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

bio-structural-biology-modern-structure-prediction

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

Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1. Use when predicting structures for novel proteins, protein complexes, or when comparing predictions across multiple methods.

OpenClawNanoClaw分析处理复现实验bio-structural-biology-modern-structure-prediction🧬 bioinformatics (gptomics bio-* suite)bioinformatics — structural biology & cheminformaticspredict

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-structural-biology-modern-structure-prediction

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

Python：ESMFold API ，通过 requests，local ESMFold ，支持 esm.pretrained。
预测 structure of my protein" → Run ML-based structure prediction ，使用 ESMFold (single-sequence，fast)，AlphaFold3 (MSA-based，highest accuracy)，Chai-1，或 Boltz-1 、 compare predictions across methods. Python：ESMFold API ，通过 requests，local ESMFold ，支持 esm.pretrained。
预测 protein structures ，使用 state-of- -art 机器学习 models. This covers cloud APIs，local installations，、 interpretation of results。
def extract_esmfold_plddt(pdb_text)：plddt = {} ，用于 line in pdb_text.split('\n')：if line.startswith('ATOM') 、 line[12：16].strip() == 'CA'：resnum = int(line[22：26]) bfactor = float(line[60：66]) plddt[resnum] = bfactor return plddt。

原始文档

SKILL.md 摘录

Model Comparison

Model	Complexes	Ligands	Speed	Access
AlphaFold3	Yes	Yes	Slow	Server only (2025)
ESMFold	No	No	Fast	API or local
Chai-1	Yes	Yes	Moderate	Local or API
Boltz-1	Yes	Yes	Moderate	Local
ColabFold	No*	No	Moderate	Colab/local

*ColabFold can predict complexes with AlphaFold-Multimer.

ESMFold (Fastest Single-Chain)

Goal: Predict a protein's 3D structure from its amino acid sequence using the ESMFold language model, which requires no MSA and runs in seconds.

Approach: Submit the sequence to the ESMFold API (or run locally with the esm library), retrieve the predicted PDB coordinates, and assess per-residue confidence via pLDDT scores in the B-factor column.

Local ESMFold

import torch
import esm

def predict_esmfold_local(sequence, device='cuda'):
    '''Run ESMFold locally (requires ~16GB GPU memory)'''
    model = esm.pretrained.esmfold_v1()
    model = model.eval().to(device)

    with torch.no_grad():
        output = model.infer_pdb(sequence)
    return output

适用场景

适合在predicting structures ，用于 novel proteins，protein complexes，或 when comparing predictions across multiple methods时使用。

不适用场景

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

上游相关技能

alphafold-predictions - Download pre-computed AlphaFold structures
structure-io - Parse and write structure files
geometric-analysis - RMSD, superimposition, distance calculations
structure-navigation - Navigate predicted structure hierarchy

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