# PDBRust Roadmap
Future development ideas for PDBRust. Priority will be determined based on user feedback.
## Completed
### Python Bindings (PyO3) ✅
- Created `pdbrust-python` package, published to PyPI (`pip install pdbrust`)
- Full API: parsing, filtering, descriptors, quality, RCSB search/download
- Numpy integration: `get_coords_array()`, `get_ca_coords_array()`
- Multi-platform wheels: Linux, macOS, Windows (Python 3.9-3.12)
- GitHub Actions CI/CD for automated releases
### Contact Maps / Distance Matrices ✅
- `structure.distance_matrix_ca()` → 2D f64 matrix (CA atoms)
- `structure.contact_map_ca(threshold)` → 2D boolean matrix (default: 8.0 Å)
- `structure.distance_matrix()` → 2D f64 matrix (all atoms)
- `structure.contact_map(threshold)` → 2D boolean matrix (default: 4.5 Å)
- Python bindings return numpy arrays (N×N)
- Essential for ML applications (GNNs, protein transformers)
### RMSD / Structure Superposition ✅
- Kabsch algorithm for optimal alignment using nalgebra SVD
- `structure.rmsd_to(other)` → f64 (CA atoms)
- `structure.align_to(target)` → (aligned_structure, AlignmentResult)
- `structure.per_residue_rmsd_to(target)` → Vec<PerResidueRmsd>
- Flexible atom selection: CA only (default), backbone, all atoms, custom
- Python bindings with AtomSelection, AlignmentResult, PerResidueRmsd types
- Under `geometry` feature flag (requires nalgebra)
### mmCIF Writing ✅
- `write_mmcif_file()` function for file output
- `write_mmcif_string()` for string output
- `write_mmcif()` for generic writer output
- `write_gzip_mmcif_file()` for compressed output (gzip feature)
- Full Python bindings included
- Supports ATOM/HETATM, SEQRES, SSBOND data
### Selection Language (Query DSL) ✅
- PyMOL/VMD-style selection syntax: `structure.select("chain A and name CA")`
- Basic selectors: `chain`, `name`, `resname`, `resid`, `element`
- Range selections: `resid 1:100`
- Keywords: `backbone`, `protein`, `nucleic`, `water`, `hetero`, `hydrogen`
- Boolean operators: `and`, `or`, `not`, parentheses for grouping
- Numeric comparisons: `bfactor < 30.0`, `occupancy >= 0.5`
- Zero external dependencies (hand-written recursive descent parser)
- Full Python bindings included
### DSSP 4-like Secondary Structure Assignment ✅
- Implements Kabsch & Sander algorithm with DSSP 4 updates (Hekkelman et al., 2025)
- H-bond detection using electrostatic energy model (E < -0.5 kcal/mol threshold)
- 9-state classification: H (α-helix), G (3₁₀-helix), I (π-helix), P (κ-helix/PPII), E (extended), B (β-bridge), T (turn), S (bend), C (coil)
- PPII/κ-helix detection using backbone dihedral angles (φ = -75° ± 29°, ψ = +145° ± 29°)
- `structure.assign_secondary_structure()` → `SecondaryStructureAssignment`
- `structure.secondary_structure_string()` → String (e.g., "HHHHEEEECCCC")
- `structure.secondary_structure_composition()` → (helix_fraction, sheet_fraction, coil_fraction)
- Full Python bindings with iterator and indexing support
- Under `dssp` feature flag (included in `analysis`)
### Async RCSB Downloads ✅
- Async variants of download functions for efficient bulk downloading
- `download_multiple_async()` with concurrency control via `AsyncDownloadOptions`
- Configurable: max_concurrent (default: 5), rate_limit_ms (default: 100ms), timeout, retries
- Preset options: `conservative()` for rate-limited scenarios, `fast()` for high-throughput
- Automatic retry with exponential backoff on transient failures
- Python bindings: `download_multiple()` with `AsyncDownloadOptions` and `DownloadResult`
- Under `rcsb-async` feature flag (included in `full`)
### B-factor Analysis ✅
- `structure.b_factor_mean()` → mean B-factor across all atoms
- `structure.b_factor_mean_ca()` → mean B-factor for CA atoms only
- `structure.b_factor_min()`, `b_factor_max()`, `b_factor_std()` → basic statistics
- `structure.b_factor_profile()` → per-residue B-factor statistics (mean, min, max)
- `structure.flexible_residues(threshold)` → identify mobile/disordered regions
- `structure.rigid_residues(threshold)` → identify well-ordered regions
- `structure.normalize_b_factors()` → Z-score normalization for cross-structure comparison
- `structure.b_factor_percentile(atom_serial)` → get percentile rank of atom's B-factor
- `ResidueBFactor` struct with chain_id, residue_seq, residue_name, and B-factor stats
- Full Python bindings with `ResidueBFactor` class
- B-factor fields added to `StructureDescriptors`
- Under existing `descriptors` feature flag
## Medium Impact
## Lower Priority
### Binding Site Detection
- Identify residues near ligands/HETATM
- `structure.binding_site(ligand_name, distance_cutoff)`
- Useful for drug discovery applications
### Surface Area Calculation
- Solvent accessible surface area (SASA)
- Per-residue and per-atom breakdown
- Requires rolling ball algorithm
### Symmetry Operations
- Parse and apply BIOMT records
- Generate biological assemblies
- `structure.biological_assembly()`
### Trajectory Support
- Parse multi-frame trajectories (e.g., from MD simulations)
- Memory-efficient streaming for large trajectories
- Basic trajectory analysis (RMSD over time, etc.)
## Community Requested
*This section will be populated based on GitHub issues and user feedback.*
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## Contributing
If you'd like to work on any of these features, please:
1. Open an issue to discuss the approach
2. Reference this roadmap in your PR
Feedback and feature requests welcome at: https://github.com/HFooladi/pdbrust/issues