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# Google Antigravity: Accelerating Antimicrobial Peptide Search

**[DeepMind](https://daily.dev/sources/dm)** · 5 min read · 0 upvotes · 0 comments

## Summary

Google Antigravity, Google's agentic development platform, now offers a science workbench for discovering antimicrobial peptides (AMPs). It uses ProtNLM, a model that predicts protein function from amino acid sequences, to scan millions of uncharacterized proteins in UniProt. In a demonstrated workflow, researchers used it to find a novel AMP candidate with predicted activity against pathogens involved in respiratory tract infections, in a search focused on Streptococcus pseudopneumoniae, and the lead is now undergoing wet-lab validation. The release also adds a Biomolecular Structure Viewer, a 3D tool that lets researchers and the agent jointly inspect protein structures from the AlphaFold Database and mark regions of interest for the agent to reason over.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://deepmind.google/blog/accelerating-antimicrobial-discovery>

## Questions this post answers

### What is ProtNLM and how is it used to find antimicrobial peptides?

ProtNLM, short for Protein Natural Language Model, is a deep learning model developed by Google that predicts biological function directly from raw amino acid sequences. It reads the evolutionary 'grammar' of protein sequences, enriching millions of previously unnamed UniProt entries with functional labels, which helps researchers identify candidate antimicrobial peptides (AMPs) hidden in unannotated protein data.

_Developers exploring agentic AI applications in science can track tools like this on daily.dev._

### What is Google Antigravity's Biomolecular Structure Viewer?

It is a 3D visualization tool embedded directly into Google Antigravity that lets a researcher and an AI agent share a visual state of a protein structure. The agent can retrieve structures from the AlphaFold Database and highlight functional regions, while the researcher can mark residues or binding pockets and ask questions, letting the agent reason over that exact structural context.

_Those evaluating agentic AI workbenches can follow tooling updates like this on daily.dev._

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