https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Wankowicz_qFit 2026-05-24T21:07:13Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2024Wankowicz_qFit&diff=4687&oldid=prev 2024-08-14T05:58:56Z

<p>Created page with &quot;== Citation == Wankowicz, Stephanie A. / Ravikumar, Ashraya / Sharma, Shivani / Riley, Blake / Raju, Akshay / Hogan, Daniel W. / Flowers, Jessica / van den Bedem, Henry / Keedy, Daniel A. / Fraser, James S. Automated multiconformer model building for X-ray crystallography and cryo-EM. 2024. eLife, Vol. 12, p. RP90606 == Abstract == In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional struc...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Wankowicz, Stephanie A. / Ravikumar, Ashraya / Sharma, Shivani / Riley, Blake / Raju, Akshay / Hogan, Daniel W. / Flowers, Jessica / van den Bedem, Henry / Keedy, Daniel A. / Fraser, James S. Automated multiconformer model building for X-ray crystallography and cryo-EM. 2024. eLife, Vol. 12, p. RP90606<br /> <br /> == Abstract ==<br /> <br /> In their folded state, biomolecules exchange between multiple conformational states<br /> that are crucial for their function. Traditional structural biology methods, such as X-ray<br /> crystallography<br /> and cryogenic electron microscopy (cryo-EM),<br /> produce density maps that are ensemble averages,<br /> reflecting molecules in various conformations. Yet, most models derived from these maps explicitly<br /> represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately<br /> reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling<br /> structural ensembles that mirror the experimental data. However, the challenge of distinguishing<br /> signal from noise complicates manual efforts to create these models. In response, we introduce the<br /> latest enhancements to qFit, an automated computational strategy designed to incorporate protein<br /> conformational heterogeneity into models built into density maps. These algorithmic improvements<br /> in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins.<br /> Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced<br /> by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be<br /> further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing<br /> the barrier of creating multiconformer models, qFit can foster the development of new hypotheses<br /> about the relationship between macromolecular conformational dynamics and function.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://elifesciences.org/articles/90606<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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