Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science

Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next. First proving their value in software engineering, coding agents now write, test, and ship production code. Scientific research can be more demanding and iterative. Researchers continually evaluate evidence, refine hypotheses, and run experiments that shape subsequent decisions. Even failed experiments can lead to unexpected insights.

Scientific problems also often require domain-specific tools, for example, to fold a protein or characterize a molecule. Orchestrating and operating these tools can be challenging. Similar packages may have vastly different environment requirements or APIs. A general-purpose agent might recognize that a task calls for protein folding or molecular docking, but not which model to run, how to format the request, or which parameters matter.

NVIDIA BioNeMo Agent Toolkit closes that gap. The toolkit packages more than a decade of NVIDIA BioNeMo life sciences models, libraries, and workflows into agent-callable skills for biology, chemistry, genomics, and drug discovery.

Built to run with any agent framework, it enables complex scientific workflows using specialized domain expertise. On internal benchmarks, BioNeMo skills raise task correctness from 60% to 100% and roughly double token efficiency.

This post uses Claude Science, Anthropic's AI workbench for scientific research, with the NVIDIA BioNeMo Agent Toolkit and NVIDIA NIM to run protein structure prediction with multiple-sequence alignment (MSA) using multiple folding models and compare results.

NVIDIA and Anthropic worked together to integrate the BioNeMo Agent Toolkit into Claude Science, enabling agents to discover, launch, and call BioNeMo NIM microservices directly.

Claude Science runs in various configurations based on where your GPU is located and your security requirements. This tutorial focuses on running the platform on a machine with a GPU. Check the product documentation for GPU compatibility information. It's also possible to run on a laptop and connect to a remote GPU using SSH, HPC, or cloud computing on Modal. For this tutorial, you need access to a workstation or cloud machine with an NVIDIA L40S GPU or NVIDIA H100 GPU and Claude Science installed. The machine needs about 700 GB of storage for this workflow: the msa-search NIM's UniRef30 database is ~490 GB (using the UniRef30-only profile instead of the full 1.4 TB set), and the Boltz-2 and OpenFold3 containers are 30–40 GB total.

By default, Claude Science runs in a sandbox. Running BioNeMo NIM microservices requires compute endpoints that expose local or remote GPU resources. In Claude Science, select Customize > Compute > NVIDIA BioNeMo NIM > Connect. From there, import the BioNeMo Agent Toolkit skills from GitHub, add an NVIDIA API key, and connect to local endpoints—Docker containers that use the host GPU.

After importing the skills, storing the API key, and configuring the local connection, start a new project and session. Then prompt Claude to create the required NIM microservice endpoints.

When prompted, select Approve endpoint for both models. Setting up these endpoints requires downloading containers for each microservice.

With the three NIM endpoints running, Claude Science orchestrates the structure-prediction workflow. To make the pipeline concrete, this tutorial examines the Seh1 protein and a predicted Mio-family partner from Paracoccidioides lutzii, a fungus that causes paracoccidioidomycosis. The example is motivated by Figure 4e of Han, Tsenkov, Venanzi et al., "AlphaFold Database expands to proteome-scale quaternary structures" (bioRxiv, DOI: 10.64898/2026.03.27.714458).

Seh1 and Mio-family proteins participate in conserved nutrient-sensing machinery, and structures from other organisms suggest that Mio can complete Seh1's otherwise open β-propeller, making this pair a useful test of MSA-supported complex prediction.

This tutorial uses a two-protein system from Paracoccidioides lutzii: the nuclear-pore protein Seh1 (C1GY11) and an uncharacterized proposed partner (C1HCX1). The example asks the structural question: How does the predicted structure of Seh1 differ when it is modeled alone versus with the proposed partner?

To investigate this question, Claude Science builds evolutionary context for the target sequences using MSA. It then passes those alignments to two independent folding models, preserving the inputs and outputs so the single-chain and two-chain predictions can be examined within each model.

Generate MSAs for single-chain and species-paired sequences with the GPU-accelerated MSA Search NIM.

Predict structure for a single chain and a two-chain complex with the OpenFold3 NIM.

Repeat the predictions with the Boltz-2 NIM and evaluate each model independently.

Figure 1. BioNeMo skills in Claude Science orchestrate MSA generation and structure prediction with MSA Search, OpenFold3, and Boltz-2 NIMs

The agent retrieves both protein sequences from UniProt and creates two types of alignment. It first creates an unpaired alignment for each protein to provide information about each chain's structure. It then creates a paired alignment by matching related versions of the two proteins found in the same species. If changes in one protein consistently correspond to changes in the other, that pattern can help the models predict where the proteins might interact.

In this run, the search returned 202 sequences for each protein. The agent recorded the source and sequence for Seh1 (384 residues) and C1HCX1 (976 residues), including checksums for verification. It didn't trim either sequence or provide structural templates, ligands, or other constraints.

OpenFold3 takes a molecule list with per-chain msa (unpaired) and, for complexes, paired_msa. The agent runs two conditions with the same Seh1 sequence: the monomer (chain A with its MSA) and the heteromer (chains A and B with their MSAs plus the paired MSA). Use mmCIF output, no templates, and store every returned sample and the confidence fields the service actually exposes.

Table 1. OpenFold3 confidence scores for Seh1 monomer and Seh1–C1HCX1 complex predictions using MSA input, mmCIF output, and no templates. Scores are reported as returned by the NIM

The response exposed confidence_score, complex_plddt_score, complex_pde_score, ptm_score, iptm_score (plus format, name, source); runtime_metrics was present but empty.

The monomer's iptm_score is 0 by construction, and OpenFold3's composite confidence_score weights the interface heavily — which is why a well-folded monomer (pTM 0.82, pLDDT 82) still scores low on the composite. Read confidence_score within OpenFold3, not across models.

The same two conditions run through Boltz-2 with the same chain IDs and no-template policy. One architectural difference matters: Boltz-2 has no separate paired_msa field. It takes one MSA per chain and pairs internally (optional top-level concatenate_msas), so each Boltz-2 chain received its per-chain A3M.

Boltz-2 generated full PAE outputs (write_full_pae=true).

Table 2. Boltz-2 confidence scores for Seh1 monomer and Seh1–C1HCX1 predictions using MSA input and no templates. Scores are reported as returned by the NIM; pLDDT uses a 0–1 scale

Boltz-2 returned a richer confidence set — confidence_scores, ptm_scores, iptm_scores, protein_iptm_scores, complex_plddt_scores, complex_iplddt_scores, complex_pde_scores, complex_ipde_scores, plus per-chain and pairwise arrays and full pae/pde matrices (up to 1360×1360 for the complex).

The one requested field the service left empty was runtime metadata (metrics: {}).

Because the workflow also ran single-sequence (no-MSA) predictions, the value of Stage 1 can be measured directly. For the heteromer, the interface pTM (iPTM), the metric that reports on the predicted contact between the two chains, collapses without an MSA in both mo