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AI Protein Design & De Novo Binder Discovery Services
As a specialized AI protein design CRO, we bridge the gap between in silico design and wet-lab reality. Our platform provides end-to-end computational antibody discovery services, transforming digital deisgns into validated leads
Computational Antibody Discovery & Design
Ranomics bridges the gap between in silico prediction and in vitro reality. Our platform utilizes state of the art algorithms to explore new design spaces and novel sequences, identifying binders and protein assets that are important for reaching your next milestone
De Novo Protein Design: Create entirely new protein folds tailored to bind specific epitopes with high specificity and stability.
AI-Driven Binder Optimization: Leverage machine learning to refine lead candidates for enhanced affinity, solubility, and manufacturability.
Computational Antibody Discovery: Utilize generative AI to design CDR loops that target "undruggable" pockets on complex membrane proteins and receptors.
High-Throughput Wet-Lab Validation: Every AI protein design is synthesized and validated using our yeast or mammalian display platforms to ensure empirical performance.
Why Choose AI-Driven Protein Design
The Limits of Traditional Discovery
Diversity - Limited to natural repertoires
Targeting - Random Epitope binding
Speed - 6 to 12 months
Optimization - Iterative "Trial and Error"
What Generative AI Design Changes
Diversity - Unlimited de novo design space
Targeting - Site Specific targeting
Speed - 4 to 5 weeks
Optimization - Data-driven predictive refinement
Our AI-Assisted Protein Discovery Process
Generative Modeling & In Silico Screening
We initiate every project with massive scale computational antibody discovery screens. By processing millions of potential sequences through AI models, we prioritize variants with the highest probability of successful folding and target engagement
Target preparation: You provide a target structure (PDB or AlphaFold model). We identify binding hotspots based on biophysics and biological parameters.
De novo design: Using state of the art models (RFdiffusion, Bindcraft and Boltzgen), Ranomics generates backbone scaffolds conditioned on target hotspots.
In silico filtering: Candidates are filtered by predicted binding confidence, structural plausibility, expressibility and manufacturability heuristics. Typical campaigns reduce 50,000 designs to 200 to 10,000 for testing
Our protein engineering services are built on a continuous feedback loop between computation and experimentation. We don't just predict, we verify using wet-lab experiments
Empirical Validation & Lead Refinement
Top tier designs are transitioned to our high-throughput wet lab. Using NGS guided yeast surface display, we capture real world binding data.
Synthesis and display: Filtered sequences are synthesized as a pooled library and display on yeast cell surfaces using Ranomics' established display platforms.
Screening and selection: FACS or MACS selection against labelled target. Stringency is tuned to your affinity threshold. Multiple selection rounds can be run to enrich for tight binders.
NGS hit calling: Sorted populations are sequenced by NGS. Enriched clones are ranked and delivered as a prioritized hit list with binding data and sequence-level resolution
Launch Your AI Design Project
Ready to harness the power of de novo protein design? Contact our scientific team today to discuss your project goals and learn how our computational antibody discovery platform can deliver the next breakthrough in your pipeline