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Use when inspecting RFdiffusion/RFdiffusion2/RFdiffusion3 outputs, viewing diffusion trajectories, tiling scaffold candidates, or performing visual QC on generated backbones through PyMOL.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | rfdiffusion-viz |
| description | Use when inspecting RFdiffusion/RFdiffusion2/RFdiffusion3 outputs, viewing diffusion trajectories, tiling scaffold candidates, or performing visual QC on generated backbones through PyMOL. |
| version | 0.1.0 |
Workflows for inspecting and QC'ing RFdiffusion backbone outputs in PyMOL. Composes with @rfd3 (config building) and @proteinmpnn-viz (sequence design step).
Send all
cmd.*code via:~/.pymol-agent-bridge/bin/pymol-agent-bridge exec "..."(or heredoc for multi-line). See @pymol-fundamentals for details.
cmd.load("/path/to/output/design_0.pdb", "design_0")
cmd.remove("resn HOH+WAT")
cmd.show("cartoon")
cmd.orient()
import glob, os
output_dir = "/path/to/rfdiffusion/output"
for f in sorted(glob.glob(os.path.join(output_dir, "*.pdb"))):
name = os.path.splitext(os.path.basename(f))[0]
cmd.load(f, name)
RFdiffusion outputs trajectory files in a /traj/ subdirectory as multi-state PDBs showing the diffusion process from noise to structure.
cmd.load("/path/to/output/traj/design_0_pX0_traj.pdb", "trajectory")
cmd.show("cartoon", "trajectory")
cmd.spectrum("count", "blue_white_red", "trajectory")
# Play through states
cmd.mset("1 -%d" % cmd.count_states("trajectory"))
cmd.mplay()
# First frame (noise)
cmd.frame(1)
# Last frame (final structure)
cmd.frame(cmd.count_states("trajectory"))
# Midpoint
n = cmd.count_states("trajectory")
cmd.frame(n // 2)
Tile all designs in a grid for rapid visual screening.
# Load all outputs first, then:
cmd.set("grid_mode", 1)
cmd.show("cartoon")
# Optional: color each by chain
for obj in cmd.get_object_list():
cmd.do("util.cbc " + obj)
cmd.set("grid_mode", 0)
Color designed vs. fixed regions using the standard design color scheme (see @rfd3 for full scheme).
# Color scheme: cyan=fixed/motif, magenta=designed, yellow=ligand
# Assuming the contig was: [5-25/A10-30/30-50]
# Residues 1-25 are designed, 26-46 are from motif (A10-30), 47-71 are designed
cmd.color("magenta", "design_0 and resi 1-25") # designed
cmd.color("cyan", "design_0 and resi 26-46") # fixed motif
cmd.color("magenta", "design_0 and resi 47-71") # designed
cmd.show("sticks", "design_0 and resi 26-46") # show motif as sticks
# If you know the motif residues from the input PDB:
cmd.select("motif_region", "design_0 and resi 26-46")
cmd.select("designed_region", "design_0 and not motif_region")
cmd.color("cyan", "motif_region")
cmd.color("magenta", "designed_region")
cmd.show("sticks", "motif_region")
cmd.show("cartoon", "designed_region")
Align the RFdiffusion output to the AlphaFold2 prediction of the designed sequence to validate designability. See @alphafold-validation for the AF2 side.
# Load both
cmd.load("rfdiffusion_output.pdb", "design")
cmd.load("af2_prediction.pdb", "af2_pred")
# Align on CA atoms
rms = cmd.align("af2_pred and name CA", "design and name CA")
print("Self-consistency RMSD: %.2f A" % rms[0])
# Color by deviation
cmd.color("green", "af2_pred")
cmd.color("cyan", "design")
| Metric | Pass | Marginal | Fail |
|---|---|---|---|
| CA-RMSD | < 1.5 A | 1.5-2.5 A | > 2.5 A |
| pLDDT (AF2) | > 80 | 60-80 | < 60 |
For motif scaffolding designs, verify the motif geometry is preserved.
# Align only the motif residues
# Suppose motif is residues 26-46 in the design, 10-30 in the original
cmd.align("design and resi 26-46 and name CA", "original and chain A and resi 10-30 and name CA")
# Per-atom RMSD for motif
rms = cmd.rms_cur("design and resi 26-46 and name CA", "original and chain A and resi 10-30 and name CA")
print("Motif RMSD: %.2f A" % rms)
| RMSD | Interpretation |
|---|---|
| < 0.5 A | Excellent motif preservation |
| 0.5-1.0 A | Good — minor backbone shifts |
| 1.0-2.0 A | Marginal — check contacts |
| > 2.0 A | Poor — motif geometry distorted |
For binder design runs (target + designed binder), visualize the complex.
cmd.load("binder_design.pdb", "complex")
# Identify chains
chains = cmd.get_chains("complex")
# Typically: target=chain A, binder=chain B
cmd.color("gray80", "chain A") # target
cmd.color("marine", "chain B") # designed binder
cmd.show("surface", "chain A")
cmd.set("transparency", 0.7, "chain A")
cmd.show("cartoon", "chain B")
# Show designed binder residues contacting the target
cmd.select("interface_binder", "byres (chain B within 4 of chain A)")
cmd.select("interface_target", "byres (chain A within 4 of chain B)")
cmd.show("sticks", "interface_binder")
cmd.show("sticks", "interface_target")
# H-bonds across the interface
cmd.distance("interface_hbonds", "chain A", "chain B", mode=2)
cmd.set("dash_color", "yellow", "interface_hbonds")
cmd.load("symmetric_design.pdb", "assembly")
cmd.do("util.cbc assembly") # color by chain
cmd.show("cartoon")
# Show inter-subunit contacts
chains = cmd.get_chains("assembly")
if len(chains) >= 2:
cmd.select("interface_AB", "byres (chain %s within 4 of chain %s)" % (chains[0], chains[1]))
cmd.show("sticks", "interface_AB")
cmd.remove("resn HOH+WAT")grid_mode=1) is essential for screening >5 designsUse when working with PyMOL for molecular visualization tasks including loading structures, creating representations, coloring, selections, and basic analysis.
Use when connecting Claude to PyMOL, troubleshooting socket errors, or setting up the PyMOL integration for the first time
Use when setting up, configuring, running, or inspecting La-Proteina protein generation tasks including unconditional design and motif scaffolding. Helps build configs and visualize outputs through PyMOL.
Use when setting up, configuring, running, or inspecting Proteina-Complexa protein binder design, ligand binder design, AME motif scaffolding, or monomer motif scaffolding. Helps build configs, select targets, and visualize outputs through PyMOL.
Use when validating protein designs with AlphaFold2/AlphaFold3/ESMFold predictions, coloring by pLDDT or pAE, computing self-consistency RMSD, or screening design candidates through PyMOL.
Use when comparing multiple protein designs, ranking design candidates, tracking design iterations, overlaying before/after structures, or performing batch visual QC through PyMOL.