Current Protein and Peptide Science - Volume 26, Issue 10, 2025
Volume 26, Issue 10, 2025
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Protein Misfolding and Aggregation of Pathological IgG Light Chains in Oncohematological Dyscrasias: From Molecular Pathways to Clinical Implications
More LessAuthors: Tomáš Guman, Ján Sýkora, Veronika Demčáková and Gabriel ŽoldákNeoplastic transformation of B cells of the post-germinative center can lead to oncohematological dyscrasias, which often results in an abnormal production of monoclonal immunoglobulin light chains. The non-physiological production of large amounts of IgG light chains leads to the formation of extracellular deposits called 'aggregomas' and rare conditions such as light chain crystal deposition disease. Kidney manifestations and heavy-chain deposition disease can also occur in plasma cell dyscrasias, emphasizing the role of IgG misfolding and aggregation. This minireview describes molecular mechanisms of IgG light-chain aggregation, as well as the consequences and therapeutic implications of IgG light chain misfolding in these disorders. By elucidating the mechanisms of IgG light chain misfolding and aggregation, researchers can identify specific molecular and cellular pathways. This knowledge opens the door to novel therapeutic targets, offering the potential for interventions that can either prevent the initial misfolding events, promote the proper folding and processing of immunoglobulins, or enhance the clearance of misfolded proteins and aggregates. These protein folding-related issues persist even after the successful elimination of the malignant B cells. Such targeted protein-folding therapies could significantly improve patients' quality of life and contribute to their recovery. Thus, a deep understanding of IgG light chain misfolding and its consequences not only sheds light on the complex biology of oncohematological dyscrasias but also opens the way for innovative treatment strategies that could transform patient care in these conditions, instilling hope and motivation in the healthcare professionals and researchers in this field.
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Recent Advances in Co-Condensation and Co-Aggregation of Amyloid Proteins Linked to Neurodegenerative Diseases
More LessAuthors: Xuefeng Zhang, Yujie Chen, Yuan Tan, Tong Pan and Guanghong WeiThe misfolding and aggregation of amyloid proteins are closely associated with a range of neurodegenerative diseases. Liquid-liquid phase separation (LLPS) can initiate the aggregation of proteins, indicating that LLPS may serve as an alternative pathway for the pathological aggregation of amyloid proteins. The co-occurrence of two or more amyloid pathologies has been observed in extensive pathophysiological studies and is linked to faster disease progression. The co-LLPS (also known as co-condensation) and co-aggregation of different disease-related proteins have been proposed as a potential molecular mechanism for combined neuropathology. Here, we reviewed the current state of knowledge regarding the co-aggregation and co-condensation of various amyloid proteins, including Aβ, tau, α-synuclein, TDP-43, FUS, and hnRNPA/B protein family, C9orf72 dipeptide repeats and prion protein. We briefly introduced the epidemiological correlation among different neurodegenerative diseases and specifically presented recent experimental findings about co-aggregation and co-condensation of two different amyloid proteins. Additionally, we discussed computational studies focusing on the molecular interactions between amyloid proteins to offer mechanistic insights into the co-LLPS and co-aggregation processes. This review provides an overview of the synergistic interactions between different disease-related proteins, which is helpful for understanding the mechanisms of combined neuropathology and developing targeted therapeutic strategies.
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Liquid-Liquid Phase Separation Associated with Intrinsically Disordered Proteins: Experimental and Computational Tools
More LessAuthors: Orkid Coskuner-Weber and Vladimir N. UverskyThe phenomenon of Liquid-Liquid Phase Separation (LLPS) serves as a vital mechanism for the spatial organization of biomolecules, significantly influencing the elementary processes within the cellular milieu. Intrinsically disordered proteins, or proteins endowed with intrinsically disordered regions, are pivotal in driving this biophysical process, thereby dictating the formation of non-membranous cellular compartments. Compelling evidence has linked aberrations in LLPS to the pathogenesis of various neurodegenerative diseases, underscored by the disordered proteins’ proclivity to form pathological aggregates. This study meticulously evaluates the arsenal of contemporary experimental and computational methodologies dedicated to the examination of intrinsically disordered proteins within the context of LLPS. Through a discerning discourse on the capabilities and constraints of these investigative techniques, we unravel the intricate contributions of these ubiquitous proteins to LLPS and neurodegeneration. Moreover, we project a future trajectory for the field, contemplating on innovative research tools and their potential to elucidate the underlying mechanisms of LLPS, with the ultimate goal of fostering new therapeutic avenues for combating neurodegenerative disorders.
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Coupling of SARS-CoV-2 to Amyloid Fibrils and Liquid-Liquid Phase Separation
More LessAuthors: Hoang Linh Nguyen and Mai Suan LiCOVID-19 is a respiratory disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), but because the receptor protein of this virus can appear not only in the lungs and throat but also in various parts of the host's body, it causes different diseases. Recent observations have suggested that SARS-CoV-2 damages the central nervous system of patients in a manner similar to amyloid-associated neurodegenerative diseases such as Alzheimer's and Parkinson's. Neurodegenerative diseases are believed to be associated with the self-assembly of amyloid proteins and peptides. On the other hand, whole proteins or parts of them encoded by SARS-CoV-2 can form amyloid fibrils, which may play an important role in amyloid-related diseases. Motivated by this evidence, this mini-review discusses experimental and computational studies of SARS-CoV-2 proteins that can form amyloid aggregates. Liquid-Liquid Phase Separation (LLPS) is a dynamic and reversible process leading to the creation of membrane-less organelles within the cytoplasm, which is not bound by a membrane that concentrates specific types of biomolecules. These organelles play pivotal roles in cellular signaling, stress response, and the regulation of biomolecular condensates. Recently, LLPS of the Nucleocapsid (N) protein and SARS-CoV-2 RNA has been disclosed, but many questions about the phase separation mechanism and the formation of the virion core are still unclear. We summarize the results of this phenomenon and suggest potentially intriguing issues for future research.
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Utilizing AfDesign for Developing a Small Molecule Inhibitor of PICK1-PDZ
More LessAuthors: Emily Hendrix, Xinyu Xia, Amy O. Stevens and Yi HeIntroductionThe PICK1 PDZ domain has been identified as a potential drug target for neurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5, have been discovered experimentally to bind to the PDZ domain with a relatively high binding affinity. With the rapid growth of computational research, there is an urgent need for more efficient computational methods to design viable ligands that target proteins.
MethodsRecently, a newly developed program called AfDesign (part of ColabDesign) at https://github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has been suggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitating the ligand development process. To evaluate the performance of this program, we explored its ability to target the PICK1 PDZ domain, given our current understanding of it. We found that the designated length of the ligand and the number of recycles play vital roles in generating ligands with optimal properties.
ResultsUtilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparable ligands to that of prior identified ligands. Moreover, these designed ligands displayed significantly lower binding energy compared to manually created sequences.
ConclusionThis work demonstrated that AfDesign can potentially be a powerful tool to facilitate the exploration of the ligand space for the purpose of targeting PDZ domains.
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Design and Characterization of Antibacterial Peptide Nanofibrils as Components of Composites for Biomaterial Applications
More LessObjectiveThe objective of this study was to design and synthesize the ug46 peptide, incorporate its fibrils into composite materials, and evaluate its structural and antimicrobial properties. Another objective was to utilize spectroscopy and molecular simulation, enhanced by Machine Vision methods, to monitor the aggregation process of the ug46 peptide and assess its potential as a scaffold for an antimicrobial peptide.
MethodsThe structural analysis of the ug46 peptide reveals its dynamic conformational changes. Initially, the peptide exhibits a disordered structure with minimal α-helix content, but as incubation progresses, it aggregates into fibrils rich in β-sheets. This transformation was validated by CD and ThT assays, which showed decreased molar ellipticity and an increase in ThT fluorescence.
ResultsLaser-induced fluorescence and molecular dynamics simulations further revealed the transition from a compact native state to extended “worm-like” filament structures, influenced by peptide concentration and temperature. TEM and AFM confirmed these changes, showing the evolution of protofibrils into mature fibrils with characteristic twists. When incorporated into chitosan-bioglass composites, these fibrils significantly enhanced antimicrobial activity against pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa.
ConclusionOverall, ug46 peptide fibrils show promise as a multifunctional scaffold with structural and antimicrobial benefits in composite biomaterials.
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Clustering-based Method for Constructing the Phase Diagram of the van der Waals Model Fluid
More LessAuthors: Dinh Quoc Huy Pham, Midhun Mohan Anila and Mateusz ChwastykIntroductionMembraneless organelles, such as nucleoli, stress granules, and P-bodies, are not enclosed by lipid membranes; rather, they are formed through a process known as liquid-liquid phase separation. To fully understand the biophysics behind the formation and regulation of these organelles, knowledge that has significant implications for cellular biology and disease research, the creation of phase diagrams is essential. Phase diagrams help clarify the physical and chemical conditions under which these organelles form, exist, and function within cells. However, methods for creating phase diagrams are often limited when the equation of state is unknown, a challenge that becomes more pronounced with increasing system complexity. While several methods exist to address this issue, their application is not universal.
MethodsWe present a new method based on the SPACEBALL algorithm and cluster size monitoring, which enables the determination of binodal and spinodal line positions by analyzing system clustering during molecular dynamics simulations of a well-studied van der Waals fluid under various conditions.
ResultsBased on an analysis of the system’s clustering behavior, we constructed the phase diagram for the monoatomic van der Waals fluid simulated at various densities and temperatures, observing that uniformly distributed van der Waals beads aggregate, causing changes in the system’s density.
DiscussionUsing the generated data, we discuss how a fitting function can be used to determine the binodal line location, and how observations of the system’s density fluctuations can be used to determine the spinodal line location and assess the critical temperature.
ConclusionWe have presented alternative methods for locating phase boundaries in protein solutions, where the absence of a validated equation of state necessitates innovative approaches and makes traditional methods challenging to apply. Our SPACEBALL-based approach enables the creation of phase diagrams using pure trajectories obtained from molecular dynamics simulations.
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Volumes & issues
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Volume 26 (2025)
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Volume (2025)
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Volume 25 (2024)
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Volume 24 (2023)
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Volume 23 (2022)
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Volume 22 (2021)
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Volume 21 (2020)
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Volume 20 (2019)
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Volume 19 (2018)
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Volume 18 (2017)
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Volume 17 (2016)
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Volume 16 (2015)
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Volume 15 (2014)
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Volume 14 (2013)
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Volume 13 (2012)
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Volume 12 (2011)
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Volume 11 (2010)
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Volume 10 (2009)
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Volume 9 (2008)
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Volume 8 (2007)
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Volume 7 (2006)
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Volume 6 (2005)
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Volume 5 (2004)
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Volume 4 (2003)
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Volume 3 (2002)
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Volume 2 (2001)
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Volume 1 (2000)
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