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alphafold3-skills

alphafold3

person作者: xbkingagroothubgithub
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AlphaFold 3

Run, analyze, and debug Google DeepMind's AlphaFold 3 inference pipeline.

Prerequisites

Important: AlphaFold 3 must be fully installed on the target machine before using this skill. This includes cloning the repository, building the Docker image, downloading sequence alignment databases, and obtaining model parameters. The skill guides you through inference — it does not install AlphaFold 3.

These are assumed to be in place (the user configured their environment):

  • Repo cloned: git clone https://github.com/google-deepmind/alphafold3.git
  • Docker image built: docker build -t alphafold3 -f docker/Dockerfile .
  • Sequence alignment databases downloaded (~252 GB download, ~630 GB decompressed): ./fetch_databases.sh <DB_DIR>
  • Model parameters obtained: via Google form, saved to <MODEL_PARAMETERS_DIR>
  • Linux host with NVIDIA GPU (A100 80 GB or H100 80 GB recommended, Compute Capability >= 8.0)

If any are missing, see references/running.md → Installation section for setup instructions.

Quick Start

If AlphaFold3 is installed on a remote server, use the remote-server skill for SSH connection, file transfer, and background job management. Then run:

ssh user@host "docker run -it --gpus all ..."

If running locally:

docker run -it \
  --volume $HOME/af_input:/root/af_input \
  --volume $HOME/af_output:/root/af_output \
  --volume <MODEL_PARAMETERS_DIR>:/root/models \
  --volume <DATABASES_DIR>:/root/public_databases \
  --gpus all \
  alphafold3 \
  python run_alphafold.py \
  --json_path=/root/af_input/fold_input.json \
  --model_dir=/root/models \
  --output_dir=/root/af_output

Input JSON

The top-level input structure:

{
  "name": "my_job",
  "modelSeeds": [1, 2],
  "sequences": [
    {"protein": {"id": "A", "sequence": "PVLSCGEWQL", ...}},
    {"rna": {"id": "B", "sequence": "AGCU", ...}},
    {"dna": {"id": "C", "sequence": "GACCTCT", ...}},
    {"ligand": {"id": "D", "ccdCodes": ["ATP"]}},
    {"ligand": {"id": "E", "smiles": "CC(=O)OC1C[NH+]2CCC1CC2"}}
  ],
  "bondedAtomPairs": [[["A", 145, "SG"], ["D", 1, "C04"]]],
  "userCCD": "...",
  "dialect": "alphafold3",
  "version": 4
}

For full details on every entity type, modifications, MSA, templates, bonds, CCD, and version differences, see references/input-format.md.

Companion Tool: af3cli

If the user needs help building the input JSON, use the af3cli skill — it provides a CLI and Python library for generating AF3 input files from sequences, FASTA, SDF, SMILES, and CCD.

Running Inference

See references/running.md for full details on:

  • Basic docker run with all volume mounts
  • Singularity alternative
  • SSD fallback — mount SSD + slower disk
  • Key flags: --run_data_pipeline, --run_inference, --conformer_max_iterations, --jax_compilation_cache_dir, --force_output_dir, --buckets, --save_embeddings, --save_distogram
  • Staged pipeline: data-pipeline-only (--run_inference=false), inference-only (--run_data_pipeline=false), MSA reuse across runs
  • Multiple inputs: use --input_dir for batch processing
  • Performance: compilation buckets, sharded databases, unified memory, JAX compilation cache

Output Interpretation

Directory structure

<job_name>/
├── seed-<seed>_sample-<n>/          # Per-sample subdir
│   ├── <job>_confidences.json        # Full confidence arrays
│   ├── <job>_summary_confidences.json # Summary metrics per chain/pair
│   └── <job>_model.cif               # Predicted 3D structure
├── <job_name>_model.cif              # Top-ranked prediction
├── <job_name>_confidences.json       # Top-ranked confidence
├── <job_name>_summary_confidences.json
├── <job_name>_data.json              # Input + MSA + templates
├── ranking_scores.csv                # All predictions ranked
├── seed-<seed>_distogram/            # (if --save_distogram=true)
└── seed-<seed>_embeddings/           # (if --save_embeddings=true)

Confidence metrics

| Metric | Range | What it means | |--------|-------|---------------| | pLDDT | 0–100 | Per-atom confidence. Higher = better. >90: high, <50: unreliable | | PAE | 0+ (Å) | Predicted aligned error between two tokens. Lower = better | | pTM | 0–1 | Overall fold confidence. >0.5 = likely correct fold | | ipTM | 0–1 | Interface confidence. >0.8 = high, <0.6 = likely failed | | ranking_score | -100–1.5 | Composite for ranking: 0.8×ipTM + 0.2×pTM + 0.5×disorder − 100×clash |

The top-ranked prediction (highest ranking_score) is always copied to the root directory.

Per-chain / per-pair metrics (in summary JSON)

  • chain_ptm[i] — pTM for chain i alone
  • chain_pair_iptm[i][j] — ipTM for interface between chains i and j
  • chain_pair_pae_min[i][j] — minimum PAE between chains i and j (useful for binder/non-binder classification)
  • chain_iptm[i] — average ipTM of chain i vs all other chains

Common workflows

  • Rank by specific interface: use chain_pair_iptm for the chain pair of interest
  • Rank by single chain: use chain_ptm for that chain
  • Check binding: chain_pair_pae_min < 10 suggests interaction; > 15 suggests no interaction
  • Select best model: sort ranking_scores.csv by ranking_score descending
  • Chirality check: see src/alphafold3/model/scoring/chirality.py::compare_chirality

Model Architecture

See references/model-architecture.md for a deep dive into:

  • Evoformer Trunk: 48 Pairformer layers processing MSA and template data into single/pair embeddings. MSA channel=64, seq channel=384, pair channel=128.
  • Diffusion Head: Denoising diffusion process (SIGMA_DATA=16.0, 5 samples) generating 3D atom coordinates from learned noise.
  • Confidence Head: Predicts pLDDT, PAE, pTM, ipTM from trunk embeddings and predicted structure.
  • Ranking Formula: 0.8 × ipTM + 0.2 × pTM + 0.5 × disorder − 100 × clash
  • Key Config: GlobalConfig (bfloat16, flash attention, sharding), num_recycles=10, num_diffusion_samples=5
  • Flash Attention: Triton (default) / cuDNN / XLA via tokamax library

Data Pipeline

See references/data-pipeline.md for a deep dive into:

  • MSA Search: Jackhmmer (protein) and Nhmmer (RNA) against sequence alignment databases (BFD, MGnify, UniRef90, UniProt, RNAcentral, NT RNA, Rfam)
  • Template Search: Hmmsearch against PDB mmCIF structures
  • Sharded Databases: Split FASTA into shards for 10-30× parallel speedup
  • Featurization Pipeline: RDKit conformers → atom layout → MSA features → template features → batch assembly
  • Staged Pipeline: Separate data pipeline (CPU) from inference (GPU) for MSA reuse and distributed execution
  • Database Configuration: All JackhmmerConfig, NhmmerConfig, HmmsearchConfig, DatabaseConfig options

Performance Tuning

See references/running.md → Performance section for:

  • Compilation buckets (--buckets 256,512,...,5376)
  • Sharded sequence alignment databases (10–30× speedup on multi-core machines)
  • Unified memory for >5120 tokens
  • JAX persistent compilation cache (--jax_compilation_cache_dir)

Troubleshooting

See references/troubleshooting.md for:

  • V100 produces bad output (clashes, ranking_score -99) — set XLA_FLAGS
  • SMILES with two-letter atoms (Cl, Br) — check git commit range
  • MSA discrepancy vs AlphaFold Server — --domE flag tuning
  • RDKit conformer failure — --conformer_max_iterations or user CCD
  • Permission errors on database directories
  • Docker mount permission denied

Development

See references/development.md for:

  • Building from Source: CMake + pybind11 C++ extensions, uv package manager, Docker build
  • Dependencies: JAX 0.9.1, Haiku 0.0.16, RDKit 2025.9.4, tokamax 0.0.11
  • Running Tests: GPU inference tests (run_alphafold_test.py) and CPU data pipeline tests (run_alphafold_data_test.py)
  • Test Data: Miniature databases, featurised examples, golden outputs for regression testing
  • Debugging: Key code locations for common issues, useful development flags
  • C++ Extensions: cif_dict, msa_profile, mkdssp via pybind11

Codebase Navigation

See references/codebase-guide.md for:

  • End-to-End Data Flow: From input JSON → MSA/templates → featurization → model → output mmCIF
  • Directory Map: Every source file with line counts and descriptions
  • Configuration Hierarchy: GlobalConfig → Model.Config → DataPipelineConfig
  • Key Entry Points: Functions to call for running, building input, data pipeline, model inference, structure I/O
  • C++ Extensions: What each compiled module provides

References in This Skill

  • references/input-format.md — Complete input JSON reference
  • references/running.md — Docker/Singularity commands, flags, staged pipelines, performance
  • references/troubleshooting.md — Known issues and solutions
  • references/model-architecture.md — Model internals: Evoformer, Diffusion Head, Confidence Head, ranking formula
  • references/data-pipeline.md — Data pipeline: MSA search, template search, sharded databases, featurization
  • references/development.md — Building from source, dependencies, testing, debugging, C++ extensions
  • references/codebase-guide.md — Full source code map, data flow, entry points, configuration hierarchy