pgdh-evaluate

Synchronize and rank protein binder designs for the 15-PGDH target.

Updated Feb 27, 2026
One-click install
npx skills add https://github.com/alex-hh/in-silico-pgdh --skill pgdh-evaluate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pgdh-evaluate
Source: https://github.com/alex-hh/in-silico-pgdh/tree/main/.claude/skills/pgdh-evaluate
Command: npx skills add https://github.com/alex-hh/in-silico-pgdh --skill pgdh-evaluate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of collecting, ranking, and evaluating protein binder designs for the 15-PGDH target, automating complex computational biology workflows.

Core Features & Use Cases

  • Design Synchronization: Collects and standardizes design outputs from various tools into a central 'source of truth'.
  • Automated Evaluation: Submits jobs for refolding, cross-validation, and scoring using computational resources.
  • Ranking: Computes composite scores to rank designs based on multiple metrics.
  • Use Case: After generating new protein binder designs, use this Skill to automatically sync them, assess their designability through refolding, validate their binding confidence with Boltz-2, and score their interaction strength with ipSAE, ultimately ranking them for further consideration.

Quick Start

Use the pgdh-evaluate skill to collect and rank designs from S3.

Frequently Asked Questions about pgdh-evaluate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I rank and evaluate protein binder designs for the 15-PGDH target?▼

To evaluate protein binder designs for 15-PGDH, this Skill synchronizes outputs from various design tools, submits computational refolding and validation jobs, and ranks designs using composite metrics based on designability, Boltz-2 binding confidence, and ipSAE interaction strength.

What is the best way to automate protein design assessment and scoring using Boltz-2 and ipSAE?▼

Automated protein design assessment is achieved by submitting computational jobs to Lyceum for refolding and validation, scoring binding confidence with Boltz-2, evaluating interaction strength with ipSAE, and computing composite scores to rank the designs.

Can I use this Skill to sync and standardize protein binder outputs from multiple design tools?▼

Yes, this Skill synchronizes and standardizes protein binder outputs from multiple design tools by collecting them into a central source of truth, requiring integration with S3 for data storage and Lyceum for GPU job submission.

Do I need Lyceum and S3 integration to run automated computational drug discovery workflows?▼

Yes, you need Lyceum integration for GPU job submission and S3 for data storage to run these automated computational drug discovery workflows, enabling cross-validation and scoring of protein binder designs.

How does composite scoring work when ranking computational protein designs?▼

Composite scoring for ranking computational protein designs works by aggregating multiple evaluation metrics, including refolding designability, Boltz-2 binding confidence, and ipSAE interaction strength, into a unified score for prioritization.