AlphaFold-Multimer, Chai, and Boltz have commoditized complex prediction. But a predicted PDB file is not a decision. We answer the real biologics questions: Where is the epitope? Which residues are hotspots? Which CDR mutations should you make? Our protein-protein docking service delivers decision-ready packages — ranked poses, epitope/paratope maps, interface hotspot rankings, mutation priorities, and validation plans — for antibody engineering, PPI inhibitors, PROTAC ternary modeling, and bispecific design.
Generate and rank near-native PPI models using global/local docking, clustering, scoring consensus, and expert structural review. We separate true binders from docking artifacts.
Identify epitope, paratope, and interface hotspot residues that guide alanine scanning, CDR optimization, or variant prioritization.
Translate docking outputs into mutation suggestions, validation priorities, and optional MD/free-energy follow-up recommendations.
Predict antibody-antigen complex structures, map conformational epitopes and paratopes, prioritize CDR residues for optimization.
Docking + per-residue interaction analysis + ΔΔG-style mutation ranking to identify residues most relevant to binding stability.
Evaluate steric feasibility, linker geometry, and spatial orientation for complex biologics before experimental construct screening.
Model target-protein/E3-ligase ternary interfaces and prioritize linker or orientation hypotheses for downstream optimization.
Analyze transient interfaces and hotspot regions to support peptidomimetic, peptide binder, or PPI inhibitor discovery programs.
Follow docking with molecular dynamics to test interface stability, reduce false-positive poses, and improve confidence in experimental plans.
For antibody-antigen docking, epitope mapping, paratope prediction, and antibody affinity maturation modeling.
For PPI interface hotspot analysis, in silico alanine scanning, and ΔΔG mutation-effect prediction.
For bispecific antibody modeling, fusion protein modeling, linker optimization, and steric feasibility evaluation.
For PROTAC ternary complex modeling, E3 ligase target interface analysis, and targeted protein degradation modeling support.
AlphaFold / Chai / Boltz for initial screen + expert constraints & review — open models as tools, not threats.
Define biological objective, target pair, available structures, antibody/variant information, expected decisions, and validation plan.
Prepare PDB/AlphaFold models, chains, CDRs, cofactors, ions, and biologically relevant constraints for docking.
Run global/local or constraint-guided docking, cluster complex models, and apply multi-criteria scoring.
Identify epitope/paratope residues, hotspot contacts, salt bridges, hydrogen bonds, hydrophobic patches, and mutation candidates.
Validate candidate poses using MD, MM-PBSA/MM-GBSA, or related free-energy and per-residue decomposition workflows.
Deliver ranked models, visualizations, residue tables, confidence notes, and recommended wet-lab validation priorities.
| Project Input | How It Is Used | Typical Deliverable | Decision Value |
|---|---|---|---|
| Protein sequences; Antibody heavy/light chain sequences; CDR information; PDB structures or AlphaFold models | Used for structure preparation, chain assignment, docking setup, interface definition, and complex model generation. | Ranked protein-protein complex models; PDB-compatible structural files; Docking clusters and scoring summary; Structural confidence notes | Helps identify which predicted complex poses are most biologically plausible for downstream analysis. |
| Known epitope residues; Binding-site information; Mutation lists; Assay data or biological constraints | Used to guide constrained docking, refine pose selection, and focus interface interpretation on experimentally relevant regions. | Epitope/paratope residue tables; Interaction maps; Interface hotspot interpretation; Mutation-priority suggestions | Supports antibody engineering, alanine scanning, affinity maturation, and targeted validation planning. |
| Affinity maturation objective; PROTAC ternary modeling objective; Bispecific design objective; PPI inhibitor support objective | Used to select the proper docking strategy, scoring logic, interface-analysis depth, and optional validation workflow. | Actionable docking report; Ranked pose visualizations; Confidence notes; Recommended next-step plan | Helps translate docking results into practical experimental or design decisions. |
| MD validation requirement; Free-energy estimation requirement; Residue-level decomposition requirement; Interface stability assessment requirement | Used to extend docking results into stability testing, interaction persistence analysis, and energetic interpretation. | MD-refined complex assessment; MM-PBSA/MM-GBSA-style analysis; Residue-level energy contribution summary; Interface stability interpretation | Improves confidence in selected complex models before costly experimental validation. |
Goal: Prioritize CDR mutations.
Workflow: Antibody-antigen docking → epitope/paratope analysis → hotspot ranking → variant shortlist.
Goal: Evaluate target/E3 interface hypotheses.
Workflow: Ternary docking → linker orientation review → interface stability recommendation.
Goal: Evaluate spatial orientation and steric feasibility.
Workflow: Complex modeling → linker geometry review → construct-prioritization guidance.
Case 1: Enzyme-Protein Inhibitor Interaction Mechanisms Study
Research Summary: This study applies protein-protein docking to the burgeoning field of Proteolysis-Targeting Chimera (PROTAC) development. The research team utilized computational modeling to systematically map the energy landscape of ternary complexes formed between a target protein and an E3 ubiquitin ligase, mediated by small-molecule PROTACs. This work demonstrates the practical value of docking in predicting these complex, dynamic interactions, providing a theoretical foundation for the rational design of high-efficiency PROTAC molecules.
Figure 1. ANGPTL3 key residues recognize a positive electrostatic patch on EL. Positive electrostatic surface (blue) on EL (light gray) in interaction with the three negatively charged glutamic acid residues (orange) of ANGPTL3 (dark gray) in the docking poses used for MD simulations.1,3
Case 2: Precision Modeling of SARS-CoV-2 Antibody-Antigen Interactions
Research Summary: This research validated a novel docking pipeline integrating AlphaFold2, Rosetta, and replica-exchange molecular dynamics. As a primary application case, the authors successfully predicted the complex structure of the SARS-CoV-2 spike protein receptor-binding domain (RBD) and the neutralizing antibody CR3022 at atomic-level precision. This study showcases the power of modern docking technologies in resolving critical antibody-antigen interactions and understanding viral neutralization mechanisms, directly supporting the development of next-generation vaccines and therapeutics.
Figure 2. Global and local docking performance.2,3
References
We are not a prediction shop. We are a decision partner that helps you separate biologically relevant complexes from artifacts — and turn docking output into experimental priorities.
Useful inputs include protein sequences, structures, chain IDs, CDR information for antibodies, known epitope or binding data, mutation lists, project stage, and the intended downstream decision.
Yes. The report can include ranked complex models, interface residue tables, hotspot predictions, and mutation priorities that can be tested by mutagenesis, binding assays, or functional validation.
Yes. MD simulation can be used as a follow-up step to evaluate whether selected docking poses remain stable under dynamic conditions and to support free-energy or residue-level decomposition analysis.
No. This is an expert-led computational biology service, including project design, structure preparation, docking, interpretation, and technical reporting.
We use them as initial screening tools — then apply expert constraints, scoring consensus, clustering, and structural review. The goal is to separate biologically plausible poses from artifacts, not to accept raw predictions blindly.
Share your target pair, antibody sequences, or PROTAC components. We'll tell you which poses are plausible, which residues matter, and what to test next.
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