pegPLAND Comprehensive Tutorial

pegPLAND is a state-of-the-art computational platform tailored for designing and analyzing prime editing guide RNAs (pegRNAs) in plant genomes.

Step 1: Inputting Your Sequences

Begin by providing the DNA sequences in the Guide Design tab.

  • Wildtype/Reference Sequence: The original, unmodified DNA sequence.
  • Edited/Desired Sequence: The DNA sequence containing your intended mutation.
Tip: For the predictions along with pegRNAs, ensure your target edit has at least 99 base pairs upstream and downstream of the edit.

Step 2: Configuring Parameters

Under the Parameters tab, you can fine-tune your prime editing system:

  • PAM Sequence: Choose from NGG, NG, or a Custom user-defined sequence.
  • Cut distance to PAM: Define the cleavage position offset (default -3).
  • Spacer length: (Range 1-40) Adjust the standard spacer length.
  • Spacer GC content (%): Constrain GC pairs in the spacer sequence (0-100%).
  • Prime editing window: Focus the edit inclusion bounds (1-15).
  • PBS length: Set exact lengths (7-16) for the PBS.
  • PBS GC content (%): Define GC boundaries (0-100%) for the PBS.
  • Recommended Tm of PBS sequence: Directly control the melting temperature (default 30°C).
  • Homologous RT template length: (Range 7-16) Adjust the RT template size.
  • Toggle Options: Include the Tm-directed PBS length model, Dual-pegRNA model processing, or Exclude first C in RT template.

Step 3: Optimization & Primers

Under the Optimization tab, configure the primers required for the assembly of your pegRNA expression vectors:

  • Pre-configured Primer Types: Instantly select standard plant editing system architectures like pOsU3, pTaU3, pTaU6, or pH-nCas9-PPE-V2 to auto-load the necessary primers.
  • Custom Primers: Alternatively, choose Custom to explicitly define your own Forward primer, Reverse primer, and Scaffold-RT sequence manually.

Step 4: Interpreting Results & Structure Analysis

After clicking Design pegRNA, the algorithm calculates permutations against thermodynamic boundaries and scoring models.

  • Program & Recommendation Rows: Results are systematically grouped into programs (highlighted in green). Within each program grouping, the most thermodynamically and functionally optimal PBS and RT template parameters for a design are designated as Recommended! (highlighted in red).
  • Column Features: Each row explicitly delineates the designed sequences for the Spacer-PAM, Linker, PBS, and RT Template, as well as indicating the target sequence Strand orientation (Sense/Antisense).
  • Efficiency Score: Predicted editing efficiency, utilizing a varaiant of deep-learning tool algorithm (PRIDICT2.0), adopting the baseline (HEK293T) score.
  • Secondary Structure (gpegRNA): Visualizes the standard folded RNA string, along with the specific modification of the last three nucleotides of the 86-nucleotide scaffold sequence, demonstrating exact dot-bracket base pairing. The 2D representations showcase specific structures like spacer, scaffold, RT, and PBS sections correctly aligned.
  • Engineered pegRNA (egpegRNA) Structure: Displays an advanced structural variant that utilizes an optimal linker sequence to attach a 3' protective structured motif (such as the evopreQ1 sequence) to the RT template. The linker prevents structural interference, while the motif protects the synthesized transcript from exonuclease degradation, stabilizing it and enhancing prime editing performance in vivo.

Frequently Asked Questions