tooluniverse-vaccine-design

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Vaccine Design

疫苗设计

Computational pipeline for designing peptide/subunit vaccine candidates through epitope prediction, population coverage optimization, and immunogenicity assessment.

通过表位预测、人群覆盖优化和免疫原性评估设计肽/亚单位候选疫苗的计算流程。

Reasoning Strategy

推理策略

Vaccine design requires presenting the right epitopes to elicit protective immunity — not just any immune response, but one that is neutralizing, durable, and broadly applicable. For T-cell vaccines, the core tool is MHC binding prediction (IEDB tools): predict peptide-MHC affinity across multiple HLA alleles, then select epitopes with broad coverage of the target population. For antibody vaccines, prioritize surface-exposed conserved regions — a deeply buried or hypervariable region makes a poor antibody target. MHC binding does not equal immunogenicity; many good binders are not immunogenic in vivo due to tolerance, poor processing, or lack of T-cell help. A multi-epitope strategy (combining MHC-I for CD8+ CTL response, MHC-II for CD4+ helper response, and B-cell epitopes for antibody induction) is more robust than any single epitope. Conservation across pathogen strains is critical — an epitope that mutates under immune pressure (like HIV envelope hypervariable regions) is a poor vaccine target.

LOOK UP DON'T GUESS: Do not predict MHC binding or population coverage from memory — use

IEDB_predict_mhci_binding

and

IEDB_predict_mhcii_binding

for predictions and

IEDB_search_epitopes

for validated experimental data. Do not assume what's on the pathogen surface; retrieve annotated sequences from UniProt or BVBRC.

Key principles:

Epitope-driven — vaccines work by presenting epitopes to T/B cells; start with epitope prediction
Population coverage matters — HLA diversity means no single epitope covers everyone; design for breadth
Multi-epitope is better — combine CD8+ (MHC-I) and CD4+ (MHC-II) epitopes for robust immunity
Conservation = broad protection — conserved epitopes across strains provide cross-protective immunity
Evidence grading — T1: clinical trial data, T2: in-vivo immunogenicity, T3: in-vitro binding, T4: computational prediction only

疫苗设计需要呈现正确的表位以诱导保护性免疫——并非任意免疫反应，而是具备中和性、持久性和广泛适用性的免疫反应。对于T细胞疫苗，核心工具是MHC结合预测（IEDB工具）：预测多肽与多个HLA等位基因的MHC亲和力，然后选择能广泛覆盖目标人群的表位。对于抗体疫苗，优先选择暴露于表面的保守区域——深埋或高变区域不适宜作为抗体靶点。MHC结合不等于免疫原性；许多结合力强的表位由于免疫耐受、加工不良或缺乏T细胞辅助，在体内不具备免疫原性。多表位策略（结合用于CD8+细胞毒性T淋巴细胞反应的MHC-I表位、用于CD4+辅助T细胞反应的MHC-II表位，以及用于诱导抗体产生的B细胞表位）比单一表位策略更稳健。病原体菌株间的保守性至关重要——在免疫压力下发生突变的表位（如HIV包膜高变区）不适宜作为疫苗靶点。

查资料，勿臆测：不要凭记忆预测MHC结合或人群覆盖情况——使用

IEDB_predict_mhci_binding

和

IEDB_predict_mhcii_binding

进行预测，使用

IEDB_search_epitopes

获取经过验证的实验数据。不要假设病原体表面的结构；从UniProt或BVBRC检索带注释的序列。

核心原则:

表位驱动——疫苗通过向T/B细胞呈递表位发挥作用；从表位预测开始
人群覆盖至关重要——HLA多样性意味着没有单一表位能覆盖所有人；设计需兼顾广度
多表位更优——结合CD8+（MHC-I）和CD4+（MHC-II）表位以获得稳健免疫效果
保守性=广泛保护——跨菌株保守的表位提供交叉保护性免疫
证据分级——T1：临床试验数据，T2：体内免疫原性数据，T3：体外结合数据，T4：仅计算预测数据

When to Use

适用场景

"Design a vaccine against [pathogen]"
"Predict T-cell epitopes for [protein]"
"What MHC-I epitopes does [protein] have?"
"Assess population coverage of these epitopes"
"Find conserved epitopes across [pathogen] strains"

Not this skill: For HLA typing or allele frequency only, use

tooluniverse-hla-immunogenomics

. For antibody engineering, use

tooluniverse-antibody-engineering

"针对[病原体]设计疫苗"
"预测[蛋白质]的T细胞表位"
"[蛋白质]具有哪些MHC-I表位？"
"评估这些表位的人群覆盖情况"
"寻找[病原体]各菌株间的保守表位"

不适用场景：仅进行HLA分型或等位基因频率分析时，使用

tooluniverse-hla-immunogenomics

；进行抗体工程时，使用

tooluniverse-antibody-engineering

。

Core Tools

核心工具

Tool	Use For
`IEDB_search_epitopes`	Search experimentally validated epitopes
`IEDB_get_epitope`	Get detailed epitope data (assay results, MHC restriction)
`iedb_search_mhc`	Search validated MHC binding assay data
`IEDB_predict_mhci_binding`	Predict MHC-I binding (NetMHCpan EL; rank < 0.5% = strong binder)
`IEDB_predict_mhcii_binding`	Predict MHC-II binding (NetMHCIIpan EL; CD4+ helper epitopes)
`UniProt_get_entry_by_accession`	Get antigen protein sequence
`UniProt_search`	Find pathogen protein sequences
`BVBRC_search_genome_features`	Search pathogen proteomes
`alphafold_get_prediction`	Get/predict antigen 3D structure
`EnsemblVEP_annotate_hgvs`	Check epitope conservation across variants
`PubMed_search_articles`	Find published vaccine studies
`search_clinical_trials`	Find ongoing vaccine clinical trials

工具	用途
`IEDB_search_epitopes`	搜索经过实验验证的表位
`IEDB_get_epitope`	获取详细表位数据（检测结果、MHC限制性）
`iedb_search_mhc`	搜索经过验证的MHC结合检测数据
`IEDB_predict_mhci_binding`	预测MHC-I结合情况（NetMHCpan EL；排名<0.5%=强结合剂）
`IEDB_predict_mhcii_binding`	预测MHC-II结合情况（NetMHCIIpan EL；CD4+辅助表位）
`UniProt_get_entry_by_accession`	获取抗原蛋白质序列
`UniProt_search`	查找病原体蛋白质序列
`BVBRC_search_genome_features`	搜索病原体蛋白质组
`alphafold_get_prediction`	获取/预测抗原3D结构
`EnsemblVEP_annotate_hgvs`	检查表位在变异体中的保守性
`PubMed_search_articles`	查找已发表的疫苗研究
`search_clinical_trials`	查找正在进行的疫苗临床试验

Workflow

工作流程

Phase 0: Antigen Selection
  Pathogen → essential surface proteins → sequence retrieval
    |
Phase 1: T-Cell Epitope Prediction
  MHC-I (CD8+ CTL) and MHC-II (CD4+ helper) binding prediction
    |
Phase 2: B-Cell Epitope Prediction
  Linear and conformational B-cell epitopes for antibody response
    |
Phase 3: Population Coverage
  HLA allele frequencies → design for target population
    |
Phase 4: Conservation Analysis
  Cross-strain epitope conservation → broad protection
    |
Phase 5: Candidate Assembly & Report
  Multi-epitope construct design → immunogenicity assessment

阶段0: 抗原选择
  病原体 → 关键表面蛋白 → 序列检索
    |
阶段1: T细胞表位预测
  MHC-I（CD8+细胞毒性T细胞）和MHC-II（CD4+辅助T细胞）结合预测
    |
阶段2: B细胞表位预测
  用于诱导抗体反应的线性和构象B细胞表位
    |
阶段3: 人群覆盖分析
  HLA等位基因频率 → 针对目标人群设计
    |
阶段4: 保守性分析
  跨菌株表位保守性 → 广泛保护
    |
阶段5: 候选疫苗组装与报告
  多表位构建体设计 → 免疫原性评估

Phase 0: Antigen Selection

阶段0: 抗原选择

Best antigens for vaccines: Surface-exposed, essential for pathogen function, conserved across strains.

python

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疫苗最优抗原：暴露于表面、对病原体功能至关重要、跨菌株保守。

python

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Find pathogen surface proteins

查找病原体表面蛋白

UniProt_search(query="[organism] AND locations:(location:cell surface) AND reviewed:true")

Or search BVBRC for annotated pathogen proteomes

或搜索BVBRC获取带注释的病原体蛋白质组

BVBRC_search_genome_features(keyword="surface protein", genome_id="[taxon_id]")


**Antigen prioritization**: prefer surface-exposed (secreted/outer membrane) over cytoplasmic; >95% conserved across strains; essential for pathogen viability; known immunogen in natural infection. Use UniProt subcellular location annotations and PubMed to verify these properties.

BVBRC_search_genome_features(keyword="surface protein", genome_id="[taxon_id]")


**抗原优先级**：优先选择表面暴露（分泌/外膜）蛋白而非胞内蛋白；跨菌株保守性>95%；对病原体生存至关重要；在自然感染中为已知免疫原。使用UniProt亚细胞定位注释和PubMed验证这些特性。

Phase 1: T-Cell Epitope Prediction

阶段1: T细胞表位预测

MHC-I epitopes (CD8+ cytotoxic T cells — kill infected cells):

python

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MHC-I表位（CD8+细胞毒性T细胞——杀伤受感染细胞）：

python

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Option A: Search for KNOWN validated epitopes from IEDB

选项A: 从IEDB搜索已知的验证表位

iedb_search_mhc( mhc_class="I", qualitative_measure="Positive", filters={"source_organism_iri": "eq.NCBITaxon:2697049"}, # SARS-CoV-2 select=["linear_sequence", "mhc_restriction", "qualitative_measure"], limit=50 )

Option B: PREDICT novel peptide binding (recommended for new proteins)

选项B: 预测新型多肽结合情况（推荐用于新蛋白质）

IEDB_predict_mhci_binding( sequence="YOUR_PROTEIN_SEQUENCE", # full protein or peptide allele="HLA-A*02:01", # or H-2-Kd for mouse method="netmhcpan_el", # EL = eluted ligand (recommended) length=9 # 8-11 for MHC-I )

IEDB_predict_mhci_binding( sequence="YOUR_PROTEIN_SEQUENCE", # 完整蛋白质或多肽 allele="HLA-A*02:01", # 小鼠使用H-2-Kd method="netmhcpan_el", # EL = 洗脱配体（推荐方法） length=9 # MHC-I表位长度为8-11 )

Returns peptides ranked by percentile_rank:

返回按percentile_rank排序的多肽:

< 0.5% = strong binder (include in vaccine)

<0.5% = 强结合剂（纳入疫苗）

0.5-2% = moderate binder (consider)

0.5-2% = 中等结合剂（考虑纳入）

> 2% = weak/non-binder (exclude)

>2% = 弱/非结合剂（排除）


**MHC-II epitopes** (CD4+ helper T cells — activate B cells and CD8+ T cells):

```python
iedb_search_mhc(
    mhc_class="II",
    qualitative_measure="Positive",
    filters={"source_organism_iri": "eq.NCBITaxon:2697049"},
    limit=50
)

Binding affinity interpretation:

IC50 (nM)	Classification	Vaccine Relevance
< 50	Strong binder	Include — high presentation probability
50-500	Moderate binder	Consider — may contribute to response
500-5000	Weak binder	Exclude — unlikely to be presented
> 5000	Non-binder	Exclude

HLA supertype strategy: For broad coverage, predict against HLA supertypes:

A2 supertype (A02:01, A02:06, A*68:02) — covers ~40% globally
A3 supertype (A03:01, A11:01, A*31:01) — covers ~25%
B7 supertype (B07:02, B35:01, B*51:01) — covers ~25%
A2 + A3 + B7 + B44 combined — covers >90% of most populations


**MHC-II表位**（CD4+辅助T细胞——激活B细胞和CD8+T细胞）：

```python
iedb_search_mhc(
    mhc_class="II",
    qualitative_measure="Positive",
    filters={"source_organism_iri": "eq.NCBITaxon:2697049"},
    limit=50
)

结合亲和力解读:

IC50 (nM)	分类	疫苗相关性
<50	强结合剂	纳入——呈递概率高
50-500	中等结合剂	考虑纳入——可能有助于免疫反应
500-5000	弱结合剂	排除——呈递可能性低
>5000	非结合剂	排除

HLA超型策略：为实现广泛覆盖，针对HLA超型进行预测：

A2超型（A02:01, A02:06, A*68:02）——覆盖全球约40%人群
A3超型（A03:01, A11:01, A*31:01）——覆盖约25%
B7超型（B07:02, B35:01, B*51:01）——覆盖约25%
A2 + A3 + B7 + B44组合——覆盖大多数人群的90%以上

Phase 2: B-Cell Epitope Prediction

阶段2: B细胞表位预测

B-cell epitopes trigger antibody production. Look for:

Linear epitopes: Continuous peptide sequences (easier to synthesize)
Conformational epitopes: 3D surface patches (requires structural data)

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B细胞表位触发抗体产生。需关注：

线性表位：连续多肽序列（易于合成）
构象表位：3D表面区域（需要结构数据）

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Check for known B-cell epitopes

查找已知B细胞表位

IEDB_search_epitopes(query="[protein_name]", epitope_type="B cell")

Get structure for conformational epitope prediction

获取结构以进行构象表位预测

alphafold_get_prediction(uniprot_id="[accession]")


**B-cell epitope criteria**: Surface-exposed loops, hydrophilic regions, flexible regions (high B-factor). Combine computational prediction with structural analysis.

alphafold_get_prediction(uniprot_id="[accession]")


**B细胞表位标准**：暴露于表面的环区、亲水区域、柔性区域（高B因子）。结合计算预测与结构分析。

Phase 3: Population Coverage

阶段3: 人群覆盖分析

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Search for epitopes restricted to common HLA alleles in target population

搜索针对目标人群常见HLA等位基因的表位

NOTE: No HLA frequency tool exists in ToolUniverse. For population coverage:

注意：ToolUniverse中无HLA频率工具。如需进行人群覆盖分析：

1. Use IEDB Analysis Resource (tools.iedb.org/population) for population coverage calculation

1. 使用IEDB分析资源（tools.iedb.org/population）计算人群覆盖情况

2. Use the HLA supertype strategy (see above) to ensure broad coverage

2. 使用上述HLA超型策略确保广泛覆盖

3. Search PubMed for published HLA frequency data: PubMed_search_articles(query="HLA allele frequency [population]")

3. 搜索PubMed获取已发表的HLA频率数据：PubMed_search_articles(query="HLA allele frequency [population]")


**Population coverage targets**:

| Coverage Level | Interpretation | Action |
|---------------|---------------|--------|
| >90% | Excellent — vaccine will work in most individuals | Proceed to development |
| 70-90% | Good — most people covered; some populations underserved | Add more epitopes for uncovered HLA types |
| 50-70% | Moderate — significant gaps | Redesign with broader HLA coverage |
| <50% | Poor — vaccine will miss too many people | Fundamental redesign needed |


**人群覆盖目标**:

| 覆盖水平 | 解读 | 行动 |
|---------------|---------------|--------|
| >90% | 极佳——疫苗对大多数个体有效 | 推进开发 |
| 70-90% | 良好——覆盖大多数人群；部分人群未充分覆盖 | 添加针对未覆盖HLA类型的更多表位 |
| 50-70% | 中等——存在显著缺口 | 重新设计以扩大HLA覆盖范围 |
| <50% | 较差——疫苗会遗漏过多人群 | 需要根本性重新设计 |

Phase 4: Conservation Analysis

阶段4: 保守性分析

Check if epitopes are conserved across pathogen strains/variants:

python

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检查表位在病原体菌株/变异体中的保守性：

python

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Search for protein variants across strains

搜索跨菌株的蛋白质变异情况

PubMed_search_articles(query="[pathogen] [protein] sequence variation strains")

Check specific mutations in epitope regions

检查表位区域的特定突变

EnsemblVEP_annotate_hgvs(hgvs_notation="[variant_in_epitope]")


**Conservation interpretation**:
- **100% conserved** across all known strains → ideal vaccine target
- **>95% conserved** → good target; monitor emerging variants
- **80-95% conserved** → may need strain-specific variants in construct
- **<80% conserved** → avoid; pathogen evolves to escape this epitope

EnsemblVEP_annotate_hgvs(hgvs_notation="[variant_in_epitope]")


**保守性解读**:
- **100%保守**于所有已知菌株 → 理想疫苗靶点
- **>95%保守** → 良好靶点；监测新出现的变异体
- **80-95%保守** → 构建体中可能需要包含菌株特异性变异体
- **<80%保守** → 避免使用；病原体可通过进化逃逸该表位

Phase 5: Candidate Assembly & Report

阶段5: 候选疫苗组装与报告

Multi-epitope construct design principles:

Include 3-5 MHC-I epitopes (CD8+ response)
Include 2-3 MHC-II epitopes (CD4+ helper response)
Include 1-2 B-cell epitopes (antibody response)
Connect with appropriate linkers (AAY for MHC-I, GPGPG for MHC-II)
Add adjuvant sequence if needed (e.g., flagellin domain for TLR5)

Report structure:

Antigen Selection — rationale, conservation, essentiality
Epitope Map — all predicted epitopes with binding affinities and HLA restrictions
Top Epitopes — ranked by binding strength × conservation × population coverage
Population Coverage — % coverage per major world population
Conservation Analysis — strain coverage, escape risk assessment
Construct Design — multi-epitope sequence with linkers
Clinical Precedent — existing vaccines/trials for related antigens
Limitations — predicted only (T4 evidence); needs experimental validation

多表位构建体设计原则:

包含3-5个MHC-I表位（CD8+免疫反应）
包含2-3个MHC-II表位（CD4+辅助免疫反应）
包含1-2个B细胞表位（抗体反应）
使用合适的连接肽连接（MHC-I使用AAY，MHC-II使用GPGPG）
必要时添加佐剂序列（如用于TLR5的鞭毛蛋白结构域）

报告结构:

抗原选择——依据、保守性、必要性
表位图谱——所有预测表位及其结合亲和力和HLA限制性
顶级表位——按结合强度×保守性×人群覆盖情况排名
人群覆盖——主要人群的覆盖百分比
保守性分析——菌株覆盖范围、逃逸风险评估
构建体设计——带连接肽的多表位序列
临床先例——相关抗原的现有疫苗/试验
局限性——仅为预测结果（T4证据）；需实验验证

Limitations

局限性

All predictions are computational (T4 evidence) — experimental validation (binding assays, immunogenicity studies) is required before any clinical development
No immunogenicity guarantee — MHC binding ≠ immunogenicity; many good binders are not immunogenic in vivo
B-cell epitope prediction is less reliable than T-cell; conformational epitopes require accurate structures
No adjuvant optimization — adjuvant selection requires empirical testing
Pathogen evasion — rapidly evolving pathogens (HIV, influenza) may escape epitope-based vaccines

所有预测均为计算结果（T4证据）——临床开发前需进行实验验证（结合检测、免疫原性研究）
无法保证免疫原性——MHC结合≠免疫原性；许多结合力强的表位在体内不具备免疫原性
B细胞表位预测的可靠性低于T细胞表位；构象表位需要准确的结构数据
未涉及佐剂优化——佐剂选择需经验测试
病原体逃逸——快速进化的病原体（如HIV、流感）可能逃逸基于表位的疫苗