Predict optimal protein construct boundaries from sequence data.
npx skills add https://github.com/farnunglab/benchaid --skill constructboundary
Please help me install this Agent Skill. Skill: constructboundary Source: https://github.com/farnunglab/benchaid/tree/main/skills/constructboundary Command: npx skills add https://github.com/farnunglab/benchaid --skill constructboundary
This tool analyzes protein sequences to identify optimal truncation boundaries for recombinant expression constructs, enabling efficient structural biology workflows and higher-quality purified proteins.
Run with a UniProt ID (for example CHD1_HUMAN) to obtain predicted construct boundaries.
High-intent search queries and answers about installing and using this skill.
The tool identifies optimal truncation boundaries by analyzing protein sequences to maximize recombinant expression and crystallization potential. It suggests domain-based and disorder-informed truncations that improve purified protein quality for structural biology workflows.
Protein truncation design accepts UniProt IDs, gene names, or raw sequences as valid inputs. This flexibility allows you to generate boundary options regardless of whether you have a database identifier or just sequence data.
Yes, construct boundary prediction leverages evidence from UniProt, AlphaFold DB, and PDB data. It aligns predicted boundaries with known structures and confidence scores to ensure evidence-informed design.
The best way to define domain boundaries is using an explicit scoring scheme that ranks boundary predictions based on structural evidence. Applying domain boundaries, truncations, and disordered regions ensures constructs maximize expression potential.
Yes, construct boundary prediction works with disordered regions across protein targets. It suggests disorder-informed truncations that remove flexible regions to maximize expression and crystallization potential.
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