ProtCross

Install: PyPI / CLI · Windows x64 Desktop · macOS Apple Silicon Desktop

ProtCross is a protein binding-site prediction tool for experimental and predicted PDB/mmCIF structures, including AlphaFold models. It converts protein coordinates into residue-level binding-site scores, ranked spatial clusters, centroids, annotated structures, and provenance for structure triage, receptor preparation, docking setup, and experimental prioritization. The method is described in ProtCross: Bridging the PDB-AlphaFold Gap for Binding Site Prediction with Protein Point Clouds, published in the Journal of Chemical Information and Modeling.

Predicted structures have expanded structural coverage, yet a single unrefined model often requires site-centred assessment before downstream atomistic modelling. Global fold accuracy and pLDDT alone do not establish a ligand-compatible pocket: local side chains, loops, conformational state, cofactors, ions, waters, and biological assembly can materially alter docking poses and virtual-screening enrichment (Holcomb et al., 2023; Karelina et al., 2023; Lyu et al., 2024).

Quick start

Choose the interface that matches your task:

Goal	Start here
Run a local prediction from a terminal	Install the CLI below
Call ProtCross from a Python workflow	Install the CLI, then open Python API
Use a guided interface and 3D viewer	Download ProtCross Desktop

ProtCross 0.2.3 requires Python 3.10. This first run installs the prediction dependencies, prepares the managed model assets, checks a structure, and writes one result package:

python3.10 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "protcross[predict]"

protcross setup-assets --accept-esm-license
protcross inspect input.pdb
protcross predict input.pdb --out-dir protcross-results

Review the ESM-C model terms before recording acceptance. Initial asset setup downloads approximately 2.14 GiB; later predictions reuse the verified local cache.

The prediction command creates:

protcross-results/
├── input.protcross.pdb
├── input.protcross.scores.tsv
├── input.protcross.pockets.json
└── input.protcross.summary.json

For a graphical workflow, install the Windows x64 or macOS Apple Silicon build from GitHub Releases, then follow the three readiness steps in Setup. Desktop manages its own runtime and assets and opens completed predictions in Mol*.

Contents

• Quick start
• Highlights
• Installation
• Run predictions
• Output package
• Batch inference
• Python API
• Assets
• Desktop application
• Model and inference pipeline
• Training and development
• Troubleshooting
• Version history
• Citation
• License

Highlights

• Inspect PDB, mmCIF, and AlphaFold coordinate files before model loading
• Score residues and rank spatial binding-site clusters with centroids
• Export annotated coordinates, a full residue table, cluster JSON, and run provenance
• Run on CPU, CUDA, or Apple MPS through the CLI, Python API, or local Desktop
• Reuse verified assets and reduced ESM/PCA features across repeated work
• Process structure collections with bounded ESM-C and PointNet++ microbatches
• Review persistent Desktop batches and regroup completed results interactively

Installation

Requirements

Workflow	Requirements
CLI and Python inference	Python 3.10, PyTorch 2.3, local runtime assets
Desktop inference	Windows 10/11 x64 or macOS 12+ Apple Silicon
Training	Python 3.10, Conda, CUDA recommended
Desktop development	Node.js 20, Rust 1.88, Tauri 2 system packages

ProtCross declares python >=3.10,<3.11. Create a dedicated Python 3.10 environment, choose the platform command below, and confirm the installed version before preparing assets.

Linux CPU

python3.10 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install \
  torch==2.3.1+cpu torchvision==0.18.1+cpu \
  --index-url https://download.pytorch.org/whl/cpu
python -m pip install "protcross[predict]"
protcross --version

Linux CUDA 12.1

Install the CUDA wheel matching the PyTorch 2.3 runtime, then install ProtCross:

python3.10 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install \
  torch==2.3.1+cu121 torchvision==0.18.1+cu121 \
  --index-url https://download.pytorch.org/whl/cu121
python -m pip install "protcross[predict]"
python -c "import torch; print(torch.cuda.is_available(), torch.version.cuda)"

Use the PyTorch installation selector when the host driver requires another PyTorch 2.3 wheel.

macOS Apple Silicon

python3.10 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "protcross[predict]"
protcross --version

PyTorch supplies Apple Silicon wheels through the Python package index.

Windows PowerShell

py -3.10 -m venv .venv
.\.venv\Scripts\python.exe -m pip install --upgrade pip
.\.venv\Scripts\python.exe -m pip install `
  torch==2.3.1+cpu torchvision==0.18.1+cpu `
  --index-url https://download.pytorch.org/whl/cpu
.\.venv\Scripts\python.exe -m pip install "protcross[predict]"
.\.venv\Scripts\protcross.exe --version

Activate the environment to use the shorter commands shown throughout this README.

After installation, run protcross setup-assets --accept-esm-license once, or open Assets to select another cache location or an existing ESM-C file.

Run predictions

Inspect a structure

protcross inspect parses coordinate metadata without loading model assets. The repository includes examples/6fhu.pdb for a first check, so input preparation can be tested before the 2.14 GiB ESM-C download.

protcross inspect input.cif
protcross inspect input.cif --chain A
protcross inspect input.cif --json

Use the report to choose a chain, identify missing Cα atoms or modified residues, and check whether a chain exceeds the 1,022-residue ESM-C context. --json sends successful and failed inspections to stdout as machine-readable JSON and uses the process exit code to signal success or failure.

Input: examples/6fhu.pdb
Format: PDB
Models: 1
Chains: A
Scorable residues: 52
Longest chain context: 52 / 1022
Ready for prediction.

Predict one structure

protcross predict input.pdb --out-dir results

Select one chain or an available accelerator when the task requires it:

protcross predict input.cif --chain A --out-dir results
protcross predict input.pdb --device auto --out-dir results

Common options:

Option	Default	Function
--chain ID	all chains	Select one author chain ID
--device	cpu	Select cpu, cuda, cuda:N, mps, or auto
--threshold	0.5	Select residues with score > threshold
--pocket-cluster-cutoff	8.0	Set the Cα graph cutoff in Å
--max-len	1022	Set the per-chain ESM-C residue limit
--allow-truncation	disabled	Score the leading max_len residues of each long chain
--embedding-cache-dir	unset	Cache reduced ESM/PCA residue features
--overwrite	disabled	Replace an existing result package
--offline	disabled	Restrict asset resolution to local files

The CLI writes four result files unless --summary-only is selected. Progress messages go to stderr; the terminal summary goes to stdout. Use --quiet when another process only needs the files and exit code.

Set explicit output paths when a workflow owns the file layout:

protcross predict input.cif \
  --chain A \
  --output results/input.protcross.cif \
  --scores-tsv results/input.protcross.scores.tsv \
  --pocket-json results/input.protcross.pockets.json \
  --summary-json results/input.protcross.summary.json

Structure handling

Input property	Processing rule
File format	.pdb, .cif, or .mmcif
Coordinate model	First model
Assembly	Coordinates supplied in the input file
Scorable residue	Standard polymer amino acid with a Cα atom
Geometry	One centered Cα point cloud across selected chains
Sequence context	Coordinate-observed sequence, embedded independently per chain
Chain selection	All scorable chains or one --chain value
Maximum context	1,022 scorable residues per chain
Canonical order	Model, author chain, label/auth sequence position, insertion code
Alternate Cα	Bio.PDB-selected conformer
Modified residue	Omitted from geometry and sequence context
Coordinate break	Observed residues remain in the same per-chain ESM-C sequence

The parser retains author and label identifiers from mmCIF, insertion codes, residue names, input B-factors, raw Cα coordinates, and the source-file hash.

Command reference

Command	Purpose
protcross inspect	Validate and summarize a coordinate file
protcross predict	Run single-structure inference
protcross setup-assets	Install and verify runtime assets
protcross preprocess	Convert structures into training tensors
protcross download-af2	Download matched AlphaFold structures
protcross map-labels	Transfer PDB-derived labels to AF2 samples
protcross train	Launch the Hydra/Lightning training workflow

protcross --help
protcross COMMAND --help

Output package

Files

Task	File to use	Contents
Color or share the scored structure	input.protcross.pdb or .cif	Input coordinates with residue scores in B-factor fields
Rank and filter every scored residue	input.protcross.scores.tsv	Identifiers, scores, calls, coordinates, cluster IDs, and ranks
Use predicted sites in a script	input.protcross.pockets.json	Thresholded residue clusters, members, centroids, and spatial statistics
Audit or reproduce a run	input.protcross.summary.json	Parameters, asset and input hashes, runtime, warnings, and top results

PDB annotation preserves record order and updates B-factor columns on ATOM/HETATM records. mmCIF annotation retains coordinate categories and serializes an updated document. PDB B-factor values use two decimal places; TSV and JSON retain full numeric precision. The default --unscored-bfactor-policy zero assigns 0.0 to unscored atoms in the scored model. The keep policy retains input values for unscored atoms. Additional coordinate models retain their original values.

Scores and clusters

Stage	Definition
Residue score	softmax(logits, dim=1)[:, 1]
Binary call	score > threshold
Cluster graph	Selected Cα pairs with distance <= cluster_cutoff
Components	Single-linkage connected components, including singletons
Cluster order	Descending count/mean/maximum, then ascending canonical index
Cluster center	Score-weighted Cα centroid

Start analysis with model_score and rank in the TSV. Higher scores indicate stronger model support for the binding-site class. Scores are continuous model outputs and are not independently calibrated probabilities. probability is a schema compatibility alias for model_score.

The threshold controls binary calls and cluster membership; it does not change the underlying scores. Empty selections produce zero clusters and null aggregate/top-cluster entries. Selected chains share one geometry graph, so a cluster can span a chain interface.

Schemas

• protcross-pocket-v2 for pockets.json
• protcross-summary-v2 for summary.json
• extended TSV columns for PDB and mmCIF residue identifiers

The JSON package records the application version, scoring procedure, selected asset bundle, asset hashes, input SHA256, threshold, clustering parameters, device, precision, and effective microbatch size.

Batch inference

Use one ProtCrossPredictor to process a directory of structures. ProtCross 0.2.3 preserves input order, keeps each structure in its own result directory, and bounds ESM-C and PointNet++ microbatches by count and residue cost.

from pathlib import Path

from protcross.inference import ProtCrossPredictor

structure_dir = Path("structures")
inputs = sorted(
    path
    for path in structure_dir.iterdir()
    if path.is_file() and path.suffix.lower() in {".pdb", ".cif", ".mmcif"}
)
if not inputs:
    raise FileNotFoundError(f"No PDB/mmCIF structures found in {structure_dir}")

output_dir = Path("batch-results")
output_dir.mkdir(parents=True, exist_ok=True)

predictor = ProtCrossPredictor.from_default_assets(
    device="auto",
    embedding_cache_dir=".protcross-feature-cache",
    accept_esm_license=True,
)

output_paths = []
for index, path in enumerate(inputs, start=1):
    result_dir = output_dir / f"{index:04d}-{path.stem}"
    result_dir.mkdir(parents=True, exist_ok=True)
    structure_suffix = ".cif" if path.suffix.lower() in {".cif", ".mmcif"} else ".pdb"
    output_paths.append(
        {
            "output_pdb": result_dir / f"{path.stem}.protcross{structure_suffix}",
            "scores_tsv": result_dir / f"{path.stem}.protcross.scores.tsv",
            "pocket_json": result_dir / f"{path.stem}.protcross.pockets.json",
            "summary_json": result_dir / f"{path.stem}.protcross.summary.json",
        }
    )

results = predictor.predict_many(
    inputs,
    output_paths=output_paths,
    batch_size=4,
    max_batch_residues=4096,
    max_batch_quadratic_cost=4 * 1022**2,
    return_exceptions=True,
)

for path, result in zip(inputs, results):
    if isinstance(result, Exception):
        print(f"FAILED {path}: {result}")
    else:
        print(f"DONE   {path}: {result.output_files['summary_json']}")

The per-input result directories keep files distinct when structures share a stem or use different coordinate formats. return_exceptions=True lets the remaining inputs finish and keeps each exception in its original list position. Pass chain_ids=[None, "A", ...] to choose a chain independently for each input; None selects all scorable chains. The list must follow inputs order.

Scheduler controls

Parameter	Default	Budget
batch_size	4	Maximum PointNet++ graphs per microbatch
max_batch_residues	4096	Maximum total graph nodes
max_batch_quadratic_cost	4 * 1022**2	Maximum sum(n_i**2) geometry cost
feature_batch_size	CPU: min(batch_size, 2); accelerator: batch_size	Maximum ESM-C sequences per microbatch
max_feature_padded_tokens	2048	Maximum padded ESM-C token matrix
return_exceptions	False	Per-item exception collection

Each structure remains one PointNet++ graph and each chain remains one ESM-C context. Identical chain sequences share feature extraction within a microbatch. Persistent cache entries include the sequence, PCA dimension, context limit, cache schema, and ESM/PCA asset identity.

The residue and quadratic-cost settings bound both a microbatch and each individual structure. Select one chain or raise an explicit limit when a graph exceeds that budget. Accelerator memory errors trigger recursive microbatch splitting; a single item that still exhausts memory is reported through the selected exception mode. Desktop batch jobs use this API with groups of four.

With the default exception mode, the return type is list[PredictionResult]. With return_exceptions=True, it is list[PredictionResult | Exception]. Each output_paths entry accepts the four writer names shown in the example.

Python API

Single-structure helper

Use predict_pdb for a script that scores one structure and writes a complete result package:

from pathlib import Path

from protcross.inference import predict_pdb

output_dir = Path("results")
output_dir.mkdir(parents=True, exist_ok=True)

result = predict_pdb(
    "examples/6fhu.pdb",
    device="cpu",
    accept_esm_license=True,
    output_pdb="results/6fhu.protcross.pdb",
    scores_tsv="results/6fhu.protcross.scores.tsv",
    pocket_json="results/6fhu.protcross.pockets.json",
    summary_json="results/6fhu.protcross.summary.json",
)

print(result.format_summary())

predict_pdb resolves and downloads missing managed assets by default. Set offline=True for a local-cache-only run.

Reusable predictor

Load ProtCrossPredictor once when a process will score several structures:

from protcross.inference import ProtCrossPredictor

predictor = ProtCrossPredictor.from_default_assets(
    device="cpu",
    accept_esm_license=True,
)

result = predictor.predict("examples/6fhu.pdb", threshold=0.5)
records = result.to_records()
pockets = result.to_pocket_dict()
summary = result.to_summary_dict()

Use one predictor per device worker and serialize calls that share an instance. Independent processes load independent model instances. Python writers publish each output file through atomic replacement and keep the annotated structure in the input coordinate format.

Structure inspection is also available from Python:

from protcross.data import inspect_structure

inspection = inspect_structure("examples/6fhu.pdb")

PredictionResult interface

Member	Value
scores	Read-only NumPy residue-score array
binding_residues	Residues above the active threshold
to_records()	Extended residue dictionaries
to_pocket_dict()	Cluster schema payload
to_summary_dict()	Run summary payload
format_summary()	Terminal-oriented text summary
write_pdb()	Annotated coordinate output
write_scores_tsv()	Residue table output
write_pocket_json()	Cluster JSON output
write_summary_json()	Provenance JSON output

Assets

Managed assets

Prediction uses three matched assets: a ProtCross checkpoint, its PCA reducer, and ESM-C 600M weights. For most users, install the managed bundle once:

protcross setup-assets --accept-esm-license

The command verifies and installs these files under ~/.cache/protcross/assets/v0.1.2:

protcross-0.1.2-binding-moad-final.ckpt
pca_esmc_128_binding_moad_0.1.2.pkl
esmc_600m_2024_12_v0.pth
protcross-assets.json

The ESM-C download is approximately 2.14 GiB. Interrupted transfers resume from retained partial data. Setup verifies SHA256 hashes and publishes completed files atomically. Later predictions reuse the manifest verification while file size and modification time remain unchanged.

Bundle	Checkpoint and PCA
default, latest, 0.1.2	protcross-0.1.2-binding-moad-final.ckpt and pca_esmc_128_binding_moad_0.1.2.pkl
0.1.1-paper	best-epoch=59.ckpt and pca_esmc_128.pkl for the published PDBbind v2020 workflow

Release compatibility:

Interface	Version
Application and Desktop	0.2.3
Default checkpoint/PCA bundle	0.1.2
Paper reproduction bundle	0.1.1-paper
Pocket and summary schemas	protcross-pocket-v2, protcross-summary-v2

default and latest resolve to the bundle pinned by the installed package. Keep the checkpoint and PCA reducer from the same bundle.

Configure another managed directory with either interface:

PROTCROSS_ASSETS_DIR=/data/protcross-assets \
  protcross setup-assets --accept-esm-license

protcross setup-assets \
  --output-dir /data/protcross-assets \
  --accept-esm-license

Use --refresh-assets for a fresh download and verification. Use --offline or --no-auto-assets to limit prediction to local files.

Existing or custom assets

Reuse an existing ESM-C file with an absolute path:

protcross setup-assets --skip-esm --accept-esm-license
protcross predict input.pdb \
  --esm-weights /absolute/path/to/esmc_600m_2024_12_v0.pth \
  --accept-esm-license \
  --out-dir protcross-results

Explicit release assets are verified against the selected bundle's SHA256. Custom experimental files use --trust-unverified-assets; output provenance stores their real hashes and verification status.

protcross predict input.pdb \
  --checkpoint /trusted/custom/model.ckpt \
  --esm-weights /trusted/custom/esmc.pth \
  --pca /trusted/custom/reducer.pkl \
  --trust-unverified-assets \
  --accept-esm-license

Checkpoint, PCA, and PyTorch weight files can contain executable serialized objects. Load them from controlled storage. ESM-C weights are distributed through the EvolutionaryScale model repository.¹

CLI and Desktop assets use separate storage roots. Desktop records its selected assets in the operating-system application-data directory.

Desktop application

ProtCross Desktop combines a Tauri 2 shell, React interface, Mol* viewer, and local Python sidecar. The sidecar binds to 127.0.0.1 on a dynamic port and uses a per-session token for local API requests.

Install

Download the matching release artifact and SHA256SUMS.txt from the v0.2.3 release:

ProtCross_Desktop_0.2.3_x64-setup.exe
ProtCross_Desktop_0.2.3_macos-aarch64.dmg

The guided first-launch workflow installs a CPU runtime, records ESM-C term acceptance, downloads or imports model assets, and validates readiness. Advanced runtime options provide NVIDIA CUDA on Windows, Apple MPS on macOS, custom Conda environments, and proxy configuration. Reserve approximately 5 GiB for the runtime and ESM-C asset.

Run a first Desktop prediction in five steps:

1. Open Setup, install a backend, and validate it.
2. Review the ESM-C terms, then download or import the ESM-C weights.
3. Open Predict, select a local PDB/mmCIF file, and inspect it.
4. Select the chain scope and output directory; expand prediction settings when needed.
5. Open Results to inspect the 0–1 score color scale, residue clusters, and output package.

The interface follows the system appearance by default and also provides light and dark modes. Keyboard focus indicators, reduced-motion handling, high-contrast support, resizable layouts, semantic status messages, and compact-window reflow are built into the frontend design system.

Local architecture

Tauri 2 shell
  -> React + Mol* frontend
  -> token-authenticated localhost API
  -> protcross_desktop Python sidecar
  -> ProtCross predictor and batch scheduler

Desktop batch jobs reuse one predictor and the input inspection reports. Each staged file has its own chain selector, including all scorable chains and blank chain IDs. The monitor exposes per-item status and full errors, supports cancellation between microbatches, and can start a new queue containing only failed or interrupted items.

For a batch run, open Batch, add the structures, review the deduplicated list, choose one output root, and start the queue. Progress and per-item status remain visible while the queue runs. Each input receives a unique subdirectory with the four-file result package. Select any completed row to inspect it in Results. Recent batch history is stored in the Desktop application-data directory and restored on the next launch. Work that was active during a restart appears as interrupted and can be retried; completed items and their files remain available.

When the output field is empty, Desktop writes single predictions under its application-data outputs/<structure>/ directory and batch predictions under outputs/batch/<job-id>/. The active platform path is displayed below the output field.

The Results workspace colors scored residues by model score and gives unscored residues a neutral gray color, including residues outside a selected chain or a truncated sequence context. Adjust the displayed score cutoff and Cα clustering distance to regroup the complete residue table immediately; this updates the viewer and cluster inspector without running the model or changing output files. Reopen a previous package by selecting its *.protcross.summary.json file. Use Diagnostics to test the runtime, review asset health, and export a sanitized support ZIP with bounded log excerpts.

Model and inference pipeline

flowchart LR
    accTitle: ProtCross inference pipeline
    accDescr: Coordinate files are parsed into per-chain sequences and a shared C-alpha graph, embedded with ESM-C and PCA, scored by PointNet++, and serialized as annotated coordinates, scores TSV, pockets JSON, and summary JSON.

    coordinates["PDB or mmCIF"] --> parser["Structure parser"]
    parser --> sequence["Per-chain sequence"]
    parser --> geometry["Centered Cα graph"]
    sequence --> esmc["ESM-C 600M"]
    esmc --> pca["PCA 128"]
    pca --> pointnet["PointNet++"]
    geometry --> pointnet
    pointnet --> scores["Residue scores"]
    scores --> clusters["Threshold and cluster"]
    clusters --> outputs["Four-file result package"]

Components

Component	Configuration
ESM-C	600M, hidden size 1,152, 36 layers, 18 attention heads
PCA	Paired reducer, 128 output dimensions
Set abstraction 1	Sampling ratio 0.5, radius 10 Å, 64 neighbors
Set abstraction 2	Sampling ratio 0.25, radius 20 Å, 64 neighbors
Set abstraction 3	Sampling ratio 0.1, radius 40 Å, 64 neighbors
Feature propagation	Three k=3 interpolation stages
Segmentation head	128 -> 64 -> 32 -> 2, dropout 0.5

The inference parser creates centered Cα geometry and per-chain sequence chunks. ESM-C embeddings are reduced with the PCA asset paired to the selected checkpoint. PointNet++ processes every input structure as an independent graph and returns one two-class logit vector per residue.

The geometry backend uses pure-PyTorch farthest-point sampling, radius search, and stable KNN interpolation. Radius neighborhoods retain the first 64 source neighbors in canonical input order. Inference runs in FP32 and records the execution mode in summary.json. Canonical ordering and neighbor selection are deterministic; floating-point reductions remain device- and kernel-dependent.

Training architecture

ProtCross uses a source-domain residue segmentation objective and adversarial domain adaptation between PDB and matched AF2 structures. The target-domain adversarial term supports pLDDT weighting. The maintained model configuration uses feature_dim=128, use_esm=true, use_da=true, and da_weight=0.2.

Training labels are generated from standard-residue Cα atoms within 6 Å of eligible hetero-residue atoms. The parser applies a versioned residue-name filter for waters, common crystallization additives, salts, ions, and terminal caps.

Training and development

Repository layout

src/protcross/
├── cli/             unified command-line entry points
├── data/            parsing, ESM, PCA, preprocessing, datasets
├── models/          PointNet++, segmentation, domain adaptation
├── inference/       prediction, batching, clustering, serialization
├── training/        Hydra and Lightning orchestration
├── evaluation/      metrics and adaptive evaluation
└── experiments/     benchmark and strategy-search workflows
configs/             Hydra training configuration
desktop/             Tauri, React, Python sidecar, packaging
tests/               core, inference, data, CLI, Desktop
reproduction/        archived paper-era workflows
examples/            example coordinate files

Development environment

conda env create -f environment.yml
conda activate protcross
python -m pip install -e ".[dev,esm]"

Maintained training workflow

Place source coordinate files in data/raw_pdb. protcross download-af2 resolves PDB-to-UniProt accessions, downloads matching AF2 structures, and writes artifacts/pdb_uniprot_mapping.json for the later mapping stage.

protcross download-af2 \
  --raw-pdb-dir data/raw_pdb \
  --output-dir data/raw_af2 \
  --mapping-file artifacts/pdb_uniprot_mapping.json

protcross preprocess \
  --data-dir data/raw_pdb \
  --output-dir data/processed_pdb \
  --fit-pca \
  --esm-weights ~/.cache/protcross/assets/v0.1.2/esmc_600m_2024_12_v0.pth \
  --pca artifacts/protcross-pca-128.pkl \
  --pca-dim 128 \
  --accept-esm-license

protcross preprocess \
  --data-dir data/raw_af2 \
  --output-dir data/processed_af2 \
  --esm-weights ~/.cache/protcross/assets/v0.1.2/esmc_600m_2024_12_v0.pth \
  --pca artifacts/protcross-pca-128.pkl \
  --is-af2 \
  --accept-esm-license

protcross map-labels \
  --processed-pdb-dir data/processed_pdb \
  --processed-af2-dir data/processed_af2 \
  --raw-pdb-dir data/raw_pdb \
  --raw-af2-dir data/raw_af2 \
  --mapping-file artifacts/pdb_uniprot_mapping.json

protcross train

Preprocessing writes one .pt tensor package per structure and an atomic protcross-preprocess-manifest.json. The manifest records completion state, input hashes, generated outputs, failures, and skipped files. PCA fitting uses the configured preprocessing seed. Training cache freshness uses .pt file names, sizes, and modification times, so dataset startup does not reread every tensor package solely to hash it.

Hydra configuration entry points:

File	Scope
configs/train.yaml	experiment seed and composed defaults
configs/data/protein_seg.yaml	source/target datasets and loaders
configs/model/da_module.yaml	segmentation and domain-adaptation model
configs/trainer/default.yaml	epochs, precision, devices, logging

Example overrides:

protcross train model.use_da=False
protcross train model.use_esm=False trainer.max_epochs=5
protcross train \
  data.data_dir_pdb=/abs/path/to/processed_pdb \
  data.data_dir_af2=/abs/path/to/processed_af2

Paper workflow

protcross setup-assets \
  --asset-version 0.1.1-paper \
  --accept-esm-license

python reproduction/legacy/run_Predict_ProtCross.py \
  --pdb_file examples/6fhu.pdb \
  --asset-version 0.1.1-paper \
  --accept-esm-license

The reproduction/legacy/ directory contains the archived PDBbind v2020 workflow and paper-era entry points.

Tests

python -m pytest -q
ruff check src tests desktop/backend desktop/installer
(cd desktop/frontend && npm ci && npm run build)
python desktop/installer/validate_version_consistency.py
python -m build --wheel --outdir dist

Desktop backend tests run from the repository root:

python -m pytest -q tests/desktop

Desktop development requires the Tauri 2 platform prerequisites.²

python -m pip install -e ".[predict]"
python -m pip install -e desktop/backend
cd desktop/frontend
npm ci
export PROTCROSS_DESKTOP_BACKEND_PATH="../backend"
export PROTCROSS_DESKTOP_PYTHON="python"
npm run tauri:dev

Troubleshooting

Symptom	Resolution
Unsupported Python version	Create a Python 3.10 environment and reinstall
ESM-C acceptance prompt	Run protcross setup-assets --accept-esm-license
Interrupted asset transfer	Repeat setup; the downloader resumes retained .part data
Asset verification failure	Run setup with --refresh-assets
Existing output path	Select another --out-dir or pass --overwrite
CUDA or MPS unavailable	Select --device cpu and verify with protcross predict --help
Structure parse failure	Run protcross inspect input.pdb --json and review the report
Chain exceeds 1,022 residues	Select a chain or pass --allow-truncation
Batch memory pressure	Reduce batch_size, residue budget, or padded-token budget
Desktop backend failure	Open Diagnostics, run the environment test, and reinstall the selected backend
Desktop asset failure	Re-run asset verification or import a local asset file from Setup
Desktop issue report	Export the diagnostic package from Diagnostics

Report reproducible issues through the issue tracker. Include the exact command, summary.json, platform, Python/PyTorch versions, and sanitized diagnostics.

Version history

0.2.3

• Reused verified asset manifests and metadata-based dataset signatures to cut repeated hashing and startup work; accelerated AF2 indexing, preprocessing, strategy search, and long-log assembly.
• Removed redundant prediction, CLI, data-loading, and output-rollback layers; retained bounded scheduling, input contracts, and atomic per-file outputs.
• Improved machine-readable inspection errors, chain guidance, Desktop error display and diagnostic exports, per-file batch chain selection, failed-item retry, restart-safe batch history, interactive result regrouping, and neutral rendering for unscored residues.

0.2.2

• Added bounded ESM-C and PointNet++ microbatching with graph, residue, quadratic-cost, and padded-token budgets; duplicate-sequence reuse; validated feature caching; recursive accelerator-OOM splitting; and ordered per-item error isolation.
• Accelerated deterministic geometry and inference parsing; corrected small-set split leakage, label-alignment statistics, AF2 mapping, and mmCIF residue identity; added finite-value gates, isolated feature-cache namespaces, and transactional dataset publication and atomic per-file result writes.
• Rebuilt ProtCross Desktop around responsive task workspaces, semantic OKLCH themes, accessible interaction states, score-aware Mol* rendering, structured diagnostics, persistent batch feedback, and result-package reopening.

0.2.1

• Added model-free structure inspection and Desktop input checks.
• Added resumable, locked, SHA256-verified asset setup.
• Added deterministic pure-PyTorch geometry and canonical residue ordering.
• Added v2 output schemas and record-preserving structure annotation.

0.2.0

• Added the Tauri/React Desktop application and Mol* visualization.
• Added local backend setup, batch queue foundations, and diagnostics.

0.1.3

• Added the four-file result package and unified protcross CLI.
• Archived paper-era wrappers under reproduction/legacy/.

0.1.2

• Added the Binding MOAD checkpoint and paired PCA asset bundle.
• Added the maintained structure preprocessing rules.

0.1.1 and 0.1.0

• Added the paper checkpoint/PCA, preprocessing pipeline, domain adaptation, evaluation components, and initial command-line workflow.

Citation

If ProtCross contributes to a publication, cite:

@article{zhong2026protcross,
  title = {{ProtCross}: Bridging the {PDB}-{AlphaFold} Gap for Binding Site Prediction with Protein Point Clouds},
  author = {Zhong, Shuyu and Jiang, Yuying},
  journal = {Journal of Chemical Information and Modeling},
  year = {2026},
  volume = {66},
  number = {7},
  pages = {3688--3701},
  doi = {10.1021/acs.jcim.5c03224}
}

License

ProtCross source code is distributed under the MIT License. ESM-C weights use EvolutionaryScale's model terms.³ ProtCross checkpoint and PCA bundles are distributed separately from ESM-C weights.

1. EvolutionaryScale. ESM-C 600M 2024-12 model repository. ↩

2. Tauri. Prerequisites. ↩

3. EvolutionaryScale. Cambrian Non-Commercial License Agreement. ↩