Predict biologically relevant complex models, map epitope/paratope regions, identify PPI interface hotspots and prioritize mutations for antibody engineering, protein design, PROTAC ternary modeling and experimental validation.
Generate and rank near-native PPI models using global/local docking, clustering, scoring consensus and expert structural review.
Identify epitope, paratope and interface hotspot residues that can 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, and prioritize CDR residues for optimization.
Use docking, per-residue interaction analysis and 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.
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 |
|---|---|---|---|
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Used for structure preparation, chain assignment, docking setup, interface definition and complex model generation. |
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Helps identify which predicted complex poses are most biologically plausible for downstream analysis. |
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Used to guide constrained docking, refine pose selection and focus interface interpretation on experimentally relevant regions. |
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Supports antibody engineering, alanine scanning, affinity maturation and targeted validation planning. |
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Used to select the proper docking strategy, scoring logic, interface-analysis depth and optional validation workflow. |
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Helps translate docking results into practical experimental or design decisions. |
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Used to extend docking results into stability testing, interaction persistence analysis and energetic interpretation. |
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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
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.
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