https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2023Tang_Reweighting 2026-05-24T19:36:10Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2023Tang_Reweighting&diff=4374&oldid=prev 2023-07-19T06:56:09Z

<p>Created page with &quot;== Citation == Tang, Wai Shing / Silva-Sánchez, David / Giraldo-Barreto, Julian / Carpenter, Bob / Hanson, Sonya M. / Barnett, Alex H. / Thiede, Erik H. / Cossio, Pilar. Ens...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Tang, Wai Shing / Silva-Sánchez, David / Giraldo-Barreto, Julian / Carpenter, Bob / Hanson, Sonya M. / Barnett, Alex H. / Thiede, Erik H. / Cossio, Pilar. Ensemble Reweighting Using Cryo-EM Particle Images. 2023. The J. of Physical Chemistry B <br /> <br /> == Abstract ==<br /> <br /> Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://pubs.acs.org/doi/full/10.1021/acs.jpcb.3c01087<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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