Journal of the American Chemical SocietyBiophysicsPeer-reviewed journal article
Origins of reactivity in SAM-utilizing ribozyme SAMURI-catalyzed RNA alkylation
Julie Puyo-FourtineiD, Yanan DuiD, Erika McCarthyiD, Şölen EkesaniD, Darrin M. YorkiD
Abstract
Unlocking the design principles of programmable RNA catalysts capable of sitespecific chemical modification is critical for expanding the functional and therapeutic potential of RNA. The SAM analogue-utilizing ribozyme (SAMURI) enables sitespecific RNA alkylation using either S-adenosylmethionine (SAM) or the synthetic cofactor propargylic Se-2,6-diaminopurinribosyl-selenomethionineamide (ProSeDMA), yet the molecular determinants of its reactivity remain incompletely understood. Here, we combined molecular dynamics, 3D-RISM solvation analysis, alchemical free-energy calculations, quantum pKa shift predictions, and ab initio QM/MM free-energy simulations to characterize the conformational and electronic factors that govern catalysis. Simulations show that, although the global fold of SAMURI remains stable in solution, the formation of catalytically competent near-attack configurations is rare, indicating that the observed rate depends on access to a minor fraction of these reactive conformations (freact). A putative Mg2+ binding site between the SAM carboxylate and the G30 phosphate, together with a hydrogen bond between the cofactor -amine and U8:O2, enriches freact. QM/MM simulations support an SN2-like alkyl transfer mechanism and show that ProSeDMA reacts more readily than SAM primarily due to its more favorable electronic leaving-group properties that enhance the intrinsic rate (kint). Atomic substitutions at A52 that tune the N3 pKa enhance nucleophilicity, further lower the activation barrier, and increase kint. Together, these results show that SAMURI catalysis is governed by a combination of conformational preorganization and electronic effects, providing a framework to guide the design of new programmable RNA alkyltransferases. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/720726v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@90e0c1org.highwire.dtl.DTLVardef@193b8d6org.highwire.dtl.DTLVardef@14c0d49org.highwire.dtl.DTLVardef@1fc6793_HPS_FORMAT_FIGEXP M_FIG C_FIG
- assay: proteomics
- research focus: protein folding & dynamics
- approach: deep learning
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
- FWCI
- 2.05
- Field percentile
- top 11%
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