Amyloid fibrils are built from layers of peptides stacked like bricks, most commonly in so-called in-register parallel beta-sheets, in which identical residues in neighboring chains align laterally and the strands run in the same direction. The precise packing of these sheets determines a fibril’s toxicity, its ability to seed new aggregates, and its capacity to propagate between cells. Experimental techniques such as cryo-electron microscopy, atomic force microscopy, and solid-state nuclear magnetic resonance have delivered a growing catalogue of high-resolution fibril structures, and dedicated databases now support systematic comparison of these assemblies. Yet the known structures remain sparse relative to the enormous diversity of amyloidogenic sequences and the wide range of conditions under which fibrillation can occur.

Artificial intelligence systems such as AlphaFold have transformed the prediction of folded proteins and their complexes, but amyloids present a fundamentally different challenge. These methods are trained largely on well-folded, monomeric proteins and typically aim to identify a single native conformation at the global minimum of an energy landscape. Amyloid fibrils, by contrast, are polymorphic: a single peptide sequence can adopt multiple structurally distinct fibril architectures depending on environmental conditions, and the energy differences between these alternative packings can be subtle. Short, intrinsically disordered peptides also lack the evolutionary signals that power modern AI predictors, and benchmarking studies have shown reduced accuracy for sequences dominated by flexible coil regions. A recent AI approach called RibbonFold has begun to address polymorphism by embedding parallel in-register constraints and a polymorphism-aware loss function into an AlphaFold2-derived framework, but the field clearly needs complementary strategies.

InRegPep takes a deliberately different route, built around multiscale simulation rather than machine learning. At its core lies the CABS coarse-grained model, a long-established framework in which each amino acid residue is represented by up to four pseudo-atoms: the alpha carbon, the beta carbon, a united atom for the remainder of the side chain, and a pseudo-atom used to define backbone hydrogen bonds. The alpha carbons sit on a dense three-dimensional lattice while the remaining pseudo-atoms float off-lattice, a hybrid scheme that preserves near-atomistic backbone resolution of roughly half an angstrom while dramatically cutting computational cost. Because lattice positions restrict conformational space to a set of inexpensive local moves, the model can sample vast stretches of conformational landscape, and its statistical force field explicitly favors extended beta-strand conformations and inter-chain contacts, exactly the interactions that stabilize amyloid-like arrangements.

The workflow begins when a user submits a peptide sequence of 10 to 100 amino acids along with parameters such as secondary structure assignments, the number of docking runs, Monte Carlo cycles, temperature range, pH, and optional restraints mimicking disulfide bridges. Distance restraints between corresponding residues in neighboring chains bias the simulation toward the in-register parallel arrangement. Five identical peptides are then docked simultaneously, with every chain remaining fully flexible throughout the process. Up to 100 independent docking runs can generate as many as one million unique aggregate models, each represented as a simplified alpha-carbon trace.

Selection is where the pipeline earns its keep. Twenty candidate aggregates are chosen from the generated pool using a combination of energy-based criteria, a structural symmetry measure called pcaRMSD, and k-means clustering. These survivors are reconstructed into all-atom representations, geometry-optimized, and subjected to molecular dynamics simulations with the OpenMM engine. Stability is judged from changes in RMSD and pcaRMSD along the trajectories, intermolecular contact analysis, and conservation of secondary structure. The top ten models are ranked, each classified as plausible or unstable, and for plausible aggregates the server assembles short protofilaments of fifteen peptides by replicating the central layer along the fibril’s long axis using transformation matrices derived from the predicted aggregate geometry.

The benchmark results are striking. Tested against fifteen experimentally determined protofilament structures solved by NMR or cryo-EM, all with in-register parallel beta-sheet architecture and peptide lengths from 22 to 40 residues, InRegPep produced a model within 5 angstroms RMSD of the experimental structure among its ten top-scoring predictions for ten of the fifteen systems. In one case, a fibril from a beta-2 microglobulin fragment, the best model came within 2.52 angstroms. The authors note that near-native structures appeared in the docking trajectories for almost all systems, but that ranking them correctly remains the hardest step, a limitation they acknowledge openly. The method also performs better on shorter peptides; tests on longer targets such as an 88-residue tau fragment found polymorph-like structures in the trajectories, but the scoring function failed to elevate them into the top ten.

Beyond single-structure prediction, the ensemble-oriented design makes InRegPep a platform for exploring amyloid polymorphism itself. Analysis of docking trajectories for tau and amyloid-beta 42 showed that the generated ensembles include structures closely resembling experimentally determined polymorphic conformations, or at least capture key structural motifs of these assemblies. Because amyloid-forming peptides populate multiple competing minima corresponding to fibrils that can differ in morphology, stability, and biological behavior, viewing amyloid structures as ensembles of accessible states rather than single architectures is increasingly seen as essential. The server’s downloadable output includes cluster centroids from independent docking trajectories, giving researchers a practical map of the conformational landscape for their sequence of interest.

The practical implications extend across medicine and nanotechnology. Accurate protofilament models can guide the design of aggregation inhibitors, molecules that modulate fibril growth, and compounds that bind selectively to particular fibril forms, which matters because different polymorphs of the same protein are associated with different diseases and disease subtypes. The rational engineering of peptides with altered aggregation propensity also opens doors to biomaterials and nanostructured devices built from amyloid-like assemblies. The authors suggest their models can serve as foundations for mutational design and for downstream experimental or computational validation.

InRegPep is freely available at bioinfo.imdik.pan.pl/inregpep, with benchmark data deposited on Zenodo, and the work was supported by the National Science Center of Poland. It arrives at a moment when cryo-EM is revealing ever more fibril structures, including brain-derived polymorphs of amyloid-beta and tau, yet the computational toolkit for predicting new assemblies from sequence alone remains thin. By combining efficient coarse-grained sampling with symmetry-aware scoring, clustering, and atomistic refinement, the Warsaw team has delivered a workflow that complements rather than competes with AI-based prediction, and one that embraces the polymorphism that makes amyloids both so dangerous and so scientifically fascinating.

Subject of Research: Computational multiscale modeling of amyloid peptide aggregate and protofilament structures

Article Title: InRegPep: a web server for modeling of parallel beta-sheet fibrillar aggregates of amyloidogenic peptides

Article References: Puławski, W., Niewieczerzał, S., Koliński, A., Firlik, G., & Koliński, M. (2026). InRegPep: a web server for modeling of parallel beta-sheet fibrillar aggregates of amyloidogenic peptides. Frontiers in Molecular Biosciences, 13, Article 1908797. https://doi.org/10.3389/fmolb.2026.1908797

Image Credits: AI Generated

DOI: 10.3389/fmolb.2026.1908797

Keywords: amyloid fibrils, InRegPep, CABS model, coarse-grained simulation, molecular dynamics, protein aggregation, amyloid polymorphism, protofilament, structure prediction, web server, neurodegenerative disease, computational biology

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