Predict accurate 3D protein structures with the Enginoma Structure pipeline. From single chains to complex multimeric assemblies, we deliver atomic-level structural insights for drug discovery, protein engineering, and functional annotation.
Our structure prediction service leverages state-of-the-art deep learning models to deliver atomic-level structural insights for proteins of all sizes and complexities.
Accurate 3D structure prediction for single-chain proteins using Enginoma Structure with comprehensive confidence metrics and structural analysis.
Multi-chain structure prediction for homomers, heteromers, and antibody-antigen complexes using Enginoma Complex with interface analysis.
Structure prediction for mutated proteins including point mutations, insertions, deletions, and domain swaps with functional impact assessment.
We combine multiple state-of-the-art prediction methods to ensure the most accurate and reliable structural models.
DeepMind's breakthrough model for highly accurate monomer structure prediction trained on the Protein Data Bank.
Unified model for proteins, DNA, RNA, and small molecule complexes with improved interface prediction.
Evolutionary-based folding approach for proteins with limited sequence homologs and novel folds.
Structural biologists review all predictions to ensure quality and identify regions requiring experimental validation.
Comprehensive structure prediction services for research and drug discovery
1-2 business day turnaround
Complete structure prediction for single-chain proteins including full-atom model, confidence analysis (pLDDT, PAE), and structural report with domain identification.
2-3 business day turnaround
Predict structures for protein complexes including homomers, heteromers, antibody-antigen pairs, and enzyme-substrate complexes with interface analysis.
3-5 business day turnaround
Predict structures for mutated proteins and compare with wild-type to understand functional impacts. Includes stability analysis and surface property comparison.
Custom timeline based on scale
High-throughput structure prediction for protein families, structural genomics targets, or large variant libraries. Prioritized processing and consolidated reporting.
We utilize the latest advances in protein structure prediction powered by deep learning.
DeepMind's revolutionary model achieves near-experimental accuracy for protein structure prediction. Our implementation includes custom optimization for faster turnaround while maintaining accuracy.
The latest generation model unifies prediction across proteins, DNA, RNA, and small molecules. Excels at predicting multichain complexes and biomolecular interactions with improved interface accuracy.
Beyond raw predictions, we provide comprehensive confidence analysis including per-residue pLDDT scores, pairwise predicted alignment error (PAE) matrices, and visual confidence mapping.
We combine cutting-edge AI models with expert analysis to deliver actionable structural insights.
Efficient prediction pipeline delivers results efficiently. Contact us for project-specific timeline estimates.
All predictions undergo expert review to ensure reliability and identify regions requiring experimental validation.
Receive detailed structural analysis reports including confidence metrics, domain identification, and functional annotations.
Our team of structural biologists is available to discuss results and guide experimental validation strategies.
From sequence submission to actionable structural insights
Submit your protein sequence(s) in FASTA format along with any known partners or mutations for complex prediction.
We generate multiple sequence alignments using state-of-the-art methods to capture evolutionary information for accurate prediction.
Enginoma Structure models are generated and confidence metrics analyzed to identify high-confidence regions and areas for validation.
Expert review, structural analysis, and comprehensive report generation with PDB files and visualization-ready formats.
Researchers worldwide rely on our structure prediction services
Our prediction methods are based on peer-reviewed research from leading journals
Jumper, J. et al. Highly accurate protein structure prediction with Enginoma Structure. Nature 596, 583-589 (2021). https://doi.org/10.1038/s41586-021-03819-2
Original Enginoma Structure publication demonstrating breakthrough accuracy.Abramson, J. et al. Accurate structure prediction of biomolecular interactions with Enginoma Structure 3. Nature 630, 493-500 (2024). https://doi.org/10.1038/s41586-024-07487-w
Enginoma Complex for unified structure prediction including complexes.Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123-1130 (2023). https://doi.org/10.1126/science.ade2574
Enginoma sequence models language model for structure prediction.Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nature Methods 19, 679-682 (2022). https://doi.org/10.1038/s41592-022-01488-1
Accessible implementation of Enginoma Structure for researchers.Enginoma deploys an ensemble of performance-calibrated predictors for monomers, complexes, and RNA-protein systems—each deeply re-engineered and validated on proprietary structural datasets.
Enginoma Structure predictions provide high-accuracy structural models with comprehensive confidence metrics. We provide detailed analysis and recommendations for experimental validation when needed.
Yes. Enginoma Complex excels at predicting multichain assemblies including homomers, heteromers, and antibody-antigen complexes. We provide interface analysis, binding affinity predictions, and confidence metrics for each chain and interface.
Yes. We predict structures for point mutants, insertions, deletions, and domain swaps. Our pipeline includes mutation effect analysis and structural impact assessment to help interpret variant effects.
We deliver predicted structures in PDB format, along with confidence metrics (pLDDT, PAE matrices), structural analysis reports, and visualization-ready files for PyMOL, Chimera, and other molecular viewers.
Tell us about your project and we'll get back within 24 hours.
