数据与复现蛋白结构与设计FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
AN

antibody-design-agent

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Antibody design: epitope mapping, CDR engineering, bispecific construction.

OpenClawNanoClaw分析处理复现实验antibody-design-agent🧠 bioos extended suitedrug discovery & designantibody

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/antibody-design-agent

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • This skill brings together cutting-edge tools ,用于 antibody engineering,,涵盖 MAGE (Monoclonal Antibody Generator) 、 RFdiffusion ,用于 Antibodies. It enables de novo design of antibodies against specific viral 或 tumoral targets。
  • De Novo Design:Generating antibody sequences/structures that bind to specific antigen。
  • Epitope Targeting:Designing VHH 或 binders ,用于 specific epitope on target protein。
  • Optimization:Improving affinity 或 stability of existing antibody candidate。
  • Viral Defense:Rapidly generating antibodies against novel viral strains。

原始文档

SKILL.md 摘录

Core Capabilities

  1. MAGE (Monoclonal Antibody Generator): Uses a protein language model to generate diverse antibody sequences against unseen viral strains.
  2. RFdiffusion for Antibodies: Generates 3D antibody structures that bind to a target structure with high precision.
  3. ProteinMPNN: Optimizes the sequence of the generated structures for solubility and expression.

Workflow

  1. Target Definition: Input the PDB structure or sequence of the antigen (target).
  2. Design Phase:
    • Use RFdiffusion to generate the backbone of the binder (CDR loops).
    • Use ProteinMPNN to design the sequence for the backbone.
    • Alternatively, use MAGE to generate sequences directly from viral strain data.
  3. Validation (In Silico): Use AlphaFold3 or ESMFold to predict the complex structure and assess binding confidence (pLDDT, PAE).
  4. Selection: Rank candidates for synthesis.

Example Usage

User: "Design a VHH nanobody that binds to the RBD of the SARS-CoV-2 KP.2 variant."

Agent Action:

  1. Retrieves RBD structure for KP.2.
  2. Runs RFdiffusion with "binder" constraints on the RBD surface.
  3. Generates 100 backbone candidates.
  4. Sequences them with ProteinMPNN.
  5. Folds the complexes with AlphaFold3 to verify binding interface.
  6. Returns top 5 sequences.

适用场景

  • **De Novo Design**:Generating antibody sequences/structures that bind to specific antigen。
  • **Epitope Targeting**:Designing VHH 或 binders ,用于 specific epitope on target protein。

不适用场景

  • Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。

相关技能

相关技能

返回目录
AL
数据与复现蛋白结构与设计

alphafold

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences f…

OpenClawNanoClaw分析处理
FreedomIntelligence/OpenClaw-Medical-Skills查看
BI
数据与复现蛋白结构与设计

bindcraft

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in…

OpenClawNanoClaw分析处理
FreedomIntelligence/OpenClaw-Medical-Skills查看
BI
数据与复现蛋白结构与设计

binder-design

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraf…

OpenClawNanoClaw分析处理
FreedomIntelligence/OpenClaw-Medical-Skills查看
BI
数据与复现蛋白结构与设计

binding-characterization

Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2)…

OpenClawNanoClaw分析处理
FreedomIntelligence/OpenClaw-Medical-Skills查看