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The EMBO Journal Sep 2023
Topics: Protein Folding; Oxidation-Reduction; Biology
PubMed: 37530578
DOI: 10.15252/embj.2023115046 -
ACS Chemical Biology Apr 2024The role of nucleic acids in protein folding and aggregation is an area of continued research, with relevance to understanding both basic biological processes and... (Review)
Review
The role of nucleic acids in protein folding and aggregation is an area of continued research, with relevance to understanding both basic biological processes and disease. In this review, we provide an overview of the trajectory of research on both nucleic acids as chaperones and their roles in several protein misfolding diseases. We highlight key questions that remain on the biophysical and biochemical specifics of how nucleic acids have large effects on multiple proteins' folding and aggregation behavior and how this pertains to multiple protein misfolding diseases.
Topics: Humans; Nucleic Acids; Protein Folding; Molecular Chaperones; Proteostasis Deficiencies
PubMed: 38477936
DOI: 10.1021/acschembio.3c00695 -
Journal of Molecular Biology Oct 2023The study of protein folding plays a crucial role in improving our understanding of protein function and of the relationship between genetics and phenotypes. In...
The study of protein folding plays a crucial role in improving our understanding of protein function and of the relationship between genetics and phenotypes. In particular, understanding the thermodynamics and kinetics of the folding process is important for uncovering the mechanisms behind human disorders caused by protein misfolding. To address this issue, it is essential to collect and curate experimental kinetic and thermodynamic data on protein folding. K-Pro is a new database designed for collecting and storing experimental kinetic data on monomeric proteins, with a two-state folding mechanism. With 1,529 records from 62 proteins corresponding to 65 structures, K-Pro contains various kinetic parameters such as the logarithm of the folding and unfolding rates, Tanford's β and the ϕ values. When available, the database also includes thermodynamic parameters associated with the kinetic data. K-Pro features a user-friendly interface that allows browsing and downloading kinetic data of interest. The graphical interface provides a visual representation of the protein and mutants, and it is cross-linked to key databases such as PDB, UniProt, and PubMed. K-Pro is open and freely accessible through https://folding.biofold.org/k-pro and supports the latest versions of popular browsers.
Topics: Humans; Databases, Protein; Kinetics; Protein Denaturation; Protein Folding; Proteins; Thermodynamics; Mutant Proteins
PubMed: 37625584
DOI: 10.1016/j.jmb.2023.168245 -
Current Opinion in Structural Biology Feb 2024Relating the native fold of a protein to its amino acid sequence remains a fundamental problem in biology. While computer algorithms have demonstrated recently their... (Review)
Review
Relating the native fold of a protein to its amino acid sequence remains a fundamental problem in biology. While computer algorithms have demonstrated recently their prowess in predicting what structure a particular amino acid sequence will fold to, an understanding of how and why a specific protein fold is achieved remains elusive. A major challenge is to define the role of conformational heterogeneity during protein folding. Recent experimental studies, utilizing time-resolved FRET, hydrogen-exchange coupled to mass spectrometry, and single-molecule force spectroscopy, often in conjunction with simulation, have begun to reveal how conformational heterogeneity evolves during folding, and whether an intermediate ensemble of defined free energy consists of different sub-populations of molecules that may differ significantly in conformation, energy and entropy.
Topics: Protein Folding; Proteins; Amino Acid Sequence; Entropy; Computer Simulation; Protein Conformation
PubMed: 38041993
DOI: 10.1016/j.sbi.2023.102738 -
BMC Biology Dec 2023Alzheimer's disease (AD) is the most common neurodegenerative disorder with clinical presentations of progressive cognitive and memory deterioration. The pathologic...
BACKGROUND
Alzheimer's disease (AD) is the most common neurodegenerative disorder with clinical presentations of progressive cognitive and memory deterioration. The pathologic hallmarks of AD include tau neurofibrillary tangles and amyloid plaque depositions in the hippocampus and associated neocortex. The neuronal aggregated tau observed in AD cells suggests that the protein folding problem is a major cause of AD. J-domain-containing proteins (JDPs) are the largest family of cochaperones, which play a vital role in specifying and directing HSP70 chaperone functions. JDPs bind substrates and deliver them to HSP70. The association of JDP and HSP70 opens the substrate-binding domain of HSP70 to help the loading of the clients. However, in the initial HSP70 cycle, which JDP delivers tau to the HSP70 system in neuronal cells remains unclear.
RESULTS
We screened the requirement of a diverse panel of JDPs for preventing tau aggregation in the human neuroblastoma cell line SH-SY5Y by a filter retardation method. Interestingly, knockdown of DNAJB6, one of the JDPs, displayed tau aggregation and overexpression of DNAJB6b, one of the isoforms generated from the DNAJB6 gene by alternative splicing, reduced tau aggregation. Further, the tau bimolecular fluorescence complementation assay confirmed the DNAJB6b-dependent tau clearance. The co-immunoprecipitation and the proximity ligation assay demonstrated the protein-protein interaction between tau and the chaperone-cochaperone complex. The J-domain of DNAJB6b was critical for preventing tau aggregation. Moreover, reduced DNAJB6 expression and increased tau aggregation were detected in an age-dependent manner in immunohistochemical analysis of the hippocampus tissues of a mouse model of tau pathology.
CONCLUSIONS
In summary, downregulation of DNAJB6b increases the insoluble form of tau, while overexpression of DNAJB6b reduces tau aggregation. Moreover, DNAJB6b associates with tau. Therefore, this study reveals that DNAJB6b is a direct sensor for its client tau in the HSP70 folding system in neuronal cells, thus helping to prevent AD.
Topics: Animals; Humans; Mice; Alternative Splicing; Alzheimer Disease; HSP40 Heat-Shock Proteins; HSP70 Heat-Shock Proteins; Molecular Chaperones; Nerve Tissue Proteins; Neuroblastoma; Protein Folding; Protein Isoforms
PubMed: 38110916
DOI: 10.1186/s12915-023-01798-6 -
Science (New York, N.Y.) Jun 2024AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models...
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.
Topics: Humans; Cryoelectron Microscopy; Drug Design; Drug Discovery; Ligands; Molecular Docking Simulation; Protein Conformation; Protein Folding; Receptor, Serotonin, 5-HT2A; Receptors, sigma; Small Molecule Libraries; Serotonin 5-HT2 Receptor Agonists; Serotonin 5-HT2 Receptor Antagonists; Deep Learning
PubMed: 38753765
DOI: 10.1126/science.adn6354 -
Molecules (Basel, Switzerland) Feb 2024The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics... (Review)
Review
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.
Topics: Protein Conformation; Artificial Intelligence; Models, Molecular; Proteins; Algorithms; Computational Biology; Databases, Protein; Software; Protein Folding
PubMed: 38398585
DOI: 10.3390/molecules29040832 -
Current Opinion in Chemical Biology Oct 2023DnaK is a chaperone that aids in nascent protein folding and the maintenance of proteome stability across bacteria. Due to the importance of DnaK in cellular... (Review)
Review
DnaK is a chaperone that aids in nascent protein folding and the maintenance of proteome stability across bacteria. Due to the importance of DnaK in cellular proteostasis, there have been efforts to generate molecules that modulate its function. In nature, both protein substrates and antimicrobial peptides interact with DnaK. However, many of these ligands interact with other cellular machinery as well. Recent work has sought to modify these peptide scaffolds to create DnaK-selective and species-specific probes. Others have reported protein domain mimics of interaction partners to disrupt cellular DnaK function and high-throughput screening approaches to discover clinically-relevant peptidomimetics that inhibit DnaK. The described work provides a foundation for the design of new assays and molecules to regulate DnaK activity.
Topics: Escherichia coli Proteins; HSP70 Heat-Shock Proteins; Molecular Chaperones; Protein Folding; Peptides; Bacteria; Bacterial Proteins
PubMed: 37516006
DOI: 10.1016/j.cbpa.2023.102373 -
DNAJB6 mutants display toxic gain of function through unregulated interaction with Hsp70 chaperones.Nature Communications Nov 2023Molecular chaperones are essential cellular components that aid in protein folding and preventing the abnormal aggregation of disease-associated proteins. Mutations in...
Molecular chaperones are essential cellular components that aid in protein folding and preventing the abnormal aggregation of disease-associated proteins. Mutations in one such chaperone, DNAJB6, were identified in patients with LGMDD1, a dominant autosomal disorder characterized by myofibrillar degeneration and accumulations of aggregated protein within myocytes. The molecular mechanisms through which such mutations cause this dysfunction, however, are not well understood. Here we employ a combination of solution NMR and biochemical assays to investigate the structural and functional changes in LGMDD1 mutants of DNAJB6. Surprisingly, we find that DNAJB6 disease mutants show no reduction in their aggregation-prevention activity in vitro, and instead differ structurally from the WT protein, affecting their interaction with Hsp70 chaperones. While WT DNAJB6 contains a helical element regulating its ability to bind and activate Hsp70, in LGMDD1 disease mutants this regulation is disrupted. These variants can thus recruit and hyperactivate Hsp70 chaperones in an unregulated manner, depleting Hsp70 levels in myocytes, and resulting in the disruption of proteostasis. Interfering with DNAJB6-Hsp70 binding, however, reverses the disease phenotype, suggesting future therapeutic avenues for LGMDD1.
Topics: Humans; Gain of Function Mutation; Molecular Chaperones; HSP40 Heat-Shock Proteins; HSP70 Heat-Shock Proteins; Protein Folding; Nerve Tissue Proteins
PubMed: 37923706
DOI: 10.1038/s41467-023-42735-z -
Genomics, Proteomics & Bioinformatics Oct 2023Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics,... (Review)
Review
Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem - finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.
Topics: Protein Conformation; Algorithms; Proteins; Neural Networks, Computer; Protein Folding; Computational Biology
PubMed: 37001856
DOI: 10.1016/j.gpb.2022.11.014