la-proteina-viz

name	la-proteina-viz
description	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.
version	0.1.0

La-Proteina: Config Builder + Visualization

This skill guides Claude through setting up, configuring, and running La-Proteina (NVIDIA) atomistic protein generation, and visualizing results in PyMOL. La-Proteina jointly generates backbone AND sidechains via partially latent flow matching.

Composes with @alphafold-validation (design validation), @design-comparison (batch QC), and @proteina-complexa-viz (binder design extension).

Send all cmd.* code via: ~/.pymol-agent-bridge/bin/pymol-agent-bridge exec "..." (or heredoc for multi-line). See @pymol-fundamentals for details.

Key Difference from RFdiffusion

• Full atomistic output — generates backbone + sidechains jointly (no separate ProteinMPNN step needed)
• Latent representation — side chains encoded as per-residue latent variables
• Supports 100-800 residues across three model variants

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Setup

Environment

mamba env create -f environment.yaml
mamba activate laproteina_env
pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cu118
pip install graphein==1.7.7 --no-deps
pip install torch_geometric torch_scatter torch_sparse torch_cluster

Required Files

Create .env in the repo root:

DATA_PATH=/path/to/your/dataset

Checkpoints go in ./checkpoints_laproteina/.

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Model Selection Guide

Unconditional Generation

Model	Checkpoint	Features	Length Range
LD1	LD1_ucond_notri_512.ckpt	No triangular layers	Up to 500
LD2	LD2_ucond_tri_512.ckpt	Triangular multiplicative layers	Up to 500
LD3	LD3_ucond_notri_800.ckpt	No triangular layers	300-800

Autoencoder pairing:

• LD1, LD2 → AE1_ucond_512.ckpt
• LD3 → AE2_ucond_800.ckpt

Motif Scaffolding

Model	Checkpoint	Motif Type	Atom Detail
LD4	LD4_motif_idx_aa.ckpt	Indexed	All-atom
LD5	LD5_motif_idx_tip.ckpt	Indexed	Tip-atom
LD6	LD6_motif_uidx_aa.ckpt	Unindexed	All-atom
LD7	LD7_motif_uidx_tip.ckpt	Unindexed	Tip-atom

All motif models use: AE3_motif.ckpt

Indexed vs. Unindexed:

• Indexed — motif position in the final sequence is specified (e.g., residues 10-25)
• Unindexed — motif can appear anywhere in the generated protein

All-atom vs. Tip-atom:

• All-atom — condition on complete sidechain atoms of motif residues
• Tip-atom — condition only on functional tip atoms (less restrictive, more diverse scaffolds)

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Config Reference

Base Config

All configs derive from configs/experiment_config/inference_base_release.yaml. Key parameters:

Parameter	Type	Description
ckpt_name	string	Latent diffusion checkpoint filename
autoencoder_ckpt_path	path	Full path to autoencoder checkpoint
self_cond	bool	Enable self-conditioning during sampling
sc_scale_noise	float/dict	Noise scale for backbone and latents

Noise Scale Defaults

Model	Backbone noise	Latent noise
LD1/LD2	0.1	0.1
LD3	0.15	0.05

Generation Codes

Defined in configs/generation/ucond_codes.yaml:

• Controls length distribution and sample count
• Default: 100 samples per length

Motif Task Definition

Motif tasks are defined in configs/generation/motif_dict.yaml with contig-style specifications.

• Tasks without _TIP suffix → use all-atom models (LD4/LD6)
• Tasks with _TIP suffix → use tip-atom models (LD5/LD7)

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Running Generation

Unconditional Generation

# LD1 — no triangular, up to 500 residues
python proteinfoundation/generate.py --config_name inference_ucond_notri

# LD2 — triangular layers, up to 500 residues
python proteinfoundation/generate.py --config_name inference_ucond_tri

# LD3 — no triangular, 300-800 residues
python proteinfoundation/generate.py --config_name inference_ucond_notri_long

Motif Scaffolding

# Indexed all-atom
python proteinfoundation/generate.py --config_name inference_motif_idx_aa

# Indexed tip-atom
python proteinfoundation/generate.py --config_name inference_motif_idx_tip

# Unindexed all-atom
python proteinfoundation/generate.py --config_name inference_motif_uidx_aa

# Unindexed tip-atom
python proteinfoundation/generate.py --config_name inference_motif_uidx_tip

Evaluation

# Download ProteinMPNN weights first
bash script_utils/download_pmpnn_weights.sh

# Run evaluation (co-designability, diversity, RMSD for motif tasks)
python proteinfoundation/evaluate.py --config_name <same_config_name>

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Deciding Which Model to Use

Ask the user:

1. What's the task?

   • "Generate a novel protein fold" → Unconditional (LD1/LD2/LD3)
   • "Scaffold a functional motif" → Motif (LD4-LD7)

2. If unconditional — how long?

   • ≤500 residues → LD1 (fast) or LD2 (higher quality with triangular layers)
   • 300-800 residues → LD3

3. If motif scaffolding — do you know where the motif goes?

   • "Yes, at specific positions" → Indexed (LD4/LD5)
   • "No, anywhere is fine" → Unindexed (LD6/LD7)

4. If motif — how strict on sidechain geometry?

   • "Must preserve exact sidechain positions" → All-atom (LD4/LD6)
   • "Just the functional groups" → Tip-atom (LD5/LD7) — more scaffold diversity

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Visualizing Outputs in PyMOL

Load and Inspect

cmd.load("/path/to/output/sample_0.pdb", "lp_design")
cmd.remove("resn HOH+WAT")
cmd.show("cartoon")
cmd.show("sticks", "sidechain")  # La-Proteina generates real sidechains
cmd.orient()

Fold Quality Check

cmd.dss("lp_design")  # assign secondary structure
cmd.color("marine", "lp_design and ss h")
cmd.color("yellow", "lp_design and ss s")
cmd.color("gray70", "lp_design and ss l+")
n_res = cmd.count_atoms("lp_design and name CA")
print("Design length: %d residues" % n_res)

Sidechain Packing Quality

# La-Proteina generates full sidechains — check packing
cmd.show("sticks", "lp_design and sidechain")
cmd.util.cbag("lp_design")  # color by element, green carbons

# Hydrophobic core
cmd.select("core_hp", "lp_design and resn ALA+VAL+LEU+ILE+MET+PHE+TRP+PRO")
cmd.show("sticks", "core_hp and sidechain")
cmd.color("yellow", "core_hp and elem C")
cmd.show("surface", "lp_design")
cmd.set("transparency", 0.7)

Motif Scaffolding Visualization

motif_resi = "10-25"  # from your motif_dict.yaml config
cmd.select("motif", "lp_design and resi %s" % motif_resi)
cmd.select("scaffold", "lp_design and not motif")

cmd.color("cyan", "motif")       # fixed motif
cmd.color("magenta", "scaffold") # generated scaffold
cmd.show("sticks", "motif")
cmd.show("cartoon", "scaffold")

Motif RMSD (All-Atom)

cmd.load("original_motif.pdb", "motif_ref")
cmd.align("lp_design and resi %s" % motif_resi, "motif_ref")

rms_bb = cmd.rms_cur("lp_design and resi %s and name CA" % motif_resi, "motif_ref and name CA")
rms_all = cmd.rms_cur("lp_design and resi %s and not name H*" % motif_resi, "motif_ref and not name H*")
print("Motif backbone RMSD: %.2f A" % rms_bb)
print("Motif all-atom RMSD: %.2f A" % rms_all)

Metric	Pass	Marginal	Fail
Backbone RMSD	< 0.5 A	0.5-1.0 A	> 1.0 A
All-atom RMSD	< 1.0 A	1.0-2.0 A	> 2.0 A

Grid Tiling for Batch QC

cmd.set("grid_mode", 1)
cmd.show("cartoon")
for obj in cmd.get_object_list():
    cmd.dss(obj)
    cmd.color("marine", "%s and ss h" % obj)
    cmd.color("yellow", "%s and ss s" % obj)
    cmd.color("gray70", "%s and ss l+" % obj)

Co-Designability Check

cmd.load("lp_design.pdb", "design")
cmd.load("af2_prediction.pdb", "af2")
cmd.align("af2 and name CA", "design and name CA")
rms = cmd.rms_cur("af2 and name CA", "design and name CA")
print("Co-designability CA-RMSD: %.2f A" % rms)

# Also check sidechain agreement (unique to atomistic generators)
rms_all = cmd.rms_cur("af2 and not name H*", "design and not name H*")
print("All-atom RMSD: %.2f A" % rms_all)

Tips

• No ProteinMPNN step needed — La-Proteina co-generates sequence and structure. But you CAN still run ProteinMPNN if you want alternative sequences.
• All-atom RMSD is more informative than backbone-only for La-Proteina outputs.
• Tip-atom models (LD5/LD7) give more diverse scaffolds at the cost of less strict sidechain geometry.
• LD2 is the highest-quality unconditional model (triangular layers) but slower.
• Color scheme: cyan=motif, magenta=scaffold (matches @rfd3).
• For validation, see @alphafold-validation. For batch ranking, see @design-comparison.