Advanced computational tools for protein analysis, structure analysis, and biomarker discovery

Our Tools

ProteomeDiscoverer AI

AdAI-enhanced protein identification and quantification

Structure Predictor

Deep learning-based protein structure prediction

BiomarkerMiner

Automated biomarker discovery and validation

MassSpecAnalyzer

Advanced mass spectrometry data processing

Technology Stack

  • TensorFlow and PyTorch

  • AlphaFold 2

  • Biopython

  • Scikit-learn

  • Apache Spark

  • PostgreSQL

  • Docker and Kubernetes

  • React and TypeScript

Public Databases We Integrate

Uniprot

Protein sequence and functional information

Protein Data Bank (PDB)

3D structural data of proteins

ProteomeXchange

Mass spectrometry proteomics data

Human Protein Atlas

Human protein expression data

PRIDE

Proteomics Identifications Database

Case Study: Cancer Biomarker Discovery in Clinical Trails

Challenge

A leading pharmaceutical company needed to identify novel protein biomarkers for early-stage cancer detection from thousands of patient samples. Traditional manual analysis would take months and risk missing subtle patterns.

Solution

We deployed our ProteomeDiscoverer AI and BiomarkerMiner tools to analyze mass spectrometry data from 5,000+ patient samples. Our AI models processed protein expression profiles, identified differential patterns, and validated candidate biomarkers against UniProt and PRIDE databases.

Challenge

  • 85% reduction in analysis time (from 6 months to 3 weeks)

  • 12 novel biomarkers identified with 92% sensitivity and 89% specificity

  • 3 biomarkers advanced to Phase II clinical trials

  • $2.4M saved in research and development costs