The plugin translates a specialized mathematical description of protein architecture into an interactive workflow for selecting structures, setting contact criteria and examining results. Its three tabs correspond to local circuit topology, single-file analysis and multi-file analysis. Local analysis focuses on the contact pairs surrounding a selected residue; single-file mode classifies the topology of an entire molecular object; and multi-file mode processes a folder of structures or the frames of a molecular-dynamics trajectory. PDB and mmCIF structures can be imported through the interface, while objects already loaded into PyMOL can be analyzed without exporting them first.

At the center of the system is a contact-pair map constructed from geometric proximity. By default, the software detects contacts between heavy atoms, ignores hydrogen atoms, requires at least five atom-to-atom contacts within a residue pair, and excludes residues separated by no more than three positions along the chain. The 4.5-ångström distance threshold and both filters are adjustable, making explicit how apparently objective structural relationships depend on user-selected criteria. An optional ProteinCT-identical mode counts all atoms, including hydrogens, but the developers warn that this can reduce comparability between models with different hydrogen representations.

For a single chain, the plugin classifies relations between pairs of contacts according to the order of their endpoints along the protein sequence. Nested endpoints produce Parallel or Inverse Parallel relations, separated endpoints produce Series relations, and interleaved endpoints produce Cross relations. The program also handles concerted variants of these categories. A Cross relation describes sequence ordering only: it does not by itself establish a physical crossing, threaded segment or protein knot, because circuit topology is not a geometric knot measure. The relation matrix, circuit diagram, contact-length and contact-energy filters, and the fraction of Parallel-plus-Cross relations described by ProteinCT as entanglement statistics are inherited from the established method.

Multi-chain models require an expanded vocabulary. Contact detection spans all chains in the object, including inter-chain pairs, but the basic Series, Parallel and Cross labels are assigned only when both contacts lie within the same chain. Relations involving contacts between different chains are labeled Independent, Tandem or Loop according to the multi-chain scheme adapted by ProteinCT. Despite the whole-model contact search, residue coloring and the CT Folding Score are calculated separately for each chain using intrachain contacts. The software therefore distinguishes the scope of global relation analysis from chain-specific measures while leaving the underlying contact map available for inspection and tuning.

Among the additions beyond the original toolkit is a direct projection of topological participation onto the three-dimensional structure. For each selected relation type, the plugin counts the relations involving every contact pair and adds that count to both endpoint residues. These values are not numbers of a residue’s own contacts, but sums of its participation in topological relationships. The colors are divided into five levels and scaled separately for each map, so equal colors across different relation panels do not represent equal numerical values. Coloring is applied to a copy of the molecular object, preserving the deposited B factors of the original structure, and the three-dimensional visualization currently supports the basic Series, Parallel and Cross types rather than their concerted counterparts.

The release also implements the CT Folding Score proposed by Akulov and colleagues as a quantitative measure of compaction in intrinsically disordered protein chains. In the developers’ demonstration using hen egg-white lysozyme, PDB accession 1AKI, single-file analysis generated a color-coded relation matrix, a circuit plot, three-dimensional residue maps and CSV files containing the contact-pair map and relation matrix. The reported distribution for chain A was 57.4% Series, 31.0% Parallel and 11.6% Cross relations, with the Parallel total including concerted parallel categories and the Series total including concerted series, following the reference ProteinCT definition. The lysozyme structure contains no deposited hydrogen atoms, so the hydrogen setting did not affect this example.

Multi-file analysis was used to examine 205 frames from replica 3 of a molecular-dynamics trajectory of the AF1c S203P fragment of the glucocorticoid receptor, an intrinsically disordered protein. The source simulation used GROMACS, the amber99SBdisp force field, 150 mM sodium chloride, a temperature of 310 kelvin and a one-microsecond duration for each replica. When the default heavy-atom criterion was used, three frames contained no residue pair meeting the contact threshold; the plugin identified and skipped those frames, leaving 202 in the final time-resolved output. The numbers of Series, Parallel and Cross relations fluctuated, and an increase followed by a return toward baseline was consistent with transient compaction, rather than proving a specific folding transition. Separate analyses with and without hydrogens showed the same qualitative pattern.

Validation extends beyond visual demonstrations. Automated tests install PyMOL 3.1.6.1 on clean Windows Server 2025, Ubuntu 24.04 and macOS 15 systems, install the plugin through PyMOL’s Plugin Manager and run the complete suite inside that environment. Interactive use was also checked manually on Windows 10, Windows 11, Ubuntu Linux 24.04 and macOS 15. On Apple Silicon, the tested PyMOL application ran through Rosetta 2 because no native arm64 build was available. The project supplies a Docker-based reproducible capsule, a PyMOL script, an exactly pinned conda environment, platform-specific lockfiles and automated parity artifacts comparing the plugin with the reference method. The MIT-licensed release is openly available through GitHub, with permanent archives provided through Zenodo.

Several limits constrain interpretation and use. The plugin analyzes polymer components, automatically excluding and reporting waters, ions, ligands and other non-polymer atoms. Nucleic acids and unknown residues are removed, while modified residues deposited as heteroatom records can leave gaps even when they occupy backbone positions. At least two intrachain contact pairs are required for a relation to exist, so extremely small structures cannot be analyzed in this way. Contact-length and energy filters apply only to single-chain structures, and the energy filter recognizes the 20 standard amino acids. Memory use is modest for a small static structure but rises sharply when an entire trajectory is colored, reaching approximately 1.3 gigabytes for 200 frames of a small protein. The tool therefore makes protein circuit topology more accessible, but its classifications remain threshold-dependent descriptions of contact organization rather than direct measurements of binding strength or physical entanglement.

Subject of Research: A PyMOL plugin for analyzing and visualizing protein circuit topology.

Article Title: PyMOL plugin for protein circuit topology

Article References: Dimiņš, M., Bazba, A., Mogyorósi, Á., Kennon, E., Fiol, T. D., Hagen, L. A., Sheikhhassani, V., Akulov, V., & Mashaghi, A. (2026). PyMOL plugin for protein circuit topology. SoftwareX, 36, Article 103142. https://doi.org/10.1016/j.softx.2026.103142

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103142

Keywords: PyMOL, protein topology, ProteinCT, structural biology, molecular visualization, protein contacts, intrinsically disordered proteins, molecular dynamics, open-source software, circuit topology, plugin, protein

The new server builds on COCaDA, short for COntact search pruning by C-alpha Distance Analysis, a command-line tool the group previously developed for detecting interatomic contacts at large scale. The core insight behind COCaDA is elegantly simple: rather than exhaustively checking every possible pair of atoms in a protein — a computation that grows explosively with protein size — the algorithm uses the distances between C-alpha atoms, the backbone reference points of each amino acid residue, to prune the search space. If two residues’ C-alpha atoms are too far apart for any of their side-chain atoms to plausibly interact, the algorithm never bothers examining those atom pairs at all. This pruning strategy slashes the computational cost of contact detection dramatically, making it feasible to analyze entire structural databases rather than one protein at a time. COCaDA-web now wraps that engine in an interactive interface and adds a precomputed database of roughly 746 million contacts, divided into about 690 million contacts within single protein chains and 55 million contacts between different chains.

Speed is one of the platform’s headline claims. According to the authors, COCaDA-web takes less than a second to process proteins of up to 1,000 residues, a size range that covers the vast majority of entries in the Protein Data Bank. That kind of responsiveness transforms the workflow of exploratory analysis. Instead of submitting a job, walking away, and returning hours later to a static text file, researchers can adjust parameters, rerun the analysis, and watch the results update in real time. The results for each protein entry are presented in a dynamic table that can be filtered and interrogated, alongside an interactive three-dimensional visualization that lets users see exactly where the detected contacts sit in the folded structure. For those who prefer to work in professional molecular graphics software, the server also generates annotated PyMOL sessions, pre-packaged so that the contact analysis can be opened, inspected and repurposed directly in one of the field’s standard visualization environments.

Two features distinguish COCaDA-web from earlier contact-analysis efforts. The first is pH value customization for protonation-aware analysis. Protonation — whether a particular chemical group on an amino acid carries an extra proton or has lost one — changes with the acidity of the surrounding environment, and those changes alter which atoms can form hydrogen bonds and salt bridges. A histidine residue that is positively charged at pH 5 may be neutral at pH 7.5, and that difference can reshape an entire interaction network at an active site or a protein-protein interface. By letting users specify the pH of interest, the server can produce contact maps that reflect the chemistry of a specific biological condition rather than a single default state. The second novelty is support for biological assemblies. Many proteins only do their real work as multimers — symmetric complexes of several identical or complementary chains — and analyzing contacts in the full biological assembly, rather than in a single asymmetric unit extracted from a crystal, can reveal interfaces that would otherwise be missed entirely.

To demonstrate what the platform can do, the authors turned to a pair of proteins that make for a striking natural experiment: Transthyretin, known as TTR, and 5-Hydroxyisourate Hydrolase, or HIUase. The two proteins are evolutionarily and structurally related, sharing a common fold, yet they perform utterly different biological jobs. TTR is a transport protein found in blood and cerebrospinal fluid that carries thyroxine and retinol-binding protein — and, infamously, it is prone to misfolding and aggregation into amyloid fibrils, the hallmark of diseases including familial amyloid polyneuropathy and senile systemic amyloidosis. HIUase, by contrast, is an enzyme involved in purine metabolism in many organisms, catalyzing the hydrolysis of 5-hydroxyisourate as part of the pathway that degrades uric acid; loss of its function is associated with gout, a painful condition caused by urate crystal deposition. Two proteins with such similar architecture but such divergent — and clinically consequential — behaviors are exactly the kind of case where contact-level analysis can expose what the static fold alone cannot.

Running both proteins through COCaDA-web, the researchers found meaningful differences in the patterns of interatomic contacts, particularly in the central cavities of the two proteins. These cavities — the internal spaces that in TTR form the thyroxine-binding channel and in HIUase contribute to the enzyme’s active-site environment — showed distinct contact signatures that help explain how two proteins built on the same structural scaffold came to serve such different functions. The comparison illustrates a broader principle in structural biology: sequence and fold tell only part of the story. The fine-grained geography of atomic interactions, especially within cavities and at interfaces, encodes the functional specialization that evolution has layered onto a shared architectural blueprint. Tools that make those signatures easy to compare, side by side and residue by residue, give researchers a new lens on questions of functional divergence, protein engineering and drug targeting.

The scale of the underlying database deserves emphasis. Three-quarters of a billion contacts, precomputed and indexed, means that much of the drudgery of contact analysis has already been done before a user ever opens the website. A researcher investigating a protein family can pull up precomputed contact data for hundreds of related structures instantly, comparing interaction networks across species, mutants or ligand-bound states without running a single calculation. For proteins not yet in the database, users can submit their own queries and have the server compute contacts on demand, with results delivered in the same dynamic table and 3D visualization environment. The server also integrates with AlphaFold, the deep-learning system that has predicted structures for hundreds of millions of proteins, extending the reach of contact analysis beyond experimentally determined structures into the vast space of computationally predicted ones.

Accessibility is a deliberate design goal. COCaDA-web is freely available at bioinfo.dcc.ufmg.br/cocada-web with no login requirements, and the team has included step-by-step usage guides to lower the barrier for newcomers. That matters because contact analysis has historically been a gatekept skill: the traditional implementations, as the authors note, remain computationally expensive and pose significant scalability and usability challenges. A graduate student in a biology lab without a computing cluster, a clinician curious about the structural basis of an amyloid disease, or a teacher building a lesson on protein structure can all now interrogate the same data that specialists use. The work was funded by Brazilian agencies CAPES, FAPEMIG and CNPq, and the authors acknowledge advice from Carlos Henrique da Silveira, Lucas Bleicher and Miguel Ortega in developing the new features.

The broader significance of tools like COCaDA-web lies in how they reshape the rhythm of discovery in structural bioinformatics. As predicted structures from AlphaFold and experimental structures from cryo-electron microscopy flood the public databases, the bottleneck is shifting from obtaining structures to interpreting them. Contact networks are one of the most information-dense summaries of a structure — a fingerprint that captures chemistry, geometry and environment in a single representation — and making those fingerprints searchable, comparable and visualizable at web speed turns a specialist computation into a routine exploratory act. For diseases of protein misfolding, where the transition from a soluble transporter to a pathological aggregate hinges on subtle shifts in which atoms find which neighbors, that accessibility could accelerate the search for the molecular tipping points. And for the everyday work of characterizing enzymes, engineering binders and annotating newly sequenced genomes, a tool that answers contact questions in under a second may simply become part of how structural questions get asked.

Subject of Research: Interactive web server for exploratory analysis of interatomic contacts in protein structures

Article Title: COCaDA-web: an interactive web server for exploratory analysis of interatomic contacts in proteins

Article References: Lemos, R. P., Mariano, D., Bastos, A. L. A., Silveira, S. D. A., & de Melo-Minardi, R. C. (2026). COCaDA-web: an interactive web server for exploratory analysis of interatomic contacts in proteins. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06624-8

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06624-8

Keywords: COCaDA-web, protein contacts, structural bioinformatics, Protein Data Bank, web server, interatomic interactions, Transthyretin, amyloidosis, 5-Hydroxyisourate Hydrolase, protein structure, biological assemblies, protonation