![]() The ubiquitous nature of such metal-aided structural architecture of the catalytic site is corroborated by the large therapeutic spectrum of drugs that target two-metal-ion enzymes and are thus broadly used to treat cancers and viral infections. All these complex enzymes catalyze the scission or synthesis of phosphodiester bonds in DNA/RNA, respectively. (3,4) This two-metal-ion reaction chemistry is identical to that of other nucleic-acid-processing protein enzymes, such as endo/exonucleases and polymerases. Both reactions, which are S N2-like nucleophilic additions, occur within an active site comprising two divalent metal ions that coordinate and activate the reacting residues ( Figure 1A). In detail, splicing chemistry consists of two sequential scissions of phosphodiester bonds at the 5′- and 3′-intron/exon junctions, respectively. Splicing is a two-step biological reaction whereby introns are excised from precursor RNA molecules and exons are ligated into mature functional protein-coding or noncoding transcripts. In this context, what is the best approach for reliable mechanistic predictions into such large macromolecular systems? How to generate such predictions, and ensure they are valued and exploited by experimentalists? Here, we address these questions with a recent example that shows how the integration of computational and experimental data helped provide key predictive structural and mechanistic insights into vital, ubiquitous, and medically relevant splicing machineries. At a time when technological advances make these multisubunit protein and RNA–protein complexes experimentally tractable, reliably predicting their structures and dynamics can be crucial in rationally guiding and accelerating their characterization and, ultimately, their modulation. Indeed, it is particularly challenging to detail the functional mechanism of very large macromolecular complexes, such as transcriptional, translational, splicing, or protein/RNA degradation machineries. On the other hand, the power of prospective mechanistic insights from computational studies is still often underestimated. Computational tools like molecular dynamics (MD) simulations are therefore powerfully used to interpret experimental data and generate integrative models of biological structures or investigate their complex function, dynamics, and even chemical reactions. However, predicting 3D structures is often not sufficient to provide mechanistic insights for dynamic biological systems. As a matter of fact, structure predictions by new artificial intelligence-driven algorithms have now achieved unprecedented accuracy, at least for single-subunit proteins. Significantly, such activity can accelerate impactful discoveries in life, environmental, and pharmacological sciences. The Market will display after the last display line on the project list page.The challenge of computationally predicting and refining the 3D structure of biological macromolecules has been highly appealing over the last decades. Choose the market from the drop down menu, and click Save. Click on the More menu, and then select "Set Market".ĥ. To set the market on multiple projects at once, go to the project list page and select the projects you want to add the market to. To remove a Market from a project, click on the Market dropdown and select None from the dropdown.Ĥ. Select the market from the dropdown and it will save to the project.ģ. The Region that is associated with that Market is the Region that will be set.Ģ. If you would like more information about upgrading please contact your account executive or contact Go to a project for which you would like to set a Region and/or Market, and click on the "Market" dropdown on the right side underneath the priority. ![]() This feature is a pro and premium feature. If you don’t have Regions set up, no Region will be set, only the market. To set a Market and Region on a project, you must have Markets set up in your portal.
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