Ranomics
Protein structure visualization rendered on a high-resolution display in a computational biology lab
Technology platforms

Our technology platforms

Integrated computational and experimental platforms for protein engineering, cell display screening, and AI-driven protein design

Computational design

AI-driven protein design and binder discovery

Generative AI models for de novo protein design. No API wrappers. No shared batch queues.

Backbone generation

RFdiffusion

Structure-guided diffusion model generating de novo protein backbones conditioned on target hotspots. Backbone diversity is controlled via noise schedule and contig specification, enabling anything from unconditional monomer generation to hotspot-guided binder design.

Learn more →
Iterative binder design

BindCraft

Integrated binder design pipeline combining scaffold generation with iterative sequence optimization. BindCraft iterates between backbone generation, sequence design, and AlphaFold2 validation in a single loop, producing candidates that pass structural confidence filters before experimental screening.

Learn more →
All-atom diffusion model

Boltzgen

All-atom generative diffusion model (MIT Jameel Clinic) for de novo binder design, with native support for glycans, PTMs, and non-canonical residues. Designs validated end-to-end with Boltz-2.

Learn more →
Free tool

Epitope Scout

Automated surface epitope identification and scoring for de novo binder design. Upload a PDB, get ranked epitope patches. Free to use.

Try Epitope Scout →
Sequence design

ProteinMPNN

Message-passing neural network solving the inverse folding problem. Designs amino acid sequences that fold into generated backbones from RFdiffusion and Boltzgen.

Learn more →
Structure prediction

Structural validation

Boltz-2, ESMFold, and ColabFold (AlphaFold2) provide layered fold and complex validation. Confidence metrics filter candidates before synthesis.

Learn more →
Experimental screening

High-throughput display and characterization

Computationally designed candidates are validated experimentally using the same platforms we operate for traditional library campaigns.

Cell display

Yeast Surface Display

High-throughput screening of protein variants on yeast cell surfaces. FACS and MACS selection with tunable stringency across 2-4 sorting rounds. Our most established platform for antibody discovery, affinity maturation, and de novo binder validation.

Learn more →
Cell display

Mammalian Display

Display on mammalian cells for targets requiring native post-translational modifications, proper glycosylation, or mammalian folding chaperones. Candidates that pass mammalian display are more likely to retain function during downstream production, reducing late-stage attrition.

Learn more →
Variant characterization

Deep Mutational Scanning

Systematic measurement of variant fitness across thousands of single and combinatorial mutations. Comprehensive fitness landscapes for stability, expression, and function in a single experiment. Also applied to drug target biology, mapping how mutations in a target protein affect drug binding, resistance, and mechanism of action.

Learn more →
Evolution

Directed Evolution

Iterative rounds of random and focused mutagenesis followed by high-throughput selection. Evolve proteins for stability, activity, specificity, or expression without requiring structural information.

Learn more →
Library construction

Variant Library Construction

Custom combinatorial and saturation mutagenesis libraries. Error-prone PCR, NNK scanning, and computationally designed focused libraries cloned into display or expression vectors.

Learn more →
Hit validation

Small-Scale Expression & BLI

Top hits from display screening are expressed as soluble protein in small-scale cultures and characterized by biolayer interferometry. BLI provides real-time binding kinetics (kon, koff, KD) to confirm target engagement and rank candidates before scale-up.

Learn more →
Data analysis

NGS Analysis Pipeline

Next-generation sequencing pipeline for quantitative, reproducible hit calling and ranking. Enrichment scoring, clone frequency tracking across sorting rounds, and prioritized hit lists with statistical confidence metrics.

Learn more →
Pipeline

From target structure to validated hits

Computational design generates candidates. Experimental screening validates them. NGS analysis closes the loop. Every stage feeds data forward.

01
Computational
Design & predict
Target structure
PDB / AlphaFold / cryo-EM
Hotspot definition
Epitope masking & residue selection
Backbone generation
RFdiffusion + BindCraft + Boltzgen
Sequence design
ProteinMPNN / SolubleMPNN
Structural validation
Boltz-2 / ESMFold / ColabFold
In silico filters
Expressibility, solubility, novelty
02
Experimental
Screen & select
Gene synthesis
Filtered candidate pool
Library construction
Cloning into display vectors
Yeast / mammalian display
Surface expression & QC
MACS pre-enrichment
Magnetic bead depletion
FACS sorting
2-4 rounds, tunable stringency
Small-scale expression & BLI
Soluble protein, binding kinetics
03
Analysis
Characterize & deliver
NGS sequencing
Enrichment ratios & clone tracking
Hit calling & ranking
Statistical enrichment scoring
DMS fitness mapping
Comprehensive variant landscapes
Affinity characterization
Binding kinetics & specificity
Developability assessment
Expression, stability, aggregation
Prioritized hit list
Sequences + data package delivery

Ready to leverage our technology platforms?

Tell us about your protein engineering challenge. We will assess which platforms are the right fit and propose a program structure.

Start a project →