Nucleotide read, frame +1
Illustrative example, not a real result.
AbProSAP
Turn nucleotide reads into protein-level frequency summaries.
Rule-based analysis. No machine learning.

AI-assisted computational platforms for sequence analysis, molecular interactions, antibody engineering, and structure-guided discovery.
Example sequence
Start with data you provide: nucleotide reads, amino-acid sequences or a PDB or mmCIF structure.
Conceptual illustration. Nothing here is a real analysis or result.
Three platforms you can run, each with its own focus. The toolkits further down this page are capabilities we apply within a project rather than tools you open. Open one to see what it takes in and what it returns. Previews use synthetic examples.
Nucleotide read, frame +1
Illustrative example, not a real result.
Turn nucleotide reads into protein-level frequency summaries.
Rule-based analysis. No machine learning.
Position 3 selected
Q at 3Illustrative example, not a real result.
Explore and rank candidate amino-acid substitutions.
Heuristic scoring (BLOSUM62 and rule-based descriptors). Not a trained model.
Atom pairs across a chain interface
Illustrative example, not a real result.
Map residue-level contacts at a protein interface.
Rule-based geometry and chemistry. No machine learning.
Real coordinates (PDB 3OGO): a nanobody on its target, an experimentally determined complex and not a designed molecule. Drag to turn.
Epitope-targeted generation of new binders where no starting antibody exists.
Generative design, antibody-aware redesign, then complex prediction. Its output is a computational proposal, not a confirmed binder.
Schematic, not a result.
Biomarker identification, MD simulations, interaction analysis, docking, and germline analysis.
Want to see the platform in action, or talk through a program that needs it?
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