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| DSSP | |
|---|---|
| Name | DSSP |
| Developer | Kabsch and Sander |
| Released | 1983 |
| Programming language | C, Fortran, Python (tools) |
| Operating system | Unix-like, Windows, macOS |
| Genre | Bioinformatics, Structural Bioinformatics |
| License | Various (public domain origins, permissive) |
DSSP
DSSP is a standardized algorithm and file format for assigning secondary structure to proteins from three-dimensional atomic coordinates. It provides per-residue annotations that bridge experimental structures from Protein Data Bank entries to analyses used by researchers at European Molecular Biology Laboratory, Max Planck Society, and groups at institutions such as University of Cambridge, Massachusetts Institute of Technology, and Stanford University. The output is widely used in comparative studies that reference resources like UniProt, SCOP, and CATH.
DSSP assigns secondary-structure labels (helix, sheet, turn, bend) to residues in protein structures determined by experimental methods such as X-ray crystallography, Nuclear magnetic resonance spectroscopy, and Cryo-electron microscopy. It is frequently used alongside databases and projects including Protein Data Bank, UniProtKB, Pfam, InterPro, and RCSB PDB for annotation, benchmarking, and training in machine learning efforts by teams at Google DeepMind, OpenAI, and university groups. The output informs structural comparisons in resources like SCOPe, CATH-Gene3D, and analyses cited in journals such as Nature, Science, and Proceedings of the National Academy of Sciences.
The algorithm evaluates hydrogen-bonding patterns and backbone geometry to designate secondary-structure elements recognized in classical works by Linus Pauling and Robert Corey. Using geometric criteria and electrostatic approximations, it computes hydrogen-bond energies to identify patterns corresponding to alpha helix and beta sheet motifs analogous to annotations used in studies by Anfinsen and methods adopted in protocols at European Bioinformatics Institute. DSSP defines turns and bends with residue-specific measures that have informed machine-learning predictors developed at University of California, Berkeley and University of Oxford.
Input is typically an atomic coordinate file from Protein Data Bank or locally generated models from packages such as Rosetta (software), Phenix, and Modeller. Outputs include per-residue secondary-structure codes, solvent accessibility values, and hydrogen-bond lists formatted for parsers used in projects like BioPython, MDAnalysis, and UCSF ChimeraX. Generated files are consumed by pipelines at facilities including Argonne National Laboratory and Lawrence Berkeley National Laboratory for high-throughput structural annotation.
DSSP annotations support comparative modeling studies in homology modeling workflows used at RosettaCommons and validation metrics in structure-determination consortia like Structural Genomics Consortium. They form features in secondary-structure prediction benchmarks compared against methods from PSIPRED, JPred, and deep-learning systems such as AlphaFold and RoseTTAFold. DSSP-based solvent accessibility measures feed into analyses of protein–ligand interfaces in studies performed at Pfizer, Novartis, and academic labs at Harvard University and Yale University.
Reference implementations exist in C and Fortran, with bindings and reimplementations in Python (programming language), Java (programming language), and Perl. Popular toolkits that include DSSP functionality are BioPython, MDTraj, MDAnalysis, ProDy, and visualization programs such as PyMOL, UCSF Chimera, and VMD. Community-maintained wrappers integrate DSSP computation into workflows on platforms like Galaxy (platform), Nextflow, and Snakemake.
Validation against curated datasets from Protein Data Bank and manually reviewed examples shows robustness for canonical helices and sheets but sensitivity to coordinate resolution and model errors common in low-resolution Cryo-electron microscopy reconstructions. Limitations include ambiguity at loop residues, dependence on hydrogen-bond criteria that differ from alternative schemes used in STRIDE, and sensitivity to missing atoms or alternative conformations present in entries from RCSB PDB. Users often cross-validate with complementary methods from DSSP-related tools and consensus annotations in CATH or SCOP.
DSSP originated in the early 1980s from work by Wolfgang Kabsch and Christof Sander to provide a reproducible rule-based assignment for secondary structure, motivated by growing archives at Protein Data Bank and the need for standardized comparisons in studies published in venues like Journal of Molecular Biology. Over decades it has been updated and reimplemented by contributors across institutions including European Bioinformatics Institute and research groups at University of Zurich and Max Planck Institute for Biophysical Chemistry, and it remains integrated into modern structural biology toolchains and databases maintained by organizations such as RCSB PDB and PDBe.