{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"PyDSSP assigns protein secondary structure (alpha-helix, beta-strand, or loop) from backbone coordinates using a simplified DSSP algorithm, with support for both NumPy and differentiable PyTorch tensors.","skillfed_tags":["protein-structure","bioinformatics","differentiable-computing"],"use_cases":["Assign secondary structure to protein backbones in molecular dynamics simulations or structure prediction pipelines.","Generate differentiable hydrogen-bond matrices for neural network training on protein structure tasks.","Batch-process multiple PDB files to extract C3 secondary structure annotations via the command-line tool.","Compute continuous hydrogen-bond scores for structure validation or quality assessment workflows.","Integrate structure assignment into deep learning models where gradients must flow through secondary structure computation."],"what_it_does":"PyDSSP is a Python implementation of the DSSP (Dictionary of Secondary Structure of Protein) algorithm that classifies each residue in a protein backbone as alpha-helix (H), beta-strand (E), or loop (-). It takes 3D atomic coordinates as input and outputs secondary structure assignments. The package is not a complete reimplementation of the original 1983 DSSP algorithm\u2014it simplifies some parts, such as omitting \u03b2-bulge annotation and using slightly different hydrogen-placement parameters\u2014but achieves over 97% agreement with the original on average.\n\nThe main innovation is support for both NumPy arrays and PyTorch tensors, with the latter enabling differentiable computation. PyDSSP can output hydrogen-bond matrices as continuous values in [0, 1] rather than binary assignments, making it suitable for machine learning pipelines where gradients flow through structure prediction. It includes a command-line tool for batch processing PDB files and a Python API for programmatic use.","worth_installing":"Yes, if you need secondary structure assignment for protein backbones and can work with a simplified DSSP that omits \u03b2-bulge annotation. The 97% agreement with original DSSP is suitable for most applications. Install friction is low and the package is actively maintained. No security vulnerabilities are known. Choose this if you need PyTorch differentiability or batch processing; otherwise, consider the original DSSP or STRIDE for full feature parity."},"id":"pydssp","links":{"html":"https://skillfed.io/packages/pydssp","md":"https://skillfed.io/packages/pydssp.md","pypi":"https://pypi.org/project/pydssp/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-02-24","license_spdx":null,"license_treatment":"permissive","name":"pydssp","python_support":"unspecified","summary":"A simplified implementation of DSSP algorithm for PyTorch and NumPy"},"popularity":{"monthly_downloads":99424,"position":13023,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.1"}