Antibody Engineering & Optimization

AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. Covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.

KEY PRINCIPLES

:

Report-first approach

- Create optimization report before analysis

Evidence-graded humanization

- Score based on germline alignment and framework retention

Developability-focused

- Assess aggregation, stability, PTMs, immunogenicity

Structure-guided

- Use AlphaFold/PDB structures for CDR analysis

Clinical precedent

- Reference approved antibodies for validation

Quantitative scoring

- Developability score (0-100) combining multiple factors

English-first queries

- Always use English terms in tool calls, even if user writes in another language. Respond in user's language

When to Use

Apply when user asks:

"Humanize this mouse antibody sequence"

"Optimize antibody affinity for [target]"

"Assess developability of this antibody"

"Predict immunogenicity risk for [sequence]"

"Engineer bispecific antibody against [targets]"

"Reduce aggregation in antibody formulation"

"Design pH-dependent binding antibody"

"Analyze CDR sequences and suggest mutations"

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

Create the report file FIRST

:

antibody_optimization_report.md

Progressively update

as analysis completes

Output separate files

:

optimized_sequences.fasta

- All optimized variants

humanization_comparison.csv

- Before/after comparison

developability_assessment.csv

- Detailed scores

See

REPORT_TEMPLATE.md

for the full report template with section formats.

2. Documentation Standards (MANDATORY)

Every optimization MUST include per-variant documentation with:

Original and optimized sequences

Humanization score (% human framework)

CDR preservation confirmation

Metrics table (humanness, aggregation risk, predicted KD, immunogenicity)

Data source citations

Phase 0: Tool Verification

Required Tools

Tool

Purpose

Category

IMGT_search_genes

Germline gene identification

Humanization

IMGT_get_sequence

Human framework sequences

Humanization

SAbDab_search_structures

Antibody structure precedents

Structure

TheraSAbDab_search_by_target

Clinical antibody benchmarks

Validation

AlphaFold_get_prediction

Structure modeling

Structure

iedb_search_epitopes

Epitope identification

Immunogenicity

iedb_search_bcell

B-cell epitope prediction

Immunogenicity

UniProt_get_protein_by_accession

Target antigen information

Target

STRING_get_interactions

Protein interaction network

Bispecifics

PubMed_search

Literature precedents

Validation

CRITICAL

SOAP tools (IMGT, SAbDab, TheraSAbDab) require an

operation

parameter. See

QUICK_START.md

for correct usage.

Workflow Overview

Phase 1: Input Analysis & Characterization

├── Sequence annotation (CDRs, framework)

├── Species identification

├── Target antigen identification

├── Clinical precedent search

└── OUTPUT: Input characterization

↓

Phase 2: Humanization Strategy

├── Germline gene alignment (IMGT)

├── Framework selection

├── CDR grafting design

├── Backmutation identification

└── OUTPUT: Humanization plan

↓

Phase 3: Structure Modeling & Analysis

├── AlphaFold prediction

├── CDR conformation analysis

├── Epitope mapping

├── Interface analysis

└── OUTPUT: Structural assessment

↓

Phase 4: Affinity Optimization

├── In silico mutation screening

├── CDR optimization strategies

├── Interface improvement

└── OUTPUT: Affinity variants

↓

Phase 5: Developability Assessment

├── Aggregation propensity

├── PTM site identification

├── Stability prediction

├── Expression prediction

└── OUTPUT: Developability score

↓

Phase 6: Immunogenicity Prediction

├── MHC-II epitope prediction (IEDB)

├── T-cell epitope risk

├── Aggregation-related immunogenicity

└── OUTPUT: Immunogenicity risk score

↓

Phase 7: Manufacturing Feasibility

├── Expression level prediction

├── Purification considerations

├── Formulation stability

└── OUTPUT: Manufacturing assessment

↓

Phase 8: Final Report & Recommendations

├── Ranked variant list

├── Experimental validation plan

├── Next steps

└── OUTPUT: Comprehensive report

Phase 1: Input Analysis & Characterization

Goal

Annotate sequences, identify species/germline, find clinical precedents.

Key steps

:

Annotate CDRs using IMGT numbering (CDR-H1: 27-38, CDR-H2: 56-65, CDR-H3: 105-117)

Identify closest human germline genes via

IMGT_search_genes

Search clinical precedents via

TheraSAbDab_search_by_target

Get target antigen info via

UniProt_get_protein_by_accession

Output

Sequence information table, CDR annotation, target info, clinical precedent list.

See

WORKFLOW_DETAILS.md

Phase 1 for code examples.

Phase 2: Humanization Strategy

Goal

Select human framework, design CDR grafting, identify backmutations.

Key steps

:

Search IMGT for IGHV/IGKV human germline genes

Score candidate frameworks by identity, CDR compatibility, and clinical use

Design CDR grafting onto selected framework

Identify Vernier zone residues that may need backmutation (positions 2, 27-30, 47-48, 67, 69, 71, 78, 93-94)

Generate at least 2 variants: full humanization and with key backmutations

Calculate humanization score (framework humanness, CDR preservation, T-cell epitopes, aggregation risk)

Output

Framework selection rationale, grafting design, backmutation analysis, humanized sequences.

See

WORKFLOW_DETAILS.md

Phase 2 for code examples.

Phase 3: Structure Modeling & Analysis

Goal

Predict structure, analyze CDR conformations, map epitope.

Key steps

:

Predict Fv structure via

AlphaFold_get_prediction

(VH:VL)

Assess pLDDT scores by region (framework, CDRs, interface)

Classify CDR canonical structures and calculate RMSD

Search known epitopes via

iedb_search_epitopes

Compare with clinical antibody structures via

SAbDab_search_structures

Output

Structure quality table, CDR conformation analysis, epitope mapping, structural comparison.

See

WORKFLOW_DETAILS.md

Phase 3 for code examples.

Phase 4: Affinity Optimization

Goal

Design affinity-improving mutations via computational screening.

Key steps

:

Identify interface residues (distance cutoff 4.5 A)

Screen all amino acid substitutions at CDR interface positions

Rank by predicted binding energy change (ddG < -0.5 kcal/mol = favorable)

Design combination strategy: single -> double -> triple mutants

Consider CDR-H3 extension, tyrosine enrichment, salt bridge formation

Optional: pH-dependent binding via histidine substitutions

Output

Ranked mutation list, combination strategy, expected affinity improvements.

See

WORKFLOW_DETAILS.md

Phase 4 for code examples.

Phase 5: Developability Assessment

Goal

Comprehensive developability scoring (0-100) across five dimensions.

Key steps

:

Aggregation

Find aggregation-prone regions, calculate TANGO/AGGRESCAN scores, assess pI

PTM liability

Scan for deamidation (NG/NS), isomerization (DG/DS), oxidation (Met/Trp), N-glycosylation (N-X-S/T)

Stability

Predict thermal stability (Tm target >70C, Tonset >65C)

Expression

Predict CHO titer and soluble fraction

Solubility

Predict maximum formulation concentration

Scoring

Weighted average (aggregation 0.30, PTM 0.25, stability 0.20, expression 0.15, solubility 0.10).

Tiers: T1 (>75), T2 (60-75), T3 (<60).

Output

Component scores, overall score, tier classification, mitigation recommendations.

See

WORKFLOW_DETAILS.md

Phase 5 and

CHECKLISTS.md

for scoring details.

Phase 6: Immunogenicity Prediction

Goal

Predict immunogenicity risk and design deimmunization strategy.

Key steps

:

Scan 9-mer peptides against IEDB for MHC-II binding epitopes

Count non-human residues in framework regions

Assess aggregation-related immunogenicity

Calculate total risk score (0-100, lower is better): Low <30, Medium 30-60, High >60

Propose deimmunization mutations (remove T-cell epitopes while preserving CDRs)

Compare with clinical precedent ADA rates

Output

T-cell epitope list, risk score breakdown, deimmunization strategy, clinical comparison.

See

WORKFLOW_DETAILS.md

Phase 6 for code examples.

Phase 7: Manufacturing Feasibility

Goal

Assess expression, purification, formulation, and CMC feasibility.

Key steps

:

Assess codon optimization for CHO, identify rare codons

Design signal peptide

Plan 3-step purification: Protein A capture -> cation exchange polishing -> viral nanofiltration

Recommend formulation (buffer, pH, stabilizer, tonicity)

Define analytical characterization panel (SEC-MALS, CEX, CE-SDS, SPR, DSF)

Estimate CMC timeline and costs (typically 18-24 months, $1.65-2.65M to IND)

Output

Expression assessment, purification strategy, formulation recommendation, CMC timeline.

See

MANUFACTURING.md

for detailed manufacturing content and

WORKFLOW_DETAILS.md

Phase 7 for code.

Phase 8: Final Report & Recommendations

Goal

Compile all findings into a ranked recommendation with validation plan.

Key outputs

:

Top candidate

with key metrics (humanness, affinity, developability, immunogenicity, stability, expression)

Key improvements

table comparing original vs. optimized

Experimental validation plan

In vitro (3-4 months) -> Lead optimization (2-3 months) -> Preclinical (6-12 months)

Backup variants

with profiles and recommendations

IP considerations

FTO analysis, CDR novelty, patentability

Next steps

Immediate (month 1-3), short-term (4-6), long-term (7-24)

See

REPORT_TEMPLATE.md

for the full report template.

Tool Reference

IMGT Tools

IMGT_search_genes

Search germline genes (IGHV, IGKV, etc.)

IMGT_get_sequence

Get germline sequences

IMGT_get_gene_info

Database information

Antibody Databases

SAbDab_search_structures

Search antibody structures

SAbDab_get_structure

Get structure details

TheraSAbDab_search_therapeutics

Search by name

TheraSAbDab_search_by_target

Search by target antigen

Immunogenicity

iedb_search_epitopes

Search epitopes

iedb_search_bcell

B-cell epitopes

iedb_search_mhc

MHC-II epitopes

iedb_get_epitope_references

Citations

Structure & Target

AlphaFold_get_prediction

Structure prediction

UniProt_get_protein_by_accession

Target info

PDB_get_structure

Experimental structures

Systems Biology (for Bispecifics)

STRING_get_interactions

Protein interactions

STRING_get_enrichment

Pathway analysis Reference Files File Contents QUICK_START.md Getting started guide, SOAP tool parameters, Python SDK and MCP usage WORKFLOW_DETAILS.md Code examples for all 8 phases REPORT_TEMPLATE.md Full report template with section formats and example tables MANUFACTURING.md Detailed manufacturing content (expression, purification, formulation, CMC) EXAMPLES.md Complete clinical scenario examples (humanization, affinity, bispecific) CHECKLISTS.md Evidence grading, completeness checklists, scoring details, special considerations