proteinmpnn

name	proteinmpnn
description	Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for generated backbones (e.g. from BindCraft), (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use boltzgen (recommended) or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
license	MIT
category	design-tools
tags	["sequence-design","inverse-folding"]
source	https://github.com/adaptyvbio/protein-design-skills

ProteinMPNN Sequence Design

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
CUDA	11.0+	11.7+
GPU VRAM	8GB	16GB (T4)
RAM	8GB	16GB

How to run

Option 1: Local installation (recommended)

git clone https://github.com/dauparas/ProteinMPNN.git
cd ProteinMPNN

python protein_mpnn_run.py \
  --pdb_path backbone.pdb \
  --out_folder output/ \
  --num_seq_per_target 16 \
  --sampling_temp "0.1"

Option 2: Modal (via LigandMPNN wrapper)

cd biomodals
modal run modal_ligandmpnn.py \
  --pdb-path backbone.pdb \
  --num-seq-per-target 16

Key parameters

Parameter	Default	Range	Description
--pdb_path	required	path	Single PDB input
--num_seq_per_target	1	1-1000	Sequences per structure
--sampling_temp	"0.1"	"0.0001-1.0"	Temperature (must be string!)
--pdb_path_chains	all	A,B	Chains to design

Temperature guide

Temp	Effect
0.1	Low diversity, high recovery (production)
0.2	Moderate diversity (default)
0.3	Higher diversity (exploration)
0.5+	Very diverse, lower quality

Common mistakes

✅ --sampling_temp "0.1" — String with quotes ❌ --sampling_temp 0.1 — Float without quotes may cause errors

✅ --pdb_path_chains A,B — No spaces ❌ --pdb_path_chains A, B — Space after comma

Variants Comparison

Variant	Use Case
ProteinMPNN	General
SolubleMPNN	Expression (E. coli)
LigandMPNN	Small molecules/metals

Output format

output/
├── seqs/backbone.fa       # FASTA sequences
└── backbone_pdb/backbone_0001.pdb

Typical performance

Campaign	Time (T4)	Cost (Modal)
100 × 8 seq	15-20 min	~$2
500 × 8 seq	1-1.5h	~$8

Throughput: ~50-100 sequences/minute.

Troubleshooting

Error	Cause	Fix
CUDA out of memory	Long protein	Reduce batch_size
KeyError: 'A'	Chain not in PDB	Check chain IDs
JSONDecodeError	Invalid JSONL	Validate JSON syntax

Next: Structure prediction → protein-qc for filtering.