Journal: Nucleic Acids Research

Article Title: Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis

doi: 10.1093/nar/gkaf1529

Figure Lengend Snippet: Design of a highly active truncated PUP. ( a ) Residue-wise C α B-factor profile of WT PUP derived from MD simulations, highlighting regions of high backbone flexibility. ( b ) Root mean square deviation (RMSD) values calculated for backbone atoms of the PUP and PUPdel protein with the initial structure as the reference. ( c ) Conformational analysis of PUP and PUPdel with 3′- O -allyl-NTPs. The bar chart compares a key dihedral angle (θ) in the full-length enzyme (PUP, purple) and the truncated variant (PUPdel, blue) upon binding to 3′- O -allyl-NTPs. The inset illustrates the structural definition of the measured dihedral angle θ. Data are presented as the mean ± standard deviation (SD). ( d ) Comparison of calculated binding free energies for 3′- O -allyl-NTPs with WT PUP and PUPdel. ( e ) Structural superposition highlighting key conformational differences, particularly in the β-trapdoor region and active site residue positioning, between representative stable states of WT PUP and PUPdel from GaMD simulations. ( f ) Representative binding mode of the modified substrate with PUPdel. The complex conformation was derived from GaMD simulations and selected from the most populated cluster in the trajectory analysis to illustrate the predominant binding state. ( g ) Gel electrophoresis analysis comparing the incorporation capability of four 3′- O -allyl-NTPs by WT PUP versus PUPdel. ( h ) Michaelis–Menten plots depicting the reaction velocity of PUPdel as a function of varying concentrations for each 3′- O -allyl-NTP substrate.

Article Snippet: Here, we describe a multi-round, closed-loop workflow integrating Gaussian accelerated molecular dynamics (GaMD), machine learning (ML), and generative artificial intelligence (AI) to engineer PUP variants with enhanced activity and stability.

Techniques: Residue, Derivative Assay, Variant Assay, Binding Assay, Standard Deviation, Comparison, Modification, Nucleic Acid Electrophoresis

Journal: Nucleic Acids Research

Article Title: Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis

doi: 10.1093/nar/gkaf1529

Figure Lengend Snippet: Analysis of the catalytic mechanism of the N159F variant. ( a ) Comparison of C α RMSF profiles for the N159F variant and PUPdel derived from GaMD simulations. The N159F variant exhibits significantly increased flexibility compared with the parental enzyme, particularly in the active site lid loop (residues 296–310). ( b ) Time-evolution analysis of the secondary structure for key flexible loops (116–152 and 296–310). ( c ) Representative conformations of the active site in the PUPdel (left) and the N159F variant (right) when complexed with 3′- O -allyl-UTP. The N159F mutation introduces a phenylalanine residue that establishes a favorable π–π stacking interaction with the uracil base of the substrate, anchoring it in a more stabilized binding conformation. Key interacting residues are shown as sticks. ( d ) Average number of hydrogen bonds formed between the enzyme’s active site and the nucleotide substrate. The N159F variant consistently forms a greater number of stable hydrogen bonds with both 3′- O -allyl-UTP and 3′- O -allyl-CTP, indicative of enhanced substrate binding affinity and stability. ( e and f ) Dynamic analysis of the active site cleft, monitored by the distance between the C α atoms of I130 and D310, in the presence of (e) 3′- O -allyl-UTP and (f) 3′- O -allyl-CTP. The N159F variant sustains a significantly smaller and more stable inter-residue distance, suggesting a more compact and catalytically competent “closed” conformation of the active site compared with the parental PUPdel. Representative snapshots illustrate this conformational difference.

Article Snippet: Here, we describe a multi-round, closed-loop workflow integrating Gaussian accelerated molecular dynamics (GaMD), machine learning (ML), and generative artificial intelligence (AI) to engineer PUP variants with enhanced activity and stability.

Techniques: Variant Assay, Comparison, Derivative Assay, Mutagenesis, Residue, Binding Assay