7nfo

<table><tr><td colspan='2'>[[7nfo]] is a 3 chain structure with sequence from [https://en.wikipedia.org/wiki/Synthetic_construct Synthetic construct]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=7NFO OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=7NFO FirstGlance]. <br>

</td></tr><tr id='ligand'><td class="sblockLbl"><b>[[Ligand|Ligands:]]</b></td><td class="sblockDat" id="ligandDat"><scene name='pdbligand=ACE:ACETYL+GROUP'>ACE</scene>, <scene name='pdbligand=NH2:AMINO+GROUP'>NH2</scene></td></tr>

<div style="background-color:#fffaf0;">

== Publication Abstract from PubMed ==

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling alpha-helical barrels (alphaHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As alphaHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

Differential sensing with arrays of de novo designed peptide assemblies.,Dawson WM, Shelley KL, Fletcher JM, Scott DA, Lombardi L, Rhys GG, LaGambina TJ, Obst U, Burton AJ, Cross JA, Davies G, Martin FJO, Wiseman FJ, Brady RL, Tew D, Wood CW, Woolfson DN Nat Commun. 2023 Jan 24;14(1):383. doi: 10.1038/s41467-023-36024-y. PMID:36693847<ref>PMID:36693847</ref>

From MEDLINE&reg;/PubMed&reg;, a database of the U.S. National Library of Medicine.<br>

</div>

<div class="pdbe-citations 7nfo" style="background-color:#fffaf0;"></div>

== References ==

<references/>

[[Category: Synthetic construct]]