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Antibody engineering
Good antibodies need more than a binding score.
Review structure, humanness, stability, and developability before synthesis.
About Antibody engineering
Antibody engineering is the design, selection, and refinement of antibody candidates for a target. It combines sequence and structural decisions with checks such as numbering, germline context, humanness, developability, binding-site geometry, and complex modeling before experimental follow-up.
ProteinIQ connects those complementary analyses without separating the evidence from the candidate. Researchers can annotate heavy and light chains, assess humanness and sequence liabilities, generate or inspect structures, and compare docking or design outputs alongside the original inputs.
Begin with an antigen, antibody sequence, framework, or candidate set and choose a focused tool or workflow. Each run preserves the resulting tables, residue annotations, structures, confidence values, logs, and downloadable files for a traceable synthesis shortlist.
- 1
Target preparation
Repair the antigen structure, identify likely epitope regions, and review protonation before design.
PDBFixer
PDBFixer is an OpenMM-based tool used for fixing problems in protein/DNA/RNA structure files, including adding missing atoms, adding missing residues, and fixing improper formatting.
structure-analysisquality-validation+3
ScanNet
Geometric deep learning model for predicting protein binding sites directly from 3D structure. Identifies where proteins interact with other proteins, antibodies, or disordered proteins with high accuracy, including for novel protein folds.
interaction-predictiondeep-learning+3
PROPKA 3
Predict pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure. PROPKA calculates environment-driven pKa shifts for standard ionizable residues, terminal groups, and supported ligand atom types.
protein-analysisproperty-prediction+3 - 2
Candidate generation
Generate complementary antibody, Fab, and nanobody candidates while preserving each method’s native ranking.
BoltzGen
BoltzGen uses generative diffusion models to design protein, peptide, nanobody, and Fab binders against protein and small-molecule targets.
binder-designai-powered+5
RFantibody
Structure-based de novo antibody and nanobody design pipeline combining antibody-tuned RFdiffusion, ProteinMPNN sequence design, and antibody-tuned RoseTTAFold2 filtering.
binder-designai-powered+5
IgGM
IgGM is a generative foundation model for antibody and nanobody design against a target antigen. Supports CDR design, affinity maturation, inverse design, and framework design. Requires an antigen structure (PDB) and antibody sequences with "X" marking positions to design.
protein-designantibody-design+5 - 3
Structure and binding review
Check candidate geometry and estimate binding affinity from compatible antibody–antigen complexes.
MolProbity
Validate protein structure quality with all-atom contact analysis, Ramachandran plots, rotamer assessment, and geometry checks.
structure-analysisquality-validation+4
PPAP
PPAP predicts protein-protein binding affinity as −ΔG and Kd from a multi-chain PDB complex using interfacial graph features and ESM2-3B embeddings.
protein-analysisai-powered+3 - 4
Sequence and developability review
Number variable regions and review humanness, solubility, stability, and sequence-level risk before synthesis.
ANARCII
Number antibody, T cell receptor, and shark VNAR/VHH sequences with language models, or renumber PDB, mmCIF, and mmJSON structures using ANARCII.
sequence-analysisai-powered+4
BioPhi
Antibody humanization and humanness evaluation platform from Merck. Sapiens mode uses deep learning trained on the Observed Antibody Space (OAS) to humanize antibody sequences, while OASis mode evaluates humanness using 9-mer peptide search against human antibody databases.
sequence-designai-powered+3
NetSolP-1.0
Predict protein solubility and usability for E. coli expression using ESM protein language models
protein-analysisproperty-prediction+3
Protein stability prediction
Calculate sequence-derived indicators related to protein stability, including the Guruprasad instability index, aliphatic index, GRAVY, aromaticity, estimated net charge, and charged-residue fraction.
protein-analysisphysicochemical-properties+2
Prot2Prop
Predict multiple protein developability properties from amino-acid sequences using a multitask ProstT5 adapter.
protein-analysisdeep-learning+5
AbLang-2
Antibody-specific language model for predicting non-germline residues (NGL) in antibody sequences. AbLang-2 addresses germline bias in existing antibody language models by focusing on somatic hypermutation patterns, enabling more accurate prediction of amino acid likelihoods and generation of context-aware embeddings for antibody sequences.
sequence-analysisai-powered+5
Ready to research antibodies?
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$23/mo/user
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Most popularFor commercial research
$79/mo/user
96,000 credits/user/year
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Questions & answers
ProteinIQ supports antibody engineering workflows for numbering, germline context, humanness review, humanization triage, developability screening, antibody structure prediction, nanobody modeling, antibody-antigen docking, and design review. Each workflow keeps upstream scores, files, and tool conventions visible for scientific inspection.
ProteinIQ antibody tools can accept FASTA sequences, paired heavy and light chains, single-domain or nanobody sequences, PDB structures, and antigen structures depending on the upstream model. The platform keeps each accepted input format tied to the tool that produced the downstream annotation, model, or score.
Yes. ProteinIQ can run antibody humanization and humanness review workflows using tools such as BioPhi, AbLang-style language model review, and humanization comparison steps. The answers are presented as sequence-level evidence for review, not as a hidden replacement for expert antibody engineering judgment.
Yes. ProteinIQ includes antibody and nanobody structure workflows that can turn variable-region sequences into modeled structures when the selected upstream tool supports that input. The generated PDB or related structure files, confidence values, and logs remain available for inspection and export.
Yes. ProteinIQ supports antibody-antigen docking and exploratory complex modeling workflows that can combine antibody chains or modeled antibody structures with an antigen structure. Docking outputs are useful for hypothesis generation and triage, while binding, specificity, and epitope claims still need experimental evidence.
ProteinIQ keeps antibody numbering, germline calls, humanness scores, developability tables, modeled structures, docking files, and logs associated with the input sequence or structure that produced them. This helps reviewers avoid separating heavy chains, light chains, residue annotations, and downstream models during triage.
Yes. ProteinIQ workflows are useful when you want connected antibody annotation, humanization, structure, and docking steps, but individual tools such as ANARCI, BioPhi, ImmuneBuilder, RFantibody, ParaSurf, and HADDOCK3 can also be opened directly for focused analysis.
ProteinIQ antibody workflows can export numbered sequence tables, humanness and developability CSV files, predicted structures, designed sequences, docking models, logs, and upstream result files depending on the tool. Exported outputs preserve the upstream labels and formats needed for review outside the platform.
No. ProteinIQ organizes computational antibody engineering evidence for prioritization and review. Expression, binding, specificity, immunogenicity, developability, and manufacturability experiments are still required before making biological, therapeutic, or clinical claims.