We present the theoretical foundations of a general principle to infer structure ensembles of flexible biomolecules from spatially and temporally averaged data obtained in biophysical experiments. The central idea is to compute the Kullback-Leibler optimal modification of a given prior distribution τ(x) with respect to the experimental data and its uncertainty. This principle generalizes the successful inferential structure determination method and recently proposed maximum entropy methods. Tractability of the protocol is demonstrated through the analysis of simulated nuclear magnetic resonance spectroscopy data of a small peptide.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3820694 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079439 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Correlating Disordered Activation Domain Ensembles with Gene Expression Levels.
Biophys Rep (N Y)
January 2025
Department of Chemistry and Biochemistry, University of California Merced, Merced, 95343; Department of Chemistry, Syracuse University, Syracuse, 13244.
Transcription factor proteins bind to specific DNA promoter sequences and initiate gene transcription. These proteins often contain intrinsically disordered activation domains (ADs) that regulate their transcriptional activity. Like other disordered protein regions, ADs do not have a fixed three-dimensional structure and instead exist in an ensemble of conformations.
View Article and Find Full Text PDFImproving groundwater quality predictions in semi-arid regions using ensemble learning models.
Environ Sci Pollut Res Int
January 2025
Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducted to analyze the performance of nine ensembles and regular machine learning (ML) methods in predicting two water quality parameters including total dissolved solids (TDS) and pH, in an area with semi-arid climate conditions.
View Article and Find Full Text PDFMultiscale X-ray scattering elucidates activation and deactivation of oxide-derived copper electrocatalysts for CO reduction.
Nat Commun
January 2025
Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science and Institute for Sustainable and Circular Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Electrochemical reduction of carbon dioxide (CO) into sustainable fuels and base chemicals requires precise control over and understanding of activity, selectivity and stability descriptors of the electrocatalyst under operation. Identification of the active phase under working conditions, but also deactivation factors after prolonged operation, are of the utmost importance to further improve electrocatalysts for electrochemical CO conversion. Here, we present a multiscale in situ investigation of activation and deactivation pathways of oxide-derived copper electrocatalysts under CO reduction conditions.
View Article and Find Full Text PDFThe Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.
JMIR Cardio
December 2024
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility.
View Article and Find Full Text PDFAn ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations.
Comput Biol Chem
January 2025
School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China. Electronic address:
Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and implicated in the numerous diseases. By exploring the subcellular localizations of lncRNAs, we can not only gain crucial insights into the molecular mechanisms of lncRNA-related biological processes but also make valuable contributions towards the diagnosis, prevention, and treatment of various human diseases. However, conventional experimental techniques tend to be laborious and time-intensive.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!