Generate antibody sequences and structures conditioned on antigen targets.
npx skills add https://github.com/yjcyxky/biominer-skills --skill iggm
Please help me install this Agent Skill. Skill: iggm Source: https://github.com/yjcyxky/biominer-skills/tree/main/iggm Command: npx skills add https://github.com/yjcyxky/biominer-skills --skill iggm
IgGM provides a unified framework to generate antibody sequences and structures conditioned on target antigens, enabling faster exploration of design spaces.
Run IgGM on a GPU-enabled Docker image with an antigen PDB and a design FASTA to generate candidate antibodies.
High-intent search queries and answers about installing and using this skill.
De novo antibody design is automated by conditioning generative models on an antigen target. You provide a target antigen PDB file and a FASTA sequence to generate candidate antibody sequences and structures.
Yes, antibody humanization and affinity maturation are supported through framework design. The workflow automates sequence optimization to improve target affinity while maintaining humanized framework regions.
Antibody generation requires an antigen PDB file for structural conditioning and a FASTA file with designable regions clearly marked. These inputs drive the generative model to output candidate antibodies.
Yes, the antibody design workflow runs within a GPU-enabled Docker environment. You must use the specific Docker image to execute the generative model for structure prediction and sequence generation.
Nanobody design is supported through structure prediction and design workflows. By providing the target antigen PDB and marked FASTA inputs, the model generates candidate nanobody sequences and structures.
Inverse antibody design generates sequences and structures conditioned on target antigens. It works by using a generative model within a Docker workflow to explore design spaces from antigen to antibody.
junior1pDesign antigen-conditioned antibody and nanobody CDR loops from X-masked FASTA inputs.
adaptyvbioPredict protein structures from FASTA sequences using Chai-1.
mkurmanAnalyze protein sequences for N-glycosylation sequons and O-glycosylation hotspots.
silversteinPredict ranked protein–ligand binding poses with confidence scores from PDB structures or sequences and SMILES inputs.
