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Chemical cross-linking coupled with mass spectroscopy (CXMS) is a powerful technique for investigating protein structures. CXMS has been mostly used to characterize the predominant structure for a protein, whereas cross-links incompatible with a unique structure of a protein or a protein complex are often discarded. We have recently shown that the so-called over-length cross-links actually contain protein dynamics information. We have thus established a method called DynaXL, which allow us to extract the information from the over-length cross-links and to visualize protein ensemble structures. In this protocol, we present the detailed procedure for using DynaXL, which comprises five steps. They are identification of highly confident cross-links, delineation of protein domains/subunits, ensemble rigid-body refinement, and final validation/assessment. The DynaXL method is generally applicable for analyzing the ensemble structures of multi-domain proteins and protein-protein complexes, and is freely available at www.tanglab.org/resources.
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http://dx.doi.org/10.1007/s41048-017-0044-9 | DOI Listing |
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFBiophys J
September 2025
Biophysical and Biomedical Measurement Group, Microsystems and Nanotechnology Division, Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA. Electronic address:
Macromolecular structure is central to biology. Yet, not all biomolecules have a well-defined fold. Intrinsically disordered regions are ubiquitous, conveying a versatility to function even in otherwise folded structures.
View Article and Find Full Text PDFInt J Biol Macromol
September 2025
Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, In
Understanding the structural and functional diversity of toxin proteins is critical for elucidating macromolecular behavior, mechanistic variability, and structure-driven bioactivity. Traditional approaches have primarily focused on binary toxicity prediction, offering limited resolution into distinct modes of action of toxins. Here, we present MultiTox, an ensemble stacking framework for the classification of toxin proteins based on their molecular mode of action: neurotoxins, cytotoxins, hemotoxins, and enterotoxins.
View Article and Find Full Text PDFNucleic Acids Res
September 2025
Institute of Biophysics of the Czech Academy of Sciences, Královopolská 135, Brno 61200, Czech Republic.
RNA G-quadruplexes (rG4s) are emerging as vital structural elements involved in processes like gene regulation, translation, and genome stability. Found in untranslated regions of messenger RNAs (mRNAs), they influence translation efficiency and mRNA localization. Additionally, rG4s of long noncoding RNAs and telomeric RNA play roles in RNA processing and cellular aging.
View Article and Find Full Text PDFMultivariate Behav Res
September 2025
Department of Statistics, TU Dortmund University, Dortmund, Germany.
Predicting ordinal responses such as school grades or rating scale data is a common task in the social and life sciences. Currently, two major streams of methodology exist for ordinal prediction: traditional statistical models such as the proportional odds model and machine learning (ML) methods such as random forest (RF) adapted to ordinal prediction. While methods from the latter stream have displayed high predictive performance, particularly for data characterized by non-linear effects, most of these methods do not support hierarchical data.
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