Tools · Computational Biology

MD Trajectory Analyzer

Analyze molecular dynamics trajectories: RMSD, radius of gyration, per-residue flexibility, protein–ligand hydrogen bonds, clustering, and a representative structure.

Updated October 1, 2026

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Prices, workflows, and method papers Open in the app

The MD Trajectory Analyzer turns a molecular dynamics trajectory into the numbers you usually want from it, using MDAnalysis. It reads the result of an OpenMM MD job directly, or any topology PDB and DCD trajectory you upload. It runs on CPU.

What it computes

Backbone RMSD over time, against the first analyzed frame or the minimized structure (reference): has the structure settled, or is it drifting?
Radius of gyration over time: compaction or unfolding.
Per-residue RMSF: which parts of the protein move most.
Protein–ligand hydrogen-bond occupancy: the share of frames in which each H-bond is present (when there is a ligand).
RMSD clustering into n_clusters groups, with a representative structure (and optionally one per cluster).

Inputs

Either the OpenMM MD result zip (archive), or a topology PDB plus a trajectory DCD. Options:

Field	Default	Meaning
`stride`	1	Analyze every n-th frame
`max_frames`	2000	Cap on analyzed frames (at most 5,000)
`reference`	`frame0`	RMSD reference: the first analyzed frame, or `minimized` (the topology's coordinates)
`rmsd_selection`	`protein and name CA`	Atoms for RMSD and clustering (MDAnalysis selection syntax)
`ligand_selection`	`resname LIG`	The ligand, for hydrogen bonds
`n_clusters`	5	Number of clusters

Uploaded files are limited to 25 MiB each; in a workflow, an OpenMM MD step's result passes straight in.

Outputs

File	Contents
`timeseries.csv`	RMSD and radius of gyration per frame
`rmsf.csv`	RMSF per residue
`hbond_occupancy.csv`	Protein–ligand hydrogen bonds and their occupancy
`clusters.csv`	Each frame's cluster
`representative.pdb`	The most representative frame (plus `ensemble/*.pdb` per cluster)
`analysis_summary.json`	Settings and summary values

Run the simulation with OpenMM MD; rescore poses with MM-GBSA Rescoring; profile interactions in a single structure with Interaction Profiler.

Run it from the API

Submit with Submit a job and the job_type below. Price it first with Estimate job reservation cost: submitting reserves that amount from your wallet, and the charge settles at the actual runtime.

MD Trajectory Analyzer `md-analyze`

Job type: md-analyze
Hardware: cpu (default)
Typical runtime: 30 min on CPU

Payload

Payload fields
Field	Type	Description
`archive`	`file_object`	OpenMM MD result zip (pipeline_summary.json + simulation/trajectory.dcd).
`topology`	`file_object`	Topology PDB when not supplying an OpenMM archive.
`trajectory`	`file_object`	DCD trajectory when not supplying an OpenMM archive.
`stride`	`integer`	Default: `1`Limits: `≥ 1, ≤ 5000`
`max_frames`	`integer`	Default: `2000`Limits: `≥ 1, ≤ 5000`
`reference`	`string`	Default: `"frame0"`One of: `"frame0", "minimized"`
`rmsd_selection`	`string`	Default: `"protein and name CA"`Limits: `min length 1`
`ligand_selection`	`string`	Default: `"resname LIG"`Limits: `min length 1`
`n_clusters`	`integer`	Default: `5`Limits: `≥ 1, ≤ 50`

File fields take {"name": "x.pdb", "content_b64": "…"} or a stored artifact, {"$artifact": {"id": "art-…", "port": "…"}}. Files are up to 25 MiB each.

Example

from cognichem_client import CogniChem

client = CogniChem.from_env()  # reads COGNICHEM_API_KEY
payload = {
    "topology": {
        "name": "topology.pdb",
        "content_b64": "<file contents, base64>",
    },
    "trajectory": {
        "name": "trajectory.dcd",
        "content_b64": "<file contents, base64>",
    },
    "stride": 1,
    "max_frames": 10,
    "reference": "frame0",
    "n_clusters": 2,
}

estimate = client.jobs.estimate(job_type="md-analyze", payload=payload, resource="cpu")
print(f"Reserves ${estimate.cost:.2f}")

job = client.jobs.submit(
    job_name="my-md-analyze-run",
    job_type="md-analyze",
    payload=payload,
    resource="cpu",
)
status = client.jobs.wait(job.process_id)
if status.status == "completed":
    client.jobs.result(job.process_id, save_path=".")

curl -X POST "https://api.cognichem.com/api/v1/jobs/submit" \
  -H "X-Api-Key: $COGNICHEM_API_KEY" \
  -H "Idempotency-Key: $(uuidgen)" \
  -H "Content-Type: application/json" \
  -d '{
  "job_name": "my-md-analyze-run",
  "job_type": "md-analyze",
  "payload": {
    "topology": {
      "name": "topology.pdb",
      "content_b64": "<file contents, base64>"
    },
    "trajectory": {
      "name": "trajectory.dcd",
      "content_b64": "<file contents, base64>"
    },
    "stride": 1,
    "max_frames": 10,
    "reference": "frame0",
    "n_clusters": 2
  },
  "resource": "cpu"
}'

{
  "topology": {
    "name": "topology.pdb",
    "content_b64": "<file contents, base64>"
  },
  "trajectory": {
    "name": "trajectory.dcd",
    "content_b64": "<file contents, base64>"
  },
  "stride": 1,
  "max_frames": 10,
  "reference": "frame0",
  "n_clusters": 2
}

Sample data from the job catalog; long values are shortened here. Each job_name must be unique among your jobs.

Workflow inputs

Archive · optionalZIP
Protein structure · optionalPDB

Workflow outputs

ArchiveZIP
StructuresPDB
Protein structurePDB
TableCSV