Google Antigravity Accelerates Antimicrobial Peptide Discovery by Unifying AI Analysis and 3D Modeling

Google Antigravity Accelerates Antimicrobial Peptide Discovery by Unifying AI Analysis and 3D Modeling Google has introduced an agentic science workbench called Google Antigravity to speed up the discovery of antimicrobial peptides (AMPs) amid rising global antimicrobial resistance. The platform unifies multi-database searches, protein function predictions, and interactive 3D structural analysis into a single collaborative workspace. Antimicrobial resistance poses an urgent global threat, potentially causing 10 million annual deaths globally by 2050. Google Antigravity compresses fragmented database searches, literature synthesis, and structural evaluations into an interactive multi-turn session between researchers and an AI agent. The platform integrates ProtNLM to predict biological functions for millions of uncharacterized UniProt sequences directly from amino acid data. A walkthrough targeting Streptococcus pseudopneumoniae identified a novel candidate AMP active against respiratory pathogens, which is now undergoing wet-lab validation. Addressing Antimicrobial Resistance with AI Antimicrobial resistance threatens to cause an estimated 10 million deaths annually by 2050, driving an urgent need for new therapeutics. Antimicrobial peptides, such as bacteriocins, offer promising alternatives to conventional antibiotics because they target fundamental bacterial structures, making resistance harder to develop. While sequencing efforts have amassed billions of natural amino acid sequences, millions remain uncharacterized 'dark matter' in public repositories like UniProt. To decode these sequences, Google deployed ProtNLM, a deep learning model trained on amino acid sequences to predict biological functions directly from raw sequence data. Interactive Agentic Workflow and Candidate Discovery Google Antigravity enables researchers to guide scientific strategy while the AI agent performs cross-database querying, structural analysis, and literature synthesis. In a demonstration targeting Streptococcus pseudopneumoniae, a researcher and the agent computationally identified and validated a novel AMP candidate with predicted activity against respiratory tract pathogens. Although the candidate still requires resource-intensive wet-lab validation, the platform compressed days of fragmented database triage into a single session and allows queries across broader pathogen sets and environmental microbes. Shared 3D Biomolecular Visualization To streamline evaluation, Antigravity incorporates the Biomolecular Structure Viewer, a native 3D visualization tool connected to resources such as the AlphaFold Database. The viewer features bidirectional synchronization: the agent can highlight functional regions to explain hypotheses, while researchers can pin notes or select specific residues in 3D space to prompt immediate reasoning from the agent. This unified workflow eliminates the need to stitch together custom database query scripts, separate 3D modeling programs, and disconnected genomic tools.

Google推出Antigravity平台加速抗菌肽靶点发现与结构评估 面对日益严峻的抗微生物耐药性危机，Google展示了利用智能体开发平台Google Antigravity加速新型抗菌肽（AMP）发现的端到端工作流。该平台结合深度学习功能预测模型ProtNLM与内置3D结构可视化工具，将传统碎片化的数据库检索与分析压缩至单次人机协同交互中，目前已筛选出针对呼吸道感染病原体的新型候选靶点并进入湿实验验证阶段。 抗微生物耐药性预计到2050年可能每年导致全球1000万人死亡，开发不易产生耐药性的细菌素等抗菌肽成为关键替代路径。 Google开发的ProtNLM模型能直接从氨基酸序列预测生物功能，用于发掘UniProt中数以百万计未注释的蛋白质“暗物质”。 研究人员借助Antigravity在假肺炎链球菌中筛选出具有潜在活性的新型抗菌肽候选物，目前该候选物正在进行湿实验验证。 平台内嵌生物分子结构查看器（Biomolecular Structure Viewer），支持研究员与AI智能体双向同步共享3D分子视觉状态并展开结构推理。 耐药性危机与蛋白质暗物质挖掘 抗微生物耐药性是紧迫的全球健康危机，若不加以遏制，至2050年每年可能致死1000万人。天然进化出的抗菌肽和细菌素能精准针对细菌生理薄弱点，病原体极难对其产生耐药性。虽然基因测序产生了数十亿自然氨基酸序列，但大量序列因与已知蛋白差异巨大而缺乏功能注释，停留在数据库的暗物质区。Google的ProtNLM深度学习模型直接解析氨基酸序列的演化语法，为UniProt中数百万未命名的蛋白质条目补充了高可信度的功能标签。 人机协同的智能体工作流 Antigravity将传统的碎片化数据库排查整合为交互式多轮对话。研究人员负责把控科研方向与提出假设，AI智能体则承担跨数据库协调、结构分析与文献综合等繁重任务。在针对假肺炎链球菌的测试中，该流程迅速锁定了具有结构与基因组证据支持的新型抗菌肽候选物。科学家可随时调整筛选标准、序列长度或更换目标病原体，大幅降低了跨数据库生物学筛选的门槛。 双向同步的3D生物分子可视化交互 为解决以往需在多个建模软件和查询脚本间反复切换的痛点，Antigravity内置了生物分子结构查看器。智能体从AlphaFold数据库等来源调取结构后，可高亮关键功能区域以阐述假设；研究人员在3D空间中标记特定残基或结合口袋的操作也会实时同步给智能体，使AI能够结合当前观察到的三维空间上下文进行针对性推理，从而在进入昂贵的湿实验前筛选出高置信度候选分子。

New antibiotics are urgently needed to help with rising levels of antimicrobial resistance, but searching for new therapeutics is often a slow, labor-intensive process. With Google Antigravity, our agentic development platform, this process can now be made easier. Google Antigravity provides an agentic science workbench that empowers biologists to discover, structurally evaluate, and prioritize promising targets quickly, compressing what used to be a fragmented search over multiple databases into a single session. It also helps researchers scan through millions of previously uncharacterized proteins in the UniProt database using ProtNLM, a model that predicts biological function from amino acid sequences. This technical walkthrough shows how biologists used Google Antigravity to uncover a novel antimicrobial peptide candidate which has predicted activity against the pathogens involved in respiratory tract infections. The lead identified here is now undergoing wet-lab validation. Rising [antimicrobial resistance](https://www.gov.uk/government/publications/health-matters-antimicrobial-resistance/health-matters-antimicrobial-resistance) is one of the most pressing global health crises of our time and could result in an estimated 10 million annual deaths globally by 2050. To prevent this, we urgently need to discover novel therapeutics. Antimicrobial peptides (AMPs), particularly bacteriocins, offer a promising alternative to traditional antibiotics because they have naturally evolved to target fundamental vulnerabilities in competing bacteria, from cell envelopes to essential intracellular machinery, making it difficult for pathogens to develop resistance. The exponential growth of genomic and metagenomic sequencing has populated global databases with billions of natural amino acid sequences. This pool of natural data has already produced globally used bacteriocins like Nisin, alongside recent genome-mined breakthroughs such as Darobactin, Dynobactin, and Lariocidin. Many, however, remain locked within the unannotated "dark matter" of protein databases, with their function uncharacterized because they aren’t similar to known proteins. For example, the world's leading public resource of protein sequence and functional information, [UniProt](https://www.uniprot.org/), holds millions of sequences with unknown functionality, transferred directly from genome sequencing projects. To bridge this gap, Google developed [ProtNLM (Protein Natural Language Model)](https://www.uniprot.org/help/ProtNLM), a deep learning model trained to directly read the underlying evolutionary “grammar” of amino acid sequences. This allows the model to predict biological function directly from raw protein sequences, enriching millions of previously unnamed UniProt entries with high-confidence functional labels. Scientific discovery is rarely a single step process; finding viable candidate proteins requires human intuition and iteration to incorporate new empirical evidence. Working in Antigravity, the researcher and AI agent begin by setting a focused biological goal: find and computationally validate novel AMPs in *Streptococcus pseudopneumoniae*. During an interactive, multi-turn session the researcher steers the scientific strategy, deciding which questions to ask and what evidence to collect, while the agent handles the heavy lifting of cross-database orchestration, structural analysis, and literature synthesis. The candidate we uncover in this walkthrough represents a biologically plausible lead backed by structural and genomic evidence but it still requires wet lab validation. What makes this meaningful is how dramatically it lowers the barrier to everyday biological workflows, turning days of fragmented database triage into a single collaborative session. While this session focused on a narrow amino acid window in *Streptococcus pseudopneumoniae*, researchers can take the conversation in any direction, such as layering in new filtering criteria, exploring different sequence lengths, or targeting other priority pathogens. By broadening the search from human commensals to environmental microbes and beyond, scientists can guide the agent across UniProt to discover novel AMP candidates at scale. A protein's 3D structure can help us understand its biological function. Examining a candidate’s predicted fold in space is key. To provide this end-to-end workflow in a single workspace, we are introducing the **Biomolecular Structure Viewer**, a 3D visualization tool embedded directly into Antigravity. The researcher and agent work together over a shared visual state. As the agent retrieves structures from AlphaFold Database or the scientist’s own predictions, it can dynamically drive the viewer. For instance, it can highlight key functional regions to visually explain its hypotheses. Crucially, this awareness flows both ways: actions like highlighting specific residues or pinning notes directly onto a binding pocket are synchronised back to the agent. A researcher can simply mark a region of interest in 3D space and ask, for example, "How might this β-strand affect Lipid II binding?", allowing the agent to immediately reason over the exact structural context the scientist is looking at. Historically, uncovering insights like those in the workflow demonstrated above required fragmented and specialised software: writing custom scripts to query databases, manually exporting files to 3D modelling programs, managing disconnected genomic tools and working with multiple experts to design the wet lab assays. Because wet-lab validation remains resource-intensive, narrowing the search space to only the highest-confidence leads upfront is critical. Using Google Antigravity allows researchers to navigate the vast biological search space efficiently while leveraging accurate protein function predictions from a domain-specific model. Google Antigravity executes this end-to-end scientific reasoning in one place. This allows scientists to spend more time evaluating high-quality hypotheses, running experiments in the lab and solving research challenges. We would like to acknowledge the contributions of the following team members: Maria Gabriela Cardoso, Victoria Johnston, Lexi Walker, Gena Hong, James King, Richard Green, Filip Radlinski, Artem Sobolev, Michael V. LeVine, Ruijie K. Zhang, Zachary Wu, Luca Schulz, Amy Danson, Tristan Oliver Kwan, Ryan Krueger, Oleg Kovalevskiy, Doug Fritz, Yunhan Xu, Sebastian Nowozin