiments utilized both wild-type (WT) USP26 overexpression and shRNA-mediated USP26 knockdown to examine changes in KLF6 protein levels. The half-life experiment was performed to assess the influence of USP26 on KLF6 protein stability. Immunoprecipitation was applied to confirm the USP26-KLF6 interaction, and ubiquitination assays to explore the role of USP26 in KLF6 deubiquitination. Additional cellular assays were conducted to evaluate the effects of USP26 on HeLa cell proliferation and migration. RESULTS 1. Among the extracted feature vectors of 188D, 400D, and n-gram, all 12 classifiers demonstrated excellent performance. The RandomForest classifier demonstrated superior performance in this assessment. Phylogenetic analysis of 30 human USPs revealed the presence of nine unique motifs, comprising zinc finger and ubiquitin-specific protease domains. 2. Through a systematic screening of the deubiquitinase library, USP26 was identified as the sole DUB associated with KLF6. 3. USP26 positively regulated the protein level of KLF6, as evidenced by the decrease in KLF6 protein expression upon shUSP26 knockdown in both 293T and Hela cell lines. Additionally, half-life experiments demonstrated that USP26 prolonged the stability of KLF6. 4. Immunoprecipitation experiments revealed a strong interaction between USP26 and KLF6. Notably, the functional interaction domain was mapped to amino acids 285-913 of USP26, as opposed to the 1-295 region. 5. WT USP26 was found to attenuate the ubiquitination levels of KLF6. However, the mutant USP26 abrogated its deubiquitination activity. 6. Functional biological assays demonstrated that overexpression of USP26 inhibited both proliferation and migration of HeLa cells. Conversely, knockdown of USP26 was shown to promote these oncogenic properties. CONCLUSIONS 1. At the protein sequence level, members of the USP family can be effectively differentiated from non-USP proteins. Furthermore, specific functional motifs have been identified within the sequences of human USPs. 2. The deubiquitinating enzyme USP26 has been shown to target KLF6 for deubiquitination, thereby modulating its stability. Importantly, USP26 plays a pivotal role in the modulation of proliferation and migration in cervical cancer cells.
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Affiliation(s)
- Ying Ye
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Meng Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Qilong Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Xin Fang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China; Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, 350012, China
| | - Hong Yang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Bingying Dong
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Jiaying Yang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Yuan Zheng
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Renxiang Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Zhijun Liao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
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13
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Wojciechowski JW, Szczurek W, Szulc N, Szefczyk M, Kotulska M. PACT - Prediction of amyloid cross-interaction by threading. Sci Rep 2023; 13:22268. [PMID: 38097650 PMCID: PMC10721876 DOI: 10.1038/s41598-023-48886-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023] Open
Abstract
Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .
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Affiliation(s)
- Jakub W Wojciechowski
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 50-370, Wrocław, Poland.
| | - Witold Szczurek
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 50-370, Wrocław, Poland
| | - Natalia Szulc
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 50-370, Wrocław, Poland
- Department of Physics and Biophysics, Wrocław University of Environmental and Life Sciences, Norwida 25, 50-375, Wrocław, Poland
- LPCT, CNRS, Université de Lorraine, F-54000, Nancy, France
| | - Monika Szefczyk
- Department of Bioorganic Chemistry, Faculty of Chemistry, Wrocław University of Science and Technology, 50-370, Wrocław, Poland
| | - Malgorzata Kotulska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 50-370, Wrocław, Poland.
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14
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Kandola T, Venkatesan S, Zhang J, Lerbakken BT, Von Schulze A, Blanck JF, Wu J, Unruh JR, Berry P, Lange JJ, Box AC, Cook M, Sagui C, Halfmann R. Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal. eLife 2023; 12:RP86939. [PMID: 37921648 PMCID: PMC10624427 DOI: 10.7554/elife.86939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023] Open
Abstract
A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.
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Affiliation(s)
- Tej Kandola
- Stowers Institute for Medical ResearchKansas CityUnited States
- The Open UniversityMilton KeynesUnited Kingdom
| | | | - Jiahui Zhang
- Department of Physics, North Carolina State UniversityRaleighUnited States
| | | | | | | | - Jianzheng Wu
- Stowers Institute for Medical ResearchKansas CityUnited States
- Department of Biochemistry and Molecular Biology, University of Kansas Medical CenterKansas CityUnited States
| | - Jay R Unruh
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Paula Berry
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Jeffrey J Lange
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Andrew C Box
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Malcolm Cook
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Celeste Sagui
- Department of Physics, North Carolina State UniversityRaleighUnited States
| | - Randal Halfmann
- Stowers Institute for Medical ResearchKansas CityUnited States
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15
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Zhou Y, Huang Z, Gou Y, Liu S, Yang W, Zhang H, Dzisoo AM, Huang J. AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains. Antib Ther 2023; 6:147-156. [PMID: 37492587 PMCID: PMC10365155 DOI: 10.1093/abt/tbad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 07/27/2023] Open
Abstract
Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.
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Affiliation(s)
- Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Yushu Gou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Siqi Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Wei Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Hongyu Zhang
- Research and Development, Zhanyuan Therapeutics Ltd., Hangzhou, Zhejiang 310000, China
| | - Anthony Mackitz Dzisoo
- Bioinformatics, Data and Medical Reporting, Arcencsus GmbH, Rostock, Mecklenburg-Vorpommern 18055, Germany
| | - Jian Huang
- To whom correspondence should be addressed. Jian Huang, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 610054, China.
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16
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Sidorczuk K, Gagat P, Kała J, Nielsen H, Pietluch F, Mackiewicz P, Burdukiewicz M. Prediction of protein subplastid localization and origin with PlastoGram. Sci Rep 2023; 13:8365. [PMID: 37225726 DOI: 10.1038/s41598-023-35296-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/16/2023] [Indexed: 05/26/2023] Open
Abstract
Due to their complex history, plastids possess proteins encoded in the nuclear and plastid genome. Moreover, these proteins localize to various subplastid compartments. Since protein localization is associated with its function, prediction of subplastid localization is one of the most important steps in plastid protein annotation, providing insight into their potential function. Therefore, we create a novel manually curated data set of plastid proteins and build an ensemble model for prediction of protein subplastid localization. Moreover, we discuss problems associated with the task, e.g. data set sizes and homology reduction. PlastoGram classifies proteins as nuclear- or plastid-encoded and predicts their localization considering: envelope, stroma, thylakoid membrane or thylakoid lumen; for the latter, the import pathway is also predicted. We also provide an additional function to differentiate nuclear-encoded inner and outer membrane proteins. PlastoGram is available as a web server at https://biogenies.info/PlastoGram and as an R package at https://github.com/BioGenies/PlastoGram . The code used for described analyses is available at https://github.com/BioGenies/PlastoGram-analysis .
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Affiliation(s)
| | - Przemysław Gagat
- Faculty of Biotechnology, University of Wrocław, 50-383, Wrocław, Poland
| | - Jakub Kała
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662, Warsaw, Poland
| | - Henrik Nielsen
- Department of Health Technology, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark
| | - Filip Pietluch
- Faculty of Biotechnology, University of Wrocław, 50-383, Wrocław, Poland
| | - Paweł Mackiewicz
- Faculty of Biotechnology, University of Wrocław, 50-383, Wrocław, Poland
| | - Michał Burdukiewicz
- Institute of Biotechnology and Biomedicine, Autonomous University of Barcelona, 08193, Cerdanyola del Vallés, Spain.
- Clinical Research Centre, Medical University of Białystok, 15-089, Białystok, Poland.
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17
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Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis. Int J Mol Sci 2023; 24:ijms24065680. [PMID: 36982754 PMCID: PMC10051237 DOI: 10.3390/ijms24065680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023] Open
Abstract
Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.
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18
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Frenkel A, Zecharia E, Gómez-Pérez D, Sendersky E, Yegorov Y, Jacob A, Benichou JIC, Stierhof YD, Parnasa R, Golden SS, Kemen E, Schwarz R. Cell specialization in cyanobacterial biofilm development revealed by expression of a cell-surface and extracellular matrix protein. NPJ Biofilms Microbiomes 2023; 9:10. [PMID: 36864092 PMCID: PMC9981879 DOI: 10.1038/s41522-023-00376-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/06/2023] [Indexed: 03/04/2023] Open
Abstract
Cyanobacterial biofilms are ubiquitous and play important roles in diverse environments, yet, understanding of the processes underlying the development of these aggregates is just emerging. Here we report cell specialization in formation of Synechococcus elongatus PCC 7942 biofilms-a hitherto unknown characteristic of cyanobacterial social behavior. We show that only a quarter of the cell population expresses at high levels the four-gene ebfG-operon that is required for biofilm formation. Almost all cells, however, are assembled in the biofilm. Detailed characterization of EbfG4 encoded by this operon revealed cell-surface localization as well as its presence in the biofilm matrix. Moreover, EbfG1-3 were shown to form amyloid structures such as fibrils and are thus likely to contribute to the matrix structure. These data suggest a beneficial 'division of labor' during biofilm formation where only some of the cells allocate resources to produce matrix proteins-'public goods' that support robust biofilm development by the majority of the cells. In addition, previous studies revealed the operation of a self-suppression mechanism that depends on an extracellular inhibitor, which supresses transcription of the ebfG-operon. Here we revealed inhibitor activity at an early growth stage and its gradual accumulation along the exponential growth phase in correlation with cell density. Data, however, do not support a threshold-like phenomenon known for quorum-sensing in heterotrophs. Together, data presented here demonstrate cell specialization and imply density-dependent regulation thereby providing deep insights into cyanobacterial communal behavior.
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Affiliation(s)
- Alona Frenkel
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Eli Zecharia
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Daniel Gómez-Pérez
- grid.10392.390000 0001 2190 1447Center for Plant Molecular Biology (ZMBP), University of Tübingen, 72074 Tübingen, Germany
| | - Eleonora Sendersky
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Yevgeni Yegorov
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Avi Jacob
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Jennifer I. C. Benichou
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - York-Dieter Stierhof
- grid.10392.390000 0001 2190 1447Center for Plant Molecular Biology (ZMBP), University of Tübingen, 72074 Tübingen, Germany
| | - Rami Parnasa
- grid.22098.310000 0004 1937 0503The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002 Ramat-Gan, Israel
| | - Susan S. Golden
- grid.266100.30000 0001 2107 4242Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Center for Circadian Biology, University of California, San Diego, La Jolla, CA 92093 USA
| | - Eric Kemen
- grid.10392.390000 0001 2190 1447Center for Plant Molecular Biology (ZMBP), University of Tübingen, 72074 Tübingen, Germany
| | - Rakefet Schwarz
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, 5290002, Ramat-Gan, Israel.
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19
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Baltutis V, O'Leary PD, Martin LL. Self-Assembly of Linear, Natural Antimicrobial Peptides: An Evolutionary Perspective. Chempluschem 2022; 87:e202200240. [PMID: 36198638 DOI: 10.1002/cplu.202200240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/29/2022] [Indexed: 01/31/2023]
Abstract
Antimicrobial peptides are an ancient and innate system of host defence against a wide range of microbial assailants. Mechanistically, unstructured peptides undergo a secondary structure transition into amphipathic α-helices, upon contact with membrane surfaces. This leads to peptide binding and removal of the membrane components in a detergent-like manner or via self-organisation into trans-membrane pores (either barrel-stave or toroidal pore) thereby destroying the microbe. Self-assembly of antimicrobial peptides into oligomers and ultimately amyloid has been mostly examined in parallel, however recent findings link diseases, such as Alzheimer's disease as an aberrant activity of a protective neuropeptide with antimicrobial activity. These self-assembled oligomers can also interact with membranes. Here, we review those antimicrobial peptides reported to self-assemble into amyloid, where supported by structural evidence. We consider their membrane activities as antimicrobial peptides and present evidence of consistent self-assembly patterns across major evolutionary groups. Trends are apparent across these groups, supporting the mounting data that self-assembly of antimicrobial peptides into amyloid should be considered as synergistic to the antimicrobial peptide response.
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Affiliation(s)
- Verity Baltutis
- School of Chemistry, Monash University, 3800, Clayton, Vic, Australia
| | - Paul D O'Leary
- School of Chemistry, Monash University, 3800, Clayton, Vic, Australia
| | - Lisandra L Martin
- School of Chemistry, Monash University, 3800, Clayton, Vic, Australia
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20
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Auriemma Citarella A, Di Biasi L, De Marco F, Tortora G. ENTAIL: yEt aNoTher amyloid fIbrils cLassifier. BMC Bioinformatics 2022; 23:517. [PMID: 36456900 PMCID: PMC9714056 DOI: 10.1186/s12859-022-05070-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND This research aims to increase our knowledge of amyloidoses. These disorders cause incorrect protein folding, affecting protein functionality (on structure). Fibrillar deposits are the basis of some wellknown diseases, such as Alzheimer, Creutzfeldt-Jakob diseases and type II diabetes. For many of these amyloid proteins, the relative precursors are known. Discovering new protein precursors involved in forming amyloid fibril deposits would improve understanding the pathological processes of amyloidoses. RESULTS A new classifier, called ENTAIL, was developed using over than 4000 molecular descriptors. ENTAIL was based on the Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type, with an accuracy on the test set of 81.80%, SN of 100%, SP of 63.63% and an MCC of 0.683 on a balanced dataset. CONCLUSIONS The analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.
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Affiliation(s)
| | - Luigi Di Biasi
- grid.11780.3f0000 0004 1937 0335Department of Computer Science, University of Salerno, Fisciano, Italy
| | - Fabiola De Marco
- grid.11780.3f0000 0004 1937 0335Department of Computer Science, University of Salerno, Fisciano, Italy
| | - Genoveffa Tortora
- grid.11780.3f0000 0004 1937 0335Department of Computer Science, University of Salerno, Fisciano, Italy
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21
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Miller ME, Li MH, Baghai A, Peetz VH, Zhyvoloup A, Raleigh DP. Analysis of Sheep and Goat IAPP Provides Insight into IAPP Amyloidogenicity and Cytotoxicity. Biochemistry 2022; 61:2531-2545. [PMID: 36286531 PMCID: PMC11132794 DOI: 10.1021/acs.biochem.2c00470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Human islet amyloid polypeptide (hIAPP) plays a role in glucose regulation but forms pancreatic amyloid deposits in type 2 diabetes, and that process contributes to β-cell dysfunction. Not all species develop diabetes, and not all secrete an IAPP that is amyloidogenic in vitro under normal conditions, a perfect correlation currently exists between both. Studies of IAPPs from such organisms can provide clues about the high amyloidogenicity of hIAPP and can inform the design of soluble analogues of hIAPP. Sheep and goat IAPP are among the most divergent from hIAPP, with 13 and 11 substitutions, respectively, including an unusual Tyr to His substitution at the C-terminus. The properties of sheep and goat IAPP were examined in solution and in the presence of anionic vesicles, resulting in no observed amyloid formation, even at increased concentrations. Furthermore, both peptides are considerably less toxic to cultured β-cells than hIAPP. The effect of the Y37H replacements was studied in the context of hIAPP, as was a Y37R substitution. Buffer- and salt-dependent effects were observed. There was little impact on the time to form amyloid in phosphate-buffered saline; however, a significant deceleration was observed in Tris buffer, and amyloid formation was slower in the absence of added salt. The Y37H substitution had little impact on toxicity, while the Y37R replacement led to a 30% decrease in toxicity compared with that of hIAPP. The implications for the amyloidogenicity of hIAPP and the design of soluble analogues of the human peptide are discussed.
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Affiliation(s)
- Matthew E.T. Miller
- Department of Chemistry, Stony Brook University, Nicolls Road, Stony Brook, New York 11790, United States
| | - Ming-Hao Li
- Graduate Program in Biochemistry and Structural Biology, Stony Brook University, Stony Brook, New York 11790, United States
| | - Aria Baghai
- Institute of Structural and Molecular Biology, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Vincent H. Peetz
- Department of Chemistry, Stony Brook University, Nicolls Road, Stony Brook, New York 11790, United States
| | - Alexander Zhyvoloup
- Institute of Structural and Molecular Biology, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Daniel P. Raleigh
- Department of Chemistry, Stony Brook University, Nicolls Road, Stony Brook, New York 11790, United States
- Graduate Program in Biochemistry and Structural Biology, Stony Brook University, Stony Brook, New York 11790, United States
- Institute of Structural and Molecular Biology, University College London, Gower Street, London WC1E 6BT, United Kingdom
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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22
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Kamal M, Tokmakjian L, Knox J, Mastrangelo P, Ji J, Cai H, Wojciechowski JW, Hughes MP, Takács K, Chu X, Pei J, Grolmusz V, Kotulska M, Forman-Kay JD, Roy PJ. A spatiotemporal reconstruction of the C. elegans pharyngeal cuticle reveals a structure rich in phase-separating proteins. eLife 2022; 11:e79396. [PMID: 36259463 PMCID: PMC9629831 DOI: 10.7554/elife.79396] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 10/11/2022] [Indexed: 11/19/2022] Open
Abstract
How the cuticles of the roughly 4.5 million species of ecdysozoan animals are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. Forty-eight percent of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families whose transcripts are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs, which are introduced for the first time here. All four families have sequence properties that drive phase separation and we demonstrate phase separation for one exemplar in vitro. This systematic analysis represents the first blueprint for cuticle construction and highlights the massive contribution that phase-separating materials make to the structure.
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Affiliation(s)
- Muntasir Kamal
- Department of Molecular Genetics, University of TorontoTorontoCanada
- The Donnelly Centre for Cellular and Biomolecular Research, University of TorontoTorontoCanada
| | - Levon Tokmakjian
- The Donnelly Centre for Cellular and Biomolecular Research, University of TorontoTorontoCanada
- Department of Pharmacology and Toxicology, University of TorontoTorontoCanada
| | - Jessica Knox
- Department of Molecular Genetics, University of TorontoTorontoCanada
- The Donnelly Centre for Cellular and Biomolecular Research, University of TorontoTorontoCanada
| | - Peter Mastrangelo
- Department of Molecular Genetics, University of TorontoTorontoCanada
- The Donnelly Centre for Cellular and Biomolecular Research, University of TorontoTorontoCanada
| | - Jingxiu Ji
- Department of Molecular Genetics, University of TorontoTorontoCanada
- The Donnelly Centre for Cellular and Biomolecular Research, University of TorontoTorontoCanada
| | - Hao Cai
- Molecular Medicine Program, The Hospital for Sick ChildrenTorontoCanada
| | - Jakub W Wojciechowski
- Wroclaw University of Science and Technology, Faculty of Fundamental Problems of Technology, Department of Biomedical EngineeringWroclawPoland
| | - Michael P Hughes
- Department of Cell and Molecular Biology, St. Jude Children’s Research HospitalMemphisUnited States
| | - Kristóf Takács
- PIT Bioinformatics Group, Institute of Mathematics, Eötvös UniversityBudapestHungary
| | - Xiaoquan Chu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Jianfeng Pei
- Department of Computer Science and Technology, Tsinghua UniversityBeijingChina
| | - Vince Grolmusz
- PIT Bioinformatics Group, Institute of Mathematics, Eötvös UniversityBudapestHungary
| | - Malgorzata Kotulska
- Wroclaw University of Science and Technology, Faculty of Fundamental Problems of Technology, Department of Biomedical EngineeringWroclawPoland
| | - Julie Deborah Forman-Kay
- Molecular Medicine Program, The Hospital for Sick ChildrenTorontoCanada
- Department of Biochemistry, University of TorontoTorontoCanada
| | - Peter J Roy
- Department of Molecular Genetics, University of TorontoTorontoCanada
- The Donnelly Centre for Cellular and Biomolecular Research, University of TorontoTorontoCanada
- Department of Pharmacology and Toxicology, University of TorontoTorontoCanada
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23
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Blanco MA. Computational models for studying physical instabilities in high concentration biotherapeutic formulations. MAbs 2022; 14:2044744. [PMID: 35282775 PMCID: PMC8928847 DOI: 10.1080/19420862.2022.2044744] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.
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Affiliation(s)
- Marco A. Blanco
- Materials and Biophysical Characterization, Analytical R & D, Merck & Co., Inc, Kenilworth, NJ USA
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24
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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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25
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Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2340:1-15. [PMID: 35167067 DOI: 10.1007/978-1-0716-1546-1_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.
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26
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ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition. BMC Bioinformatics 2021; 22:545. [PMID: 34753427 PMCID: PMC8579573 DOI: 10.1186/s12859-021-04446-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/13/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Amyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer's disease, Parkinson's disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions. RESULTS To address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions. CONCLUSIONS The advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/.
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27
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Gil‐Garcia M, Iglesias V, Pallarès I, Ventura S. Prion-like proteins: from computational approaches to proteome-wide analysis. FEBS Open Bio 2021; 11:2400-2417. [PMID: 34057308 PMCID: PMC8409284 DOI: 10.1002/2211-5463.13213] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 05/07/2021] [Accepted: 05/28/2021] [Indexed: 12/16/2022] Open
Abstract
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.
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Affiliation(s)
- Marcos Gil‐Garcia
- Departament de Bioquímica i Biologia MolecularInstitut de Biotecnologia i de BiomedicinaUniversitat Autònoma de BarcelonaSpain
| | - Valentín Iglesias
- Departament de Bioquímica i Biologia MolecularInstitut de Biotecnologia i de BiomedicinaUniversitat Autònoma de BarcelonaSpain
| | - Irantzu Pallarès
- Departament de Bioquímica i Biologia MolecularInstitut de Biotecnologia i de BiomedicinaUniversitat Autònoma de BarcelonaSpain
| | - Salvador Ventura
- Departament de Bioquímica i Biologia MolecularInstitut de Biotecnologia i de BiomedicinaUniversitat Autònoma de BarcelonaSpain
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28
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In silico analysis of the aggregation propensity of the SARS-CoV-2 proteome: Insight into possible cellular pathologies. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2021; 1869:140693. [PMID: 34237472 PMCID: PMC8256665 DOI: 10.1016/j.bbapap.2021.140693] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 12/12/2022]
Abstract
The SARS-CoV-2 virus causes the coronavirus disease 19 emerged in 2020. The pandemic triggered a turmoil in public health and is having a tremendous social and economic impact around the globe. Upon entry into host cells, the SARS-CoV-2 virus hijacks cellular machineries to produce and maintain its own proteins, spreading the infection. Although the disease is known for prominent respiratory symptoms, accumulating evidence is also demonstrating the involvement of the central nervous system, with possible mid- and long-term neurological consequences. In this study, we conducted a detailed bioinformatic analysis of the SARS-CoV-2 proteome aggregation propensity by using several complementary computational tools. Our study identified 10 aggregation prone proteins in the reference SARS-CoV-2 strain: the non-structural proteins Nsp4, Nsp6 and Nsp7 as well as ORF3a, ORF6, ORF7a, ORF7b, ORF10, CovE and CovM. By searching for the available mutants of each protein, we have found that most proteins are conserved, while ORF3a and ORF7b are variable and characterized by the occurrence of a large number of mutants with increased aggregation propensity. The geographical distribution of the mutants revealed interesting differences in the localization of aggregation-prone mutants of each protein. Aggregation-prone mutants of ORF7b were found in 7 European countries, whereas those of ORF3a in only 2. Aggregation-prone sequences of ORF7b, but not of ORF3a, were identified in Australia, India, Nepal, China, and Thailand. Our results are important for future analysis of a possible correlation between higher transmissibility and infection, as well as the presence of neurological symptoms with aggregation propensity of SARS-CoV-2 proteins.
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29
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Prabakaran R, Rawat P, Kumar S, Gromiha MM. Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Brief Bioinform 2021; 22:6309925. [PMID: 34181000 DOI: 10.1093/bib/bbab240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/18/2021] [Accepted: 06/02/2021] [Indexed: 01/09/2023] Open
Abstract
Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.
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Affiliation(s)
| | | | - Sandeep Kumar
- Department of Biotherapeutics Discovery in Boehringer-Ingelheim Pharmaceutical Inc., Ridgefield, CT, USA
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30
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La Manna S, Florio D, Di Natale C, Napolitano F, Malfitano AM, Netti PA, De Benedictis I, Marasco D. Conformational consequences of NPM1 rare mutations: An aggregation perspective in Acute Myeloid Leukemia. Bioorg Chem 2021; 113:104997. [PMID: 34044346 DOI: 10.1016/j.bioorg.2021.104997] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 05/03/2021] [Accepted: 05/13/2021] [Indexed: 10/21/2022]
Abstract
Often proteins association is a physiological process used by cells to regulate their growth and to adapt to different stress conditions, including mutations. In the case of a subtype of Acute Myeloid Leukemia (AML), mutations of nucleophosmin 1 (NPM1) protein cause its aberrant cytoplasmatic mislocalization (NPMc+). We recently pointed out an amyloidogenic propensity of protein regions including the most common mutations of NPMc+ located in the C-terminal domain (CTD): they were able to form, in vitro, amyloid cytotoxic aggregates with fibrillar morphology. Herein, we analyzed the conformational characteristics of several peptides including rare AML mutations of NPMc+. By means of different spectroscopic, microscopic and cellular assays we evaluated the importance of amino acid composition, among rare AML mutations, to determine amyloidogenic propensity. This study could add a piece of knowledge to the structural consequences of mutations in cytoplasmatic NPM1c+.
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Affiliation(s)
- Sara La Manna
- Department of Pharmacy, University of Naples "Federico II", 80134 Naples, Italy
| | - Daniele Florio
- Department of Pharmacy, University of Naples "Federico II", 80134 Naples, Italy
| | - Concetta Di Natale
- Interdisciplinary Research Centre on Biomaterials (CRIB), Department of Ingegneria Chimica dei Materiali e della Produzione Industriale (DICMAPI), University of Naples "Federico II", 8012 Naples, Italy
| | - Fabiana Napolitano
- Department of Translational Medical Science, University of Naples "Federico II", 80131 Naples, Italy
| | - Anna Maria Malfitano
- Department of Translational Medical Science, University of Naples "Federico II", 80131 Naples, Italy
| | - Paolo A Netti
- Interdisciplinary Research Centre on Biomaterials (CRIB), Department of Ingegneria Chimica dei Materiali e della Produzione Industriale (DICMAPI), University of Naples "Federico II", 8012 Naples, Italy
| | | | - Daniela Marasco
- Department of Pharmacy, University of Naples "Federico II", 80134 Naples, Italy.
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31
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Szulc N, Gąsior-Głogowska M, Wojciechowski JW, Szefczyk M, Żak AM, Burdukiewicz M, Kotulska M. Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli. Int J Mol Sci 2021; 22:ijms22105127. [PMID: 34066237 PMCID: PMC8151669 DOI: 10.3390/ijms22105127] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 04/22/2021] [Accepted: 05/07/2021] [Indexed: 11/18/2022] Open
Abstract
CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1–R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment’s propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.
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Affiliation(s)
- Natalia Szulc
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland; (N.S.); (M.G.-G.); (J.W.W.)
- LPCT, CNRS, Université de Lorraine, F-54000 Nancy, France
| | - Marlena Gąsior-Głogowska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland; (N.S.); (M.G.-G.); (J.W.W.)
| | - Jakub W. Wojciechowski
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland; (N.S.); (M.G.-G.); (J.W.W.)
| | - Monika Szefczyk
- Department of Bioorganic Chemistry, Faculty of Chemistry, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland;
| | - Andrzej M. Żak
- Electron Microscopy Laboratory, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland;
| | - Michał Burdukiewicz
- Clinical Research Centre, Medical University of Białystok, Jana Kilińskiego 1, 15-089 Białystok, Poland
- Institute of Biochemistry and Biophysics, Polish Academy Sciences, 02-106 Warsaw, Poland
- Faculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, 01968 Senftenberg, Germany
- Correspondence: (M.B.); (M.K.)
| | - Malgorzata Kotulska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland; (N.S.); (M.G.-G.); (J.W.W.)
- Correspondence: (M.B.); (M.K.)
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Munir F, Gul S, Asif A, Minhas FUAA. MILAMP: Multiple Instance Prediction of Amyloid Proteins. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1142-1150. [PMID: 31443048 DOI: 10.1109/tcbb.2019.2936846] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization, and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.
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Dyrka W, Gąsior-Głogowska M, Szefczyk M, Szulc N. Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars. BMC Bioinformatics 2021; 22:222. [PMID: 33926372 PMCID: PMC8086366 DOI: 10.1186/s12859-021-04139-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/19/2021] [Indexed: 11/16/2022] Open
Abstract
Background Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs. Results First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship. Conclusions While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04139-y.
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Affiliation(s)
- Witold Dyrka
- Wydział Podstawowych Problemów Techniki, Katedra Inżynierii Biomedycznej, Politechnika Wrocławska, Wrocław, Poland.
| | - Marlena Gąsior-Głogowska
- Wydział Podstawowych Problemów Techniki, Katedra Inżynierii Biomedycznej, Politechnika Wrocławska, Wrocław, Poland
| | - Monika Szefczyk
- Wydział Chemiczny, Katedra Chemii Bioorganicznej, Politechnika Wrocławska, Wrocław, Poland
| | - Natalia Szulc
- Wydział Podstawowych Problemów Techniki, Katedra Inżynierii Biomedycznej, Politechnika Wrocławska, Wrocław, Poland
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Szulc N, Burdukiewicz M, Gąsior-Głogowska M, Wojciechowski JW, Chilimoniuk J, Mackiewicz P, Šneideris T, Smirnovas V, Kotulska M. Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data. Sci Rep 2021; 11:8934. [PMID: 33903613 PMCID: PMC8076271 DOI: 10.1038/s41598-021-86530-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/08/2021] [Indexed: 02/02/2023] Open
Abstract
Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.
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Affiliation(s)
- Natalia Szulc
- grid.7005.20000 0000 9805 3178Department of Biomedical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland ,grid.29172.3f0000 0001 2194 6418University of Lorraine, CNRS, 5400 Nancy, France
| | - Michał Burdukiewicz
- grid.48324.390000000122482838Medical University of Bialystok, 15-089 Białystok, Poland ,grid.413454.30000 0001 1958 0162Institute of Biochemistry and Biophysics, Polish Academy Sciences, 02-106 Warsaw, Poland
| | - Marlena Gąsior-Głogowska
- grid.7005.20000 0000 9805 3178Department of Biomedical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
| | - Jakub W. Wojciechowski
- grid.7005.20000 0000 9805 3178Department of Biomedical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
| | - Jarosław Chilimoniuk
- grid.8505.80000 0001 1010 5103Faculty of Biotechnology, University of Wroclaw, 50-137 Wroclaw, Poland
| | - Paweł Mackiewicz
- grid.8505.80000 0001 1010 5103Faculty of Biotechnology, University of Wroclaw, 50-137 Wroclaw, Poland
| | - Tomas Šneideris
- grid.6441.70000 0001 2243 2806Life Sciences Center, Institute of Biotechnology, Vilnius University, 01513 Vilnius, Lithuania
| | - Vytautas Smirnovas
- grid.6441.70000 0001 2243 2806Life Sciences Center, Institute of Biotechnology, Vilnius University, 01513 Vilnius, Lithuania
| | - Malgorzata Kotulska
- grid.7005.20000 0000 9805 3178Department of Biomedical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
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Mayer-Bacon C, Agboha N, Muscalli M, Freeland S. Evolution as a Guide to Designing xeno Amino Acid Alphabets. Int J Mol Sci 2021; 22:ijms22062787. [PMID: 33801827 PMCID: PMC8000707 DOI: 10.3390/ijms22062787] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 02/02/2023] Open
Abstract
Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis.
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Affiliation(s)
- Christopher Mayer-Bacon
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; (C.M.-B.); (N.A.)
| | - Neyiasuo Agboha
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; (C.M.-B.); (N.A.)
| | - Mickey Muscalli
- Individualized Study Program, University of Maryland, Baltimore County, Baltimore, MD 21250, USA;
| | - Stephen Freeland
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA; (C.M.-B.); (N.A.)
- Individualized Study Program, University of Maryland, Baltimore County, Baltimore, MD 21250, USA;
- Correspondence:
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36
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Prabakaran R, Rawat P, Thangakani AM, Kumar S, Gromiha MM. Protein aggregation: in silico algorithms and applications. Biophys Rev 2021; 13:71-89. [PMID: 33747245 PMCID: PMC7930180 DOI: 10.1007/s12551-021-00778-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/01/2021] [Indexed: 01/08/2023] Open
Abstract
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
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Affiliation(s)
- R. Prabakaran
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Puneet Rawat
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - A. Mary Thangakani
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
| | - Sandeep Kumar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT USA
| | - M. Michael Gromiha
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India
- School of Computing, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Kanagawa Japan
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Endo S, Motomura K, Tsuhako M, Kakazu Y, Nakamura M, M. Otaki J. Search for Human-Specific Proteins Based on Availability Scores of Short Constituent Sequences: Identification of a WRWSH Protein in Human Testis. Comput Biol Chem 2020. [DOI: 10.5772/intechopen.89653] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Little is known about protein sequences unique in humans. Here, we performed alignment-free sequence comparisons based on the availability (frequency bias) of short constituent amino acid (aa) sequences (SCSs) in proteins to search for human-specific proteins. Focusing on 5-aa SCSs (pentats), exhaustive comparisons of availability scores among the human proteome and other nine mammalian proteomes in the nonredundant (nr) database identified a candidate protein containing WRWSH, here called FAM75, as human-specific. Examination of various human genome sequences revealed that FAM75 had genomic DNA sequences for either WRWSH or WRWSR due to a single nucleotide polymorphism (SNP). FAM75 and its related protein FAM205A were found to be produced through alternative splicing. The FAM75 transcript was found only in humans, but the FAM205A transcript was also present in other mammals. In humans, both FAM75 and FAM205A were expressed specifically in testis at the mRNA level, and they were immunohistochemically located in cells in seminiferous ducts and in acrosomes in spermatids at the protein level, suggesting their possible function in sperm development and fertilization. This study highlights a practical application of SCS-based methods for protein searches and suggests possible contributions of SNP variants and alternative splicing of FAM75 to human evolution.
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Abstract
Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.
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Affiliation(s)
- C. Kokotidou
- University of Crete, Department of Materials Science and Technology Voutes Campus GR-70013 Heraklion Crete Greece
- FORTH, Institute for Electronic Structure and Laser N. Plastira 100 GR 70013 Heraklion Greece
| | - P. Tamamis
- Texas A&M University, Artie McFerrin Department of Chemical Engineering College Station Texas 77843-3122 USA
| | - A. Mitraki
- University of Crete, Department of Materials Science and Technology Voutes Campus GR-70013 Heraklion Crete Greece
- FORTH, Institute for Electronic Structure and Laser N. Plastira 100 GR 70013 Heraklion Greece
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Burdukiewicz M, Sidorczuk K, Rafacz D, Pietluch F, Bąkała M, Słowik J, Gagat P. CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides. Pharmaceutics 2020; 12:pharmaceutics12111045. [PMID: 33142753 PMCID: PMC7692641 DOI: 10.3390/pharmaceutics12111045] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 01/10/2023] Open
Abstract
Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells; thus, they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes. AMPs preferentially permeabilize microbial membranes, but ACPs additionally target mitochondrial and plasma membranes of cancer cells. The preference towards mitochondrial membranes is explained by their membrane potential, membrane composition resulting from α-proteobacterial origin and the fact that mitochondrial targeting signals could have evolved from AMPs. Taking into account the therapeutic potential of ACPs and millions of deaths due to cancer annually, it is of vital importance to find new cationic peptides that selectively destroy cancer cells. Therefore, to reduce the costs of experimental research, we have created a robust computational tool, CancerGram, that uses n-grams and random forests for predicting ACPs. Compared to other ACP classifiers, CancerGram is the first three-class model that effectively classifies peptides into: ACPs, AMPs and non-ACPs/non-AMPs, with AU1U amounting to 0.89 and a Kappa statistic of 0.65. CancerGram is available as a web server and R package on GitHub.
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Affiliation(s)
- Michał Burdukiewicz
- Faculty of Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, 01968 Senftenberg, Germany;
- Why R? Foundation, 03-214 Warsaw, Poland;
| | - Katarzyna Sidorczuk
- Department of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, 50-383 Wrocław, Poland; (K.S.); (F.P.)
| | - Dominik Rafacz
- Why R? Foundation, 03-214 Warsaw, Poland;
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; (M.B.); (J.S.)
| | - Filip Pietluch
- Department of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, 50-383 Wrocław, Poland; (K.S.); (F.P.)
| | - Mateusz Bąkała
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; (M.B.); (J.S.)
| | - Jadwiga Słowik
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; (M.B.); (J.S.)
| | - Przemysław Gagat
- Department of Bioinformatics and Genomics, Faculty of Biotechnology, University of Wrocław, 50-383 Wrocław, Poland; (K.S.); (F.P.)
- Correspondence:
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40
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Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram. Int J Mol Sci 2020; 21:ijms21124310. [PMID: 32560350 PMCID: PMC7352166 DOI: 10.3390/ijms21124310] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/10/2020] [Accepted: 06/11/2020] [Indexed: 01/12/2023] Open
Abstract
Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.
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41
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Louros N, Konstantoulea K, De Vleeschouwer M, Ramakers M, Schymkowitz J, Rousseau F. WALTZ-DB 2.0: an updated database containing structural information of experimentally determined amyloid-forming peptides. Nucleic Acids Res 2020; 48:D389-D393. [PMID: 31504823 PMCID: PMC6943037 DOI: 10.1093/nar/gkz758] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 08/13/2019] [Accepted: 08/30/2019] [Indexed: 02/06/2023] Open
Abstract
Transition of soluble proteins into insoluble amyloid fibrils is driven by self-propagating short sequence stretches. However, accurate prediction of aggregation determinants remains challenging. Here, we describe WALTZ-DB 2.0, an updated and significantly expanded open-access database providing information on experimentally determined amyloid-forming hexapeptide sequences (http://waltzdb.switchlab.org/). We have updated WALTZ-DB 2.0 with new entries, including: (i) experimental validation of an in-house developed dataset of 229 hexapeptides, using electron microscopy and Thioflavin-T binding assays; (ii) manual curation of 98 amyloid-forming peptides isolated from literature. Furthermore, the content has been expanded by adding novel structural information for peptide entries, including sequences of the previous version. Using a computational methodology developed in the Switch lab, we have generated 3D-models of the putative amyloid fibril cores of WALTZ-DB 2.0 entries. Structural models, coupled with information on the energetic contributions and fibril core stabilities, can be accessed through individual peptide entries. Customized filtering options for subset selections and new modelling graphical features were added to upgrade online accessibility, providing a user-friendly interface for browsing, downloading and updating. WALTZ-DB 2.0 remains the largest open-access repository for amyloid fibril formation determinants and will continue to enhance the development of new approaches focused on accurate prediction of aggregation prone sequences.
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Affiliation(s)
- Nikolaos Louros
- VIB Center for Brain & Disease Research, Switch Laboratory, Leuven, 3000, Belgium.,KU Leuven, Department of Cellular and Molecular Medicine, Leuven, 3000, Belgium
| | - Katerina Konstantoulea
- VIB Center for Brain & Disease Research, Switch Laboratory, Leuven, 3000, Belgium.,KU Leuven, Department of Cellular and Molecular Medicine, Leuven, 3000, Belgium
| | - Matthias De Vleeschouwer
- VIB Center for Brain & Disease Research, Switch Laboratory, Leuven, 3000, Belgium.,KU Leuven, Department of Cellular and Molecular Medicine, Leuven, 3000, Belgium
| | - Meine Ramakers
- VIB Center for Brain & Disease Research, Switch Laboratory, Leuven, 3000, Belgium.,KU Leuven, Department of Cellular and Molecular Medicine, Leuven, 3000, Belgium
| | - Joost Schymkowitz
- VIB Center for Brain & Disease Research, Switch Laboratory, Leuven, 3000, Belgium.,KU Leuven, Department of Cellular and Molecular Medicine, Leuven, 3000, Belgium
| | - Frederic Rousseau
- VIB Center for Brain & Disease Research, Switch Laboratory, Leuven, 3000, Belgium.,KU Leuven, Department of Cellular and Molecular Medicine, Leuven, 3000, Belgium
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L. Almeida Z, M. M. Brito R. Structure and Aggregation Mechanisms in Amyloids. Molecules 2020; 25:molecules25051195. [PMID: 32155822 PMCID: PMC7179426 DOI: 10.3390/molecules25051195] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 12/27/2022] Open
Abstract
The aggregation of a polypeptide chain into amyloid fibrils and their accumulation and deposition into insoluble plaques and intracellular inclusions is the hallmark of several misfolding diseases known as amyloidoses. Alzheimer′s, Parkinson′s and Huntington’s diseases are some of the approximately 50 amyloid diseases described to date. The identification and characterization of the molecular species critical for amyloid formation and disease development have been the focus of intense scrutiny. Methods such as X-ray and electron diffraction, solid-state nuclear magnetic resonance spectroscopy (ssNMR) and cryo-electron microscopy (cryo-EM) have been extensively used and they have contributed to shed a new light onto the structure of amyloid, revealing a multiplicity of polymorphic structures that generally fit the cross-β amyloid motif. The development of rational therapeutic approaches against these debilitating and increasingly frequent misfolding diseases requires a thorough understanding of the molecular mechanisms underlying the amyloid cascade. Here, we review the current knowledge on amyloid fibril formation for several proteins and peptides from a kinetic and thermodynamic point of view, the structure of the molecular species involved in the amyloidogenic process, and the origin of their cytotoxicity.
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43
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The molecular lifecycle of amyloid – Mechanism of assembly, mesoscopic organisation, polymorphism, suprastructures, and biological consequences. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2019; 1867:140257. [DOI: 10.1016/j.bbapap.2019.07.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 07/12/2019] [Accepted: 07/23/2019] [Indexed: 12/11/2022]
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45
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Gałan W, Bąk M, Jakubowska M. Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus. Sci Rep 2019; 9:3436. [PMID: 30837511 PMCID: PMC6400966 DOI: 10.1038/s41598-019-39847-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 01/30/2019] [Indexed: 12/04/2022] Open
Abstract
Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier. HTP’s performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa.
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Affiliation(s)
- Wojciech Gałan
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University in Kraków, ul. Gronostajowa 7, 30-387, Kraków, Poland.
| | - Maciej Bąk
- Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University in Kraków, ul. Gronostajowa 7, 30-387, Kraków, Poland
| | - Małgorzata Jakubowska
- AGH University of Science and Technology, Faculty of Materials Science and Ceramics, al. Mickiewicza 30, 30-059, Kraków, Poland
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46
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Burdukiewicz M, Sobczyk P, Chilimoniuk J, Gagat P, Mackiewicz P. Prediction of Signal Peptides in Proteins from Malaria Parasites. Int J Mol Sci 2018; 19:E3709. [PMID: 30469512 PMCID: PMC6321056 DOI: 10.3390/ijms19123709] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 11/15/2018] [Accepted: 11/17/2018] [Indexed: 01/08/2023] Open
Abstract
Signal peptides are N-terminal presequences responsible for targeting proteins to the endomembrane system, and subsequent subcellular or extracellular compartments, and consequently condition their proper function. The significance of signal peptides stimulates development of new computational methods for their detection. These methods employ learning systems trained on datasets comprising signal peptides from different types of proteins and taxonomic groups. As a result, the accuracy of predictions are high in the case of signal peptides that are well-represented in databases, but might be low in other, atypical cases. Such atypical signal peptides are present in proteins found in apicomplexan parasites, causative agents of malaria and toxoplasmosis. Apicomplexan proteins have a unique amino acid composition due to their AT-biased genomes. Therefore, we designed a new, more flexible and universal probabilistic model for recognition of atypical eukaryotic signal peptides. Our approach called signalHsmm includes knowledge about the structure of signal peptides and physicochemical properties of amino acids. It is able to recognize signal peptides from the malaria parasites and related species more accurately than popular programs. Moreover, it is still universal enough to provide prediction of other signal peptides on par with the best preforming predictors.
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Affiliation(s)
- Michał Burdukiewicz
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-661 Warszawa, Poland.
| | - Piotr Sobczyk
- Department of Mathematics, Wrocław University of Technology, 50-370 Wrocław, Poland.
| | | | - Przemysław Gagat
- Department of Genomics, University of Wrocław, 50-383 Wrocław, Poland.
| | - Paweł Mackiewicz
- Department of Genomics, University of Wrocław, 50-383 Wrocław, Poland.
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Christensen LFB, Hansen LM, Finster K, Christiansen G, Nielsen PH, Otzen DE, Dueholm MS. The Sheaths of Methanospirillum Are Made of a New Type of Amyloid Protein. Front Microbiol 2018; 9:2729. [PMID: 30483237 PMCID: PMC6242892 DOI: 10.3389/fmicb.2018.02729] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 10/25/2018] [Indexed: 12/12/2022] Open
Abstract
The genera Methanospirillum and Methanosaeta contain species of anaerobic archaea that grow and divide within proteinaceous tubular sheaths that protect them from environmental stressors. The sheaths of Methanosaeta thermophila PT are composed of the 60.9 kDa major sheath protein MspA. In this study we show that sheaths purified from Methanospirillum hungatei JF-1 are regularly striated tubular structures with amyloid-like properties similar to those of M. thermophila PT. Depolymerizing the sheaths from M. hungatei JF-1 allowed us to identify a 40.6 kDa protein (WP_011449234.1) that shares 23% sequence similarity to MspA from M. thermophila PT (ABK14853.1), indicating that they might be distant homologs. The genome of M. hungatei JF-1 encodes six homologs of the identified MspA protein. Several homologs also exist in the related strains Methanospirillum stamsii Pt1 (7 homologs, 28–66% sequence identity), M. lacunae Ki8-1 C (15 homologs, 29–60% sequence identity) and Methanolinea tarda NOBI-1 (2 homologs, 31% sequence identity). The MspA protein discovered here could accordingly represent a more widely found sheath protein than the MspA from M. thermophila PT, which currently has no homologs in the NCBI Reference Sequence database (RefSeq).
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Affiliation(s)
- Line Friis Bakmann Christensen
- Interdisciplinary Nanoscience Center (iNANO), Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Lonnie Maria Hansen
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Kai Finster
- Section for Microbiology, Department of Bioscience, Aarhus University, Aarhus, Denmark
| | - Gunna Christiansen
- Section for Medical Microbiology and Immunology, Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Per Halkjær Nielsen
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Daniel Erik Otzen
- Interdisciplinary Nanoscience Center (iNANO), Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Morten Simonsen Dueholm
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
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Niu M, Li Y, Wang C, Han K. RFAmyloid: A Web Server for Predicting Amyloid Proteins. Int J Mol Sci 2018; 19:ijms19072071. [PMID: 30013015 PMCID: PMC6073578 DOI: 10.3390/ijms19072071] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 07/10/2018] [Accepted: 07/12/2018] [Indexed: 12/22/2022] Open
Abstract
Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL http://server.malab.cn/RFAmyloid/.
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Affiliation(s)
- Mengting Niu
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
| | - Yanjuan Li
- School of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China.
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150040, China.
| | - Ke Han
- School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150040, China.
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Michał B, Gagat P, Jabłoński S, Chilimoniuk J, Gaworski M, Mackiewicz P, Marcin Ł. PhyMet 2 : a database and toolkit for phylogenetic and metabolic analyses of methanogens. ENVIRONMENTAL MICROBIOLOGY REPORTS 2018; 10:378-382. [PMID: 29624889 DOI: 10.1111/1758-2229.12648] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 03/06/2018] [Accepted: 03/27/2018] [Indexed: 06/08/2023]
Abstract
The vast biodiversity of the microbial world and how little is known about it, has already been revealed by extensive metagenomics analyses. Our rudimentary knowledge of microbes stems from difficulties concerning their isolation and culture in laboratory conditions, which is necessary for describing their phenotype, among other things, for biotechnological purposes. An important component of the understudied ecosystems is methanogens, archaea producing a potent greenhouse-effect gas methane. Therefore, we created PhyMet2 , the first database that combines descriptions of methanogens and their culturing conditions with genetic information. The database contains a set of utilities that facilitate interactive data browsing, data comparison, phylogeny exploration and searching for sequence homologues. The most unique feature of the database is the web server MethanoGram, which can be used to significantly reduce the time and cost of searching for the optimal culturing conditions of methanogens by predicting them based on 16S RNA sequences. The database will aid many researchers in exploring the world of methanogens and their applications in biotechnological processes. PhyMet2 with the MethanoGram predictor is available at http://metanogen.biotech.uni.wroc.pl.
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Affiliation(s)
- Burdukiewicz Michał
- Department of Genomics, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Przemysław Gagat
- Department of Genomics, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Sławomir Jabłoński
- Department of Biotransformation, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Jarosław Chilimoniuk
- Department of Genomics, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Michał Gaworski
- Department of Biotransformation, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Paweł Mackiewicz
- Department of Genomics, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
| | - Łukaszewicz Marcin
- Department of Biotransformation, Faculty of Biotechnology, University of Wrocław, Wrocław, Poland
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