| name | alphagem |
| description | AlphaGEM — genome-scale metabolic modelling aided by protein structure alignment and deep-learning dark-metabolism mining. Use when: (1) Building or refining GEMs beyond sequence-only gap filling, (2) Integrating structure signals into metabolic networks, (3) Comparing to gapseq / VMH workflows. Upstream: https://github.com/hongzhonglu/AlphaGEM. Route via metagenomics-llm; pair with gapseq / vmh for classical GEM context.
|
| license | MIT |
| category | analysis-tools |
| tags | ["AlphaGEM","GEM","metabolism","structure","deep-learning"] |
| upstream | https://github.com/hongzhonglu/AlphaGEM |
| stage | mining |
AlphaGEM
Upstream: hongzhonglu/AlphaGEM
Citation
Han, W. et al. AlphaGEM enables precise genome-scale metabolic modelling by
integrating protein structure alignment with deep-learning-based dark
metabolism mining. Nat. Commun. (2026).
https://doi.org/10.1038/s41467-026-75549-w
See also docs/references.md.
Analytical thinking
Sequence-only GEM reconstruction misses “dark” metabolism. AlphaGEM combines
structure alignment with DL mining to improve genome-scale models. Use on
high-quality genomes/MAGs after mag-qc; interpret human/gut context with
vmh when relevant.
Models remain computational — not measured fluxes. Pin AlphaGEM version,
structure sources (e.g. AFDB), and gap-filling rules.
How to run
Decision tree
Metabolic modelling need?
├─ Classical pathway/GEM from MAG → gapseq
├─ Structure + dark metabolism mining → alphagem
├─ Human/gut knowledge integration → vmh
└─ Annotation distillate only → dram
Related skills
metagenomics-llm · gapseq · vmh · dram · deepblast · mag-qc ·
microbial-mining · tool-selection
