<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Huang X</submitter><funding>University of Michigan</funding><funding>Cystic Fibrosis Foundation</funding><funding>NHLBI NIH HHS</funding><funding>National Institutes of Health</funding><funding>NIGMS NIH HHS</funding><pagination>bbad133</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10199764</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>24(3)</volume><pubmed_abstract>The critical first step in Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated (CRISPR-Cas) protein-mediated gene editing is recognizing a preferred protospacer adjacent motif (PAM) on target DNAs by the protein's PAM-interacting amino acids (PIAAs). Thus, accurate computational modeling of PAM recognition is useful in assisting CRISPR-Cas engineering to relax or tighten PAM requirements for subsequent applications. Here, we describe a universal computational protein design framework (UniDesign) for designing protein-nucleic acid interactions. As a proof of concept, we applied UniDesign to decode the PAM-PIAA interactions for eight Cas9 and two Cas12a proteins. We show that, given native PIAAs, the UniDesign-predicted PAMs are largely identical to the natural PAMs</pubmed_abstract><journal>Briefings in bioinformatics</journal><pubmed_title>Decoding CRISPR-Cas PAM recognition with UniDesign.</pubmed_title><pmcid>PMC10199764</pmcid><funding_grant_id>R41 GM122181</funding_grant_id><funding_grant_id>R01 HL147527</funding_grant_id><funding_grant_id>HL159871</funding_grant_id><funding_grant_id>R01 HL159900</funding_grant_id><funding_grant_id>R42 GM122181</funding_grant_id><funding_grant_id>XU19XX0</funding_grant_id><funding_grant_id>GM122181</funding_grant_id><funding_grant_id>R01 HL159871</funding_grant_id><funding_grant_id>HL147527</funding_grant_id><funding_grant_id>HL159900</funding_grant_id><funding_grant_id>R41 GM149016</funding_grant_id><funding_grant_id>GM149016</funding_grant_id><pubmed_authors>Xia X</pubmed_authors><pubmed_authors>Zhang J</pubmed_authors><pubmed_authors>Chen YE</pubmed_authors><pubmed_authors>Huang X</pubmed_authors><pubmed_authors>Yang D</pubmed_authors><pubmed_authors>Xu J</pubmed_authors><pubmed_authors>Zhou J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Decoding CRISPR-Cas PAM recognition with UniDesign.</name><description>The critical first step in Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated (CRISPR-Cas) protein-mediated gene editing is recognizing a preferred protospacer adjacent motif (PAM) on target DNAs by the protein's PAM-interacting amino acids (PIAAs). Thus, accurate computational modeling of PAM recognition is useful in assisting CRISPR-Cas engineering to relax or tighten PAM requirements for subsequent applications. Here, we describe a universal computational protein design framework (UniDesign) for designing protein-nucleic acid interactions. As a proof of concept, we applied UniDesign to decode the PAM-PIAA interactions for eight Cas9 and two Cas12a proteins. We show that, given native PIAAs, the UniDesign-predicted PAMs are largely identical to the natural PAMs</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 May</publication><modification>2026-06-01T21:19:41.461Z</modification><creation>2025-06-27T03:05:16.757Z</creation></dates><accession>S-EPMC10199764</accession><cross_references><pubmed>37078688</pubmed><doi>10.1093/bib/bbad133</doi></cross_references></HashMap>