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Swiss-model
Swiss-model
Swiss-model
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Swiss-model
TypeStructural bioinformatics tool
Licensefreeware, source code unavailable
Websiteswissmodel.expasy.org

Swiss-model (stylized as SWISS-MODEL) is a structural bioinformatics web-server dedicated to homology modeling of 3D protein structures.[1][2] As of 2024, homology modeling is the most accurate method to generate reliable three-dimensional protein structure models and is routinely used in many practical applications. Homology (or comparative) modelling methods make use of experimental protein structures (templates) to build models for evolutionary related proteins (targets).

Today, Swiss-model consists of three tightly integrated components: (1) The Swiss-model pipeline – a suite of software tools and databases for automated protein structure modelling,[1] (2) The Swiss-model Workspace – a web-based graphical user interface workbench,[2] (3) The Swiss-model Repository – a continuously updated database of homology models for a set of model organism proteomes of high biomedical interest.[3]

Pipeline

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Swiss-model pipeline comprises the four main steps that are involved in building a homology model of a given protein structure:

  1. Identify structural template(s). BLAST and HHblits are used to identify templates. Those are stored in the Swiss-model Template Library (SMTL), which is derived from Protein Data Bank (PDB).
  2. Align target sequence and template structure(s).
  3. Build model and minimize energy. Swiss-model implements a rigid fragment assembly approach in modelling.
  4. Assess model quality using QMEAN, a statistical potential of mean force.

Workspace

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The Swiss-model Workspace integrates programs and databases required for protein structure prediction and modelling in a web-based workspace. Depending on the complexity of the modelling task, different modes of use can be applied, in which the user has different levels of control over individual modelling steps: automated mode, alignment mode, and project mode. A fully automated mode is used when a sufficiently high sequence identity between target and template (>50%) allows for no human intervention at all. In this case only the sequence or UniProt accession code of the protein is required as input. The alignment mode enables the user to input their own target-template alignments from which the modelling procedure starts (i.e. search for templates step is skipped and rarely only minor changes in the provided alignment are made). The project mode is used in more difficult cases, when manual corrections of target-template alignments are needed to improve the quality of the resulting model. In this mode the input is a project file that can be generated by the DeepView (Swiss Pdb Viewer) visualization and structural analysis tool,[4] to allow the user to examine and manipulate the target-template alignment in its structural context. In all three cases the output is a pdb file with atom coordinates of the model or a DeepView project file. The four main steps of homology modelling may be repeated iteratively until a satisfactory model is achieved.

The Swiss-model Workspace is accessible via the ExPASy web server, or it can be used as part of the program DeepView (Swiss Pdb-Viewer). As of September 2015 it has been cited 20000 times in scientific literature,[5] making it one of the most widely used tools for protein structure modelling. The tool is free for academic use.

Repository

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The Swiss-model Repository provides access to an up-to-date collection of annotated three-dimensional protein models for a set of model organisms of high general interest. Model organisms include human,[6] mouse,[7] C.elegans,[8] E.coli,[9] and various pathogens including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).[10] Swiss-model Repository is integrated with several external resources, such as UniProt,[11] InterPro,[12] STRING,[13] and Nature Protein Structure Initiative (PSI) SBKB.[14]

New developments of the Swiss-model expert system feature (1) automated modelling of homo-oligomeric assemblies; (2) modelling of essential metal ions and biologically relevant ligands in protein structures; (3) local (per-residue) model reliability estimates based on the QMEAN local score function;[15] (4) mapping of UniProt features to models. (1) and (2) are available when using the automated mode of the Swiss-model Workspace; (3) is always provided when calculating an homology model using the Swiss-model Workspace, and (4) is available in the Swiss-model Repository.

Accuracy and reliability of the method

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In the past, the accuracy, stability and reliability of the Swiss-model server pipeline was validated by the EVA-CM benchmark project. As of 2024, the Swiss-model server pipeline is participating in the Continuous Automated Model EvaluatiOn (CAMEO3D) project, which continuously evaluates the accuracy and reliability of protein structure prediction services via fully automated means.[16]

References

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See also

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Swiss-model

View on Grokipedia
from Grokipedia
Swiss-model (stylized as SWISS-MODEL) is a structural bioinformatics web-server dedicated to of 3D protein structures. It provides a fully automated platform for generating reliable three-dimensional models of proteins and protein complexes using comparative modeling methods, making it accessible to life science researchers worldwide. As of 2024, remains one of the most accurate approaches for when experimental data is unavailable, routinely applied in , , and functional annotation. Developed by the Computational Structural Biology Group at the SIB Swiss Institute of Bioinformatics and the Biozentrum, , SWISS-MODEL originated in 2003 as an automated comparative modeling server. It has since evolved to include a web-based workspace for user-guided modeling, integration with tools like DeepView for visualization, and a repository of pre-computed models for key species such as humans and mice, updated weekly. The modeling identifies suitable templates from databases like the and AlphaFoldDB using sequence similarity searches (e.g., BLAST, HHblits), performs alignments, builds models, and assesses quality with metrics like QMEAN. Recent enhancements include support for oligomeric assemblies, modeling, and participation in blind experiments like CAMEO3D for ongoing accuracy evaluation.

Overview

History and Development

The origins of SWISS-MODEL trace back to , when it was initiated by Manuel C. Peitsch at the Biozentrum of the as the first fully automated server for homology modeling, making comparative modeling accessible via the early . This pioneering effort addressed the growing need for structural predictions in the post-genomic era, where experimental structures were limited, and built upon precursor tools like ProMod, Peitsch's automated modeling engine developed in the early 1990s. Initially hosted at the Basel institution, the server quickly gained adoption for its user-friendly interface and reliability, marking a shift from manual to automated workflows in structural bioinformatics. Key milestones in the 2000s enhanced interactivity and quality assessment. In 2006, the SWISS-MODEL Workspace was introduced, providing a web-based environment for iterative model building, template exploration, and refinement, which empowered users beyond fully automated submissions. That same year, foundational work on scoring functions laid the groundwork for improved reliability, with QMEAN—a composite scoring function evaluating local geometry, all-atom interactions, and solvation—integrated shortly thereafter to estimate model quality. By the , expansions included support for oligomeric () structures and modeling, enabling more biologically relevant predictions by incorporating evolutionary information and annotations from the . The server's 25th anniversary in 2018 underscored its enduring impact, having democratized protein modeling for researchers worldwide and generated millions of structures. Ongoing maintenance is led by the Computational Structural Biology Group at the Swiss Institute of Bioinformatics (SIB), under the oversight of Torsten Schwede, who succeeded Peitsch as the primary developer. In the post- era, an update in April 2023 incorporated structures from the Database as templates in the pipeline. In June 2025, the OpenStructure Actions was introduced for benchmarking and comparing molecular models.

Purpose and Applications

SWISS-MODEL serves as a fully automated web-based server for homology modeling, enabling the prediction of three-dimensional (3D) protein structures from amino acid sequences by leveraging experimentally determined templates from the (PDB). The core purpose is to democratize access to tools, allowing life scientists without specialized computational expertise to generate reliable models quickly and efficiently. This is achieved through comparative modeling, a method that exploits the evolutionary conservation of among homologs, assuming that proteins sharing sequence similarity adopt similar folds. The tool's primary applications span multiple domains of biological research, including where homology models facilitate target validation and structure-based ligand design by providing atomic-level insights into protein active sites. In functional annotation efforts within , SWISS-MODEL aids in inferring protein roles by mapping structural features to known functions, enhancing the interpretation of newly sequenced genes. It also supports evolutionary studies by enabling comparative analyses of structural conservation across species, revealing insights into protein family divergence and adaptation. Additionally, models generated by SWISS-MODEL can integrate with experimental techniques such as cryo-electron microscopy (cryo-EM), serving as initial frameworks to refine low-resolution density maps or interpret hybrid structures. Targeted at researchers in bioinformatics, structural biology, and related fields, SWISS-MODEL has become a cornerstone resource, with its associated publications garnering thousands of citations and the server producing approximately 3,000 models daily as of 2018—equating to over one million annually. Its scope is optimized for proteins with detectable homologs exhibiting at least 30% sequence identity to templates, ensuring high accuracy for well-conserved targets, but it is not designed for de novo folding of novel protein architectures lacking close relatives.

Modeling Workflow

Automated Pipeline

The automated pipeline in SWISS-MODEL enables users to generate high-quality protein homology models without manual intervention, processing submissions through a streamlined, end-to-end . Users initiate the process by providing a target protein sequence in or a identifier, which the server uses to query relevant databases for modeling. This fully automated mode, designed for accessibility, handles the entire procedure from input to output, typically completing in minutes to an hour depending on complexity. Template identification forms the first step, where the target sequence is searched against the SWISS-MODEL Template Library (SMTL), a curated collection of experimentally determined structures from the (PDB) and predicted structures from the Database. The search employs BLAST for rapid detection of close homologs and HHblits for more sensitive identification of distant evolutionary relationships, ranking potential templates by sequence identity, coverage, and structural quality metrics such as resolution and ligand presence. Up to 20 top templates are selected automatically to support single- or multi-template modeling. In the alignment step, the target is aligned to the selected template structures using PROMALS3D for progressive multiple sequence-structure alignment or HHalign for hidden Markov model-based pairwise alignments, ensuring optimal superposition of conserved regions while accommodating insertions and deletions. These tools generate a refined alignment file that guides subsequent model , prioritizing accuracy in core secondary structure elements. Model building proceeds with ProMod3, the core engine of the pipeline, which constructs the three-dimensional coordinates by copying backbone atoms from the template(s) and modeling variable regions. Side chains are placed using rotamer libraries, such as the Dunbrack library, selected via energy-based optimization to minimize steric clashes. Loops and gaps are modeled by sampling fragments from the SMTL or a dedicated loop database, refined through simulations to achieve low-energy conformations. For multi-template scenarios, ProMod3 integrates segments from multiple sources to cover the target sequence comprehensively. The final refinement step involves energy minimization using OpenMM, an open-source molecular dynamics toolkit, to relax the model and resolve stereochemical violations, such as bond lengths and angles, while preserving overall fold integrity. The output consists of one or more PDB-formatted model files, accompanied by a basic quality report summarizing template details, alignment coverage, and preliminary scores like QMEAN for local and global reliability. This automated approach offers significant advantages, including rapid processing suitable for batch submissions of thousands of sequences, and has been a of SWISS-MODEL since its in as one of the first web-based tools for . It supports high-throughput applications in , generating approximately 2,000–3,000 models daily with consistent accuracy.

Workspace Interface

The SWISS-MODEL Workspace is a web-based graphical interface designed for interactive homology modeling, providing users with a personal environment to manage multiple projects in parallel without requiring local software installation. Introduced in 2006, it integrates seamlessly with DeepView (Swiss-PdbViewer), a downloadable visualization tool that enables detailed 3D structure manipulation and alignment refinement. This integration allows iterative model improvements by exporting project files for local editing and re-importing them into the workspace. The interface operates in three primary modes to accommodate varying levels of user control: an automated mode as the default for straightforward cases with high sequence identity (>50%), an alignment mode where users can manually edit target-template alignments, and a project mode for handling complex workflows involving multiple templates or domains. In project mode, users can build and compare models step-by-step, starting from the automated pipeline's initial output for further customization. Key features include sequence input via direct entry, file upload, or accession; interactive template selection with visualization of structural alignments and coverage; and that supports both monomeric and oligomeric structures by leveraging quaternary annotations from the template library. Additional capabilities encompass alignment editing to adjust insertions, deletions, and gaps; mutation analysis through structural visualization and ; and export options for models in PDB format or full project files compatible with DeepView. The workspace links to broader ecosystem tools, such as for sequence retrieval and QMEAN for quality evaluation, enhancing workflow efficiency. Tutorials and step-by-step guides, including video demonstrations, assist users—particularly non-experts—in navigating the interface and refining models for applications like or functional annotation. Accessible for free at swissmodel.expasy.org, it democratizes advanced modeling by requiring only a .

Model Resources

Template Library

The SWISS-MODEL Template Library (SMTL) is a curated collection of protein structures that forms the foundational resource for in the SWISS-MODEL pipeline. Derived exclusively from the (PDB), the SMTL contains 1,138,856 chains, 415,105 biounits, and 168,103 unique SEQRES sequences as of November 2025, encompassing individual chains, multi-chain assemblies, and ligand-bound complexes to reflect diverse biological contexts. This library is updated weekly to integrate the most recent PDB releases, ensuring access to the latest experimentally validated structures for template-based predictions. Curation of the SMTL emphasizes quality and non-redundancy to optimize modeling outcomes. Structures are filtered to include only those with high resolution (better than 3.0 ), as lower-resolution entries may introduce inaccuracies in atomic coordinates. Redundancy is systematically removed to preserve representative high-quality entries, with multimeric assemblies and structures with bound ligands or cofactors retained, as they capture interactions and functional sites critical for biologically relevant models. Template detection within the SMTL relies on a two-tiered search strategy to identify suitable scaffolds. BLAST is employed for rapid detection of templates with high sequence similarity to the target, providing straightforward alignments for close homologs. For more distant relationships, HHblits performs profile-based searches using hidden Markov models, enabling the identification of evolutionary homologs with low sequence identity but conserved folds. These methods, augmented by predictions integrated since April 2023, collectively supply evolutionarily related structural scaffolds, enabling detectable templates for over 99% of reviewed protein sequences in and supporting robust across diverse proteomes. The inclusion of structures allows hybrid modeling for regions lacking experimental data, significantly enhancing coverage and accuracy for challenging targets.

Repository Features

The SWISS-MODEL Repository, established in 2006, is a public database of annotated three-dimensional protein structure models generated through automated homology modeling, containing over 3.7 million models as of the 2025_04 release for 13 key model organisms, including human (Homo sapiens), mouse (Mus musculus), the plant Arabidopsis thaliana, and pathogens such as SARS-CoV-2. These models cover a broad range of proteins, with particular emphasis on understudied targets lacking experimental structures, enabling researchers to access reliable structural predictions without performing de novo computations. Key features of the repository include support for oligomeric assemblies, where homo- and hetero-oligomers are modeled based on template quaternary structure annotations, as well as ligand-bound models that transfer essential cofactors, metal ions, and small molecules from templates when applicable. Local QMEAN scores are computed for each model to provide residue-level reliability estimates, highlighting regions of high confidence versus potential errors. Users can browse the collection by organism-specific proteomes (e.g., 42,819 models for proteins), functional categories, or structural similarity, facilitating targeted exploration of protein families or pathways. Access to the repository is provided through a web interface supporting searches by sequence, accession number, or keywords, with results displaying model previews, quality metrics, and template details. Models are downloadable in standard PDB or mmCIF formats, including associated files for oligomers, ligands, and quality reports, while an enables programmatic retrieval for large-scale analyses. The repository undergoes monthly updates aligned with new UniProtKB releases, incorporating sequence revisions and adding models for newly annotated entries, complemented by regular rebuilds for core organism proteomes to integrate emerging templates. This automated process ensures models reflect the latest structural data, with rebuilds triggered by updates to the SWISS-MODEL Template Library. Integrations with external resources enhance model interpretability: links to provide sequence and functional annotations, offers domain architecture details, and enables visualization of protein interaction networks derived from the modeled structures.

Validation and Accuracy

Quality Assessment Methods

SWISS-MODEL employs QMEAN (Quantitative Model Energy ), a composite scoring function originally developed for evaluating models by integrating statistical knowledge-based potentials from experimentally determined structures. While the core QMEAN components assess potential, torsion angles, and all-atom interactions, current implementations incorporate it into updated scores like QMEANDisCo for enhanced accuracy. The original QMEAN formulation is: QMEAN=w1⋅Psolv+w2⋅Ptor+w3⋅Ppair\text{QMEAN} = w_1 \cdot P_{\text{solv}} + w_2 \cdot P_{\text{tor}} + w_3 \cdot P_{\text{pair}} where PsolvP_{\text{solv}}, PtorP_{\text{tor}}, and PpairP_{\text{pair}} are normalized scores for free energy, secondary structure-specific torsion angle distributions, and pairwise atomic interactions, respectively, with weights w1w_1, w2w_2, and w3w_3 empirically determined. However, the QMEAN Z-score is deprecated, and users should consult GMQE and QMEANDisCo for global quality estimates. For local quality assessment, SWISS-MODEL uses QMEANDisCo, a residue-level scoring function that extends traditional potentials with distance constraints derived from homologous structures. QMEANDisCo computes per-residue contributions, identifying unreliable regions such as misaligned loops or disordered segments with low scores (on a 0-1 scale, higher better). These local estimates are visualized in per-residue plots to guide refinement in downstream analyses. QMEANDisCo global scores (0-1) correlate well with experimental accuracy measures like lDDT. In addition to QMEANDisCo-based scores, SWISS-MODEL incorporates GMQE (Global Model Quality Estimation) as a complementary metric (0-1 scale, higher better) that combines template selection reliability, identity, and alignment coverage. For example, models from templates with >50% identity and full coverage typically yield GMQE >0.7. Model quality is contextualized by comparing to experimental structures using root-mean-square deviation (RMSD) in angstroms, with <2 Å indicating high similarity. The outputs include scores on a 0-1 scale for GMQE and QMEANDisCo, where values >0.6 generally indicate reliable models for most applications, enabling prioritization without extensive manual checks.

Reliability Evaluations

The reliability of SWISS-MODEL has been assessed through independent benchmarking initiatives, including the historical EVA-CM (Evaluation of Automated Comparative Modeling) project and the ongoing CAMEO3D (Continuous Automated Model EvaluatiOn). In EVA-CM evaluations prior to 2010, SWISS-MODEL showed strong performance for targets with >40% sequence identity to templates, achieving average global Cα RMSD of 2.0 Å against experimental structures. This highlighted its accuracy over servers like 3D-Jigsaw, based on >48,000 models from weekly PDB releases. As of 2024-2025, SWISS-MODEL participates in CAMEO3D using blind pre-release PDB , ranking highly for . In the three-month evaluation from November 2024 to February 2025, it achieved lDDT scores of 87.8 for easy , 75.2 for medium difficulty, and approximately 77 overall, outperforming several reference servers in template-based predictions. Quaternary structure QS-scores were around 80-82 for applicable easy . Empirical studies show 80-90% of SWISS-MODEL structures with >30% template sequence identity achieve backbone RMSD <3 to native folds, especially in cores. Performance drops for <20% identity due to alignment issues, often exceeding 5 RMSD. In low-homology cases, SWISS-MODEL provides reliable baselines when templates exist, complementing deep learning tools like , which excel in de novo predictions. SWISS-MODEL's automated pipeline outperforms manual methods in speed and consistency for high-identity cases. Ongoing refinements since 2022, informed by CAMEO, improve template selection and alignment for novel folds.

References

  1. https://swissmodel.expasy.org/
  2. https://www.expasy.org/resources/swiss-model
  3. https://academic.oup.com/nar/article/52/W1/W333/7665125
  4. https://pmc.ncbi.nlm.nih.gov/articles/PMC168927/
  5. https://academic.oup.com/nar/article/42/W1/W252/2435215
  6. https://swissmodel.expasy.org/help/pipeline
  7. https://pubmed.ncbi.nlm.nih.gov/12824332/
  8. https://pubmed.ncbi.nlm.nih.gov/19517507/
  9. https://www.researchgate.net/publication/26283634_Automated_comparative_protein_structure_modeling_with_SWISS-MODEL_and_Swiss-PdbViewer_A_historical_perspective
  10. https://pubmed.ncbi.nlm.nih.gov/16301204/
  11. https://pubmed.ncbi.nlm.nih.gov/17932912/
  12. https://academic.oup.com/nar/article/42/W1/W252/2435313
  13. https://academic.oup.com/nar/article/46/W1/W296/5000024
  14. https://swissmodel.expasy.org/docs/blog
  15. https://academic.oup.com/nar/article/31/13/3381/2904142
  16. https://www.sciencedirect.com/science/article/pii/S235291482200034X
  17. https://pmc.ncbi.nlm.nih.gov/articles/PMC1347419/
  18. https://pmc.ncbi.nlm.nih.gov/articles/PMC3423504/
  19. https://www.researchgate.net/publication/365191238_A_structural_biology_community_assessment_of_AlphaFold2_applications
  20. https://www.umass.edu/molvis/workshop/homolmod.htm
  21. https://swissmodel.expasy.org/docs/help
  22. https://pmc.ncbi.nlm.nih.gov/articles/PMC6030848/
  23. https://pmc.ncbi.nlm.nih.gov/articles/PMC7872268/
  24. https://academic.oup.com/nar/article/45/D1/D313/2605750
  25. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008667
  26. https://academic.oup.com/bioinformatics/article/22/2/195/423760
  27. https://www.expasy.org/resources/swiss-model-workspace
  28. https://swissmodel.expasy.org/docs/tutorial
  29. https://swissmodel.expasy.org/templates/
  30. https://pmc.ncbi.nlm.nih.gov/articles/PMC5210589/
  31. https://pubmed.ncbi.nlm.nih.gov/16381875/
  32. https://swissmodel.expasy.org/repository
  33. https://www.expasy.org/resources/swiss-model-repository
  34. https://swissmodel.expasy.org/qmean/
  35. https://swissmodel.expasy.org/repository/species/9606
  36. https://swissmodel.expasy.org/repository/api-docs
  37. https://swissmodel.expasy.org/docs/repository_help
  38. https://academic.oup.com/nar/article/37/suppl_1/D387/1008685
  39. https://swissmodel.expasy.org/repository/uniprot/O75678
  40. https://academic.oup.com/nar/article/49/D1/D605/6006194
  41. https://academic.oup.com/bioinformatics/article/36/6/1765/5614424
  42. https://www.cameo3d.org/modeling/targets/3-months/difficulty/medium/?to_date=2025-02-08
  43. https://archive.cameo3d.org/modeling/3-months/difficulty/easy/?to_date=2025-02-08
  44. https://pmc.ncbi.nlm.nih.gov/articles/PMC419651/
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