How does this differ from ESMFold?

AlphaFold is the gold standard for high-accuracy predictions and complexes, whereas ESMFold is faster and recommended for rapid screening of single-chain proteins.

Is AlphaFold-Multimer supported for protein complexes?

Yes, the skill fully supports AlphaFold-Multimer for predicting multi-chain interactions by using the multimer model preset.

Can I run this skill without a local GPU?

Yes, this skill is designed to work with Modal, allowing you to run AlphaFold2 on remote A100 GPUs, or use ColabFold's MSA server to reduce local computational load.

What protein metrics does the AlphaFold skill provide?

The skill provides critical confidence metrics including pLDDT (per-residue confidence), pTM (global topology score), ipTM (interface quality for complexes), and PAE (predicted aligned error).

What are the hardware requirements for local execution?

For local runs, it is recommended to have at least 40GB of VRAM (e.g., NVIDIA A100) and 500GB of disk space for the required genetic databases.

AlphaFold Protein Structure Validation

Name: AlphaFold Protein Structure Validation
Author: adaptyvbio

byadaptyvbio

•

Data Science & ML

Validates protein designs and predicts complex structures using AlphaFold2 to ensure structural integrity and binding accuracy.

This skill enables Claude to perform high-fidelity protein structure prediction and validation within the protein design workflow. By leveraging AlphaFold2 and AlphaFold-Multimer, it allows researchers to predict single-chain folds, validate designed sequences, and model complex protein-protein interactions. It provides detailed confidence metrics like pLDDT and pTM, integrates with high-performance computing platforms like Modal, and offers decision-making logic for choosing between ColabFold, ESMFold, and local installations based on sequence length and accuracy requirements.

Key Features

01Comprehensive metrics including pLDDT, pTM, and ipTM calculation

02Automated extraction and interpretation of confidence scores

0375 GitHub stars

04Structure prediction for single proteins and multi-chain complexes

05Self-consistency validation for de novo protein designs

06Integrated support for Modal and ColabFold MSA servers

Use Cases

01Large-scale quality control screening of protein design campaigns

02Predicting the interface and binding affinity of protein-protein complexes

03Validating that a designed sequence folds into the intended 3D structure

Key Features

01Comprehensive metrics including pLDDT, pTM, and ipTM calculation

02Automated extraction and interpretation of confidence scores

0375 GitHub stars

04Structure prediction for single proteins and multi-chain complexes

05Self-consistency validation for de novo protein designs

06Integrated support for Modal and ColabFold MSA servers

Use Cases

01Large-scale quality control screening of protein design campaigns

02Predicting the interface and binding affinity of protein-protein complexes

03Validating that a designed sequence folds into the intended 3D structure