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Peptide Design

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ssinder
peptide-design

This skill should be used when analyzing or designing antimicrobial peptides — computing physicochemical properties such as molecular weight, isoelectric point, Kyte-Doolittle hydrophobicity and instability index, classifying peptides as antimicrobial or non-antimicrobial from GO terms and sequence features, deduplicating peptide libraries by clustering, and exporting FASTA or CSV. 用于多肽理化性质计算、抗菌肽判别、序列聚类去冗余与导出的场景。

Overview

Publisherssinder
Repositorybioinformatics-agent-skills
Skill namepeptide-design
Stars
75
Forks
2
Bundled files
7
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ssinder on GitHub. Read the source before you install it.

Installation

Install the Peptide Design AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ssinder/bioinformatics-agent-skills/tree/main/skills/peptide-design
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ssinder/bioinformatics-agent-skills.git /tmp/bioinformatics-agent-skills
mkdir -p .claude/skills
cp -r /tmp/bioinformatics-agent-skills/skills/peptide-design .claude/skills/peptide-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Peptide Design in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Peptide Design on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Peptide Design is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

从 FASTA 或 CSV 读取多肽序列，用自包含的公式实现计算全部理化性质（分子量、等电点、疏水性、净电荷、不稳定指数、二级结构倾向、溶解度、Boman 值），再结合 UniProt GO 术语与关键词做抗菌肽判别打分，最后用聚类去冗余提取代表序列并导出 FASTA / CSV。

理化性质不依赖 Biopython：分子量按氨基酸残基质量表累加加水的质量；等电点用 Henderson-Hasselbalch 在 pH 0–14 的电荷曲线上二分求零点；疏水性用 Kyte-Doolittle 标准值表；不稳定指数用 Guruprasad 的 DIHEX 二面角公式。

When to Use This Skill

  • 需要批量计算多肽的理化性质（分子量、pI、疏水性、净电荷、稳定性）作为筛选依据
  • 需要判断一批候选肽中哪些是抗菌肽、哪些是非抗菌肽（用于构建负样本集）
  • 需要对肽库做去冗余（MMseqs2 不可用时有内置聚类回退）
  • 需要为下游模型准备带特征列的训练数据
  • 需要把筛选结果导出为 FASTA 或带理化性质的 CSV

Quick Start

bash
export PYTHONPATH="$PWD/packages"

# 1) 计算 FASTA 中所有序列的理化性质
python skills/peptide-design/scripts/analyze_peptides.py \
    --input peptides.fasta --output results/peptides_props.csv \
    --report results/summary.txt

# 2) 判别抗菌肽并导出筛选结果 + FASTA
python skills/peptide-design/scripts/analyze_peptides.py \
    --input peptides.csv --sequence-column sequence \
    --classify --antimicrobial-threshold 5 \
    --label antimicrobial \
    --export-fasta results/amp.fasta --output results/amp.csv

# 3) 提取高质量样本（需 UniProt protein_existence 注释）
python skills/peptide-design/scripts/analyze_peptides.py \
    --input uniprot_peptides.csv --classify --min-quality 60 \
    --require-complete-metadata --output results/high_quality.csv

# 4) 聚类去冗余（优先 MMseqs2，缺失时自动回退内置算法）
python skills/peptide-design/scripts/cluster_peptides.py \
    --input peptides.fasta --method auto --min-identity 0.3 \
    --output results/representatives.fasta --output-csv results/cluster_members.csv

Workflow

  1. 读取序列：FASTA（多行序列、; 注释、CRLF 均可）或 CSV（自动嗅探列名，缺 sequence 列时依次回退到 seq/peptide/aa_seq）
  2. 理化性质计算（peptide_properties.py）
    • 分子量 = Σ残基平均质量 + 18.0153
    • 等电点：pH 0–14 步长 0.1 扫描电荷曲线 → 找符号翻转区间 → 二分 60 次求精确零点
    • 净电荷：Henderson-Hasselbalch 分别计算 N 端/Lys/Arg/His 的质子化与 C 端/Asp/Glu/Cys/Tyr 的去质子化比例
    • 疏水性：Kyte-Doolittle 平均值 + Hobbs 疏水矩（单位向量合成）
    • 不稳定指数：Guruprasad DIHEX 权重表，II = 100/L × Σ DIHEX(di-1, di)
    • 构象倾向：Chou-Fasman 螺旋/折叠/拐角参数 + 螺旋终止残基修正
    • 溶解度：Zhao (2009) 残基级 log-s 贡献 + 长度项 + 螺旋项
    • Boman 值、芳香性、脂肪族指数、消光系数、估算 kD
  3. 判别打分：注释层（GO 术语 5 组 + 关键词 5 组加权）× 0.6 + 理化层（8 条规则）× 0.4
  4. 质量评估：UniProt 蛋白质存在水平 + 序列完整性 + 注释完整度 + 理化合理性
  5. 筛选：长度区间 / 未知残基 / 元数据完整度 / 质量分 / 判别类别
  6. 聚类去冗余（cluster_peptides.py）：MMseqs2 easy-cluster 优先，回退到内置 k-mer Jaccard + LCS identity + average-linkage 聚类
  7. 导出：CSV（UTF-8 BOM）/ FASTA / JSON / 文本报告

Parameters

参数说明默认值
--input输入 FASTA 或 CSV必填
--format输入格式（auto 按扩展名与内容嗅探）auto
--sequence-columnCSV 序列列名sequence
--id-column / --name-columnCSV 的 ID 列 / 名称列id / protein_name
--go-column / --keyword-columnCSV 的 GO 术语列 / 关键词列go_terms / keywords
--classify启用抗菌肽判别关闭
--antimicrobial-threshold抗菌肽判定阈值5.0
--non-antimicrobial-threshold非抗菌肽判定阈值2.0
--annotation-weight注释层在综合分中的权重0.6
--label保留哪一类：antimicrobial / non_antimicrobial / uncertain / allantimicrobial
--min-length / --max-length长度过滤区间0（不限）
--drop-unknown剔除含 X 等未知残基的序列关闭
--require-complete-metadata只保留同时有 GO 与关键词的记录关闭
--min-quality最低质量分0
--export-fasta / --report / --json-output额外导出 FASTA / 报告 / JSON无
--method（聚类）auto / mmseqs / internalauto
--min-identity（聚类）簇内最小序列 identity0.3
--representative（聚类）medoid / longest / firstmedoid

Outputs

文件说明
peptide_analysis.csv主结果，含 24 个理化性质列 + label / combined_score / quality_score / qc_flags
selected.fasta筛选后序列（--export-fasta）
summary.txt统计报告：长度/MW/pI/疏水性分布、稳定性占比、判别分布、GO 覆盖度、物种分布
cluster_members.csv聚类明细：cluster_id / representative / member_id / is_representative / cluster_size
representatives.fasta代表序列（每簇一条）
cluster_report.txt聚类统计：簇数、冗余率、最大/中位簇大小、单序列簇数

Validation

  • 分子量合理性：20 aa 肽的 MW 应在 2000–2500 Da 区间；含 C 残基时每形成一对二硫键减少约 2 Da
  • 等电点合理性：酸性肽（Asp/Glu 多）pI < 7，碱性肽（Lys/Arg 多）pI > 7；等电点应落在 pH 1–14 之间
  • 疏水性：Kyte-Doolittle 平均值 > 1.8 强疏水，< 0 强亲水；典型两性抗菌肽在 0.5–2.5 且疏水矩 > 0.4
  • 稳定性：不稳定指数 < 40 为预测稳定，> 40 提示可能快速降解（此时 qc_flags 会自动标记）
  • 未知残基：含 X 的序列所有定量指标可靠性下降，qc_flags 与 has_unknown 会标出
  • 判别结果：抽查各 label 的样本，确认 hit_keywords 与 classification_reasons 合理；纯 FASTA 输入无注释时只能靠理化层，此时 uncertain 占比升高属正常
  • 聚类合理性：冗余率应与序列多样性相符——短肽库通常冗余率 > 50%；若全部聚成单序列簇，说明 --min-identity 过严
  • 代表序列：medoid 代表序列应与簇内其他成员有肉眼可见的相似性；可用 --representative longest 交叉验证

References

  • references/methodology.md —— 各理化性质的公式推导、参数选择与判别规则设计原理
  • references/troubleshooting.md —— 常见报错、数值异常与排错步骤

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Peptide Design AI skill do?

This skill should be used when analyzing or designing antimicrobial peptides — computing physicochemical properties such as molecular weight, isoelectric point, Kyte-Doolittle hydrophobicity and instability index, classifying peptides as antimicrobial or non-antimicrobial from GO terms and sequence features, deduplicating peptide libraries by clustering, and exporting FASTA or CSV. 用于多肽理化性质计算、抗菌肽判别、序列聚类去冗余与导出的场景。

Why use Peptide Design on TypingMind?

Because you install it once and use it with any model. Peptide Design is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Peptide Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ssinder/bioinformatics-agent-skills/tree/main/skills/peptide-design. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Peptide Design?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Peptide Design?

As many as you like. As long as a model supports skills, you can use Peptide Design with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Peptide Design AI skill free?

Yes. It is published on GitHub by ssinder under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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