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Therapeutic Design

CRISPR & Protein Engineering

Design precision CRISPR therapeutics and novel biologics with structural validation. Guide RNA optimization, HDR template generation, and protein engineering.

Metrics
70% Pfam-hit Rate
⚡
Time
2-5 minutes
🎯
Impact
Design undruggable targets with validated structural confirmation
✂️CRISPR Intelligence

To create a world where CRISPR-based therapies are designed and validated with computational certainty, eliminating guesswork and accelerating the path to cures

CRISPR Intelligence: Advanced Therapeutic R&D Platform

The definitive therapeutic design platform. Evo2-powered guide RNA design with AlphaFold 3 structural validation (pLDDT ≥70). 100% pass rate on validation benchmarks. IND package generation and IP monetization workflow included.

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CRISPR Intelligence Journey

The transformation from speculative gene editing to predictable therapeutic design with AI-powered target validation and guide RNA optimization.

Traditional Approach

Current limitations and challenges

Step 1

Manual Target Validation

Researchers manually sift through databases and literature to validate potential gene targets, a process fraught with uncertainty and high failure rates.

Key Problems:
  • High failure rate of 60-70% due to manual validation overlooking critical genetic variants
  • Time consuming process taking weeks or months for single target validation
  • Limited understanding of off-target effects and safety profiles
  • No systematic approach to guide RNA design and optimization
Step 2

Speculative Guide RNA Design

Guide RNA sequences are designed using basic algorithms without comprehensive off-target analysis or efficacy prediction.

Key Problems:
  • Basic algorithms miss 40% of potential off-target sites
  • No efficacy prediction leads to 50% guide RNA failure rate
  • Limited understanding of sequence context and chromatin accessibility
  • Manual optimization process takes 2-3 weeks per target
Step 3

Trial and Error Experiments

Extensive experimental validation required due to lack of predictive models, leading to resource waste and delayed timelines.

Key Problems:
  • Experimental validation required for every guide RNA design
  • High resource consumption with 70% experimental failure rate
  • Delayed project timelines by 4-6 weeks per target
  • Limited scalability for multiple target validation
Step 4

Unpredictable Outcomes

Without predictive models, CRISPR experiments yield unpredictable results, making therapeutic development risky and inefficient.

Key Problems:
  • Unpredictable editing efficiency across different cell types
  • High variability in off-target effects between experiments
  • Limited understanding of repair pathway preferences
  • Therapeutic development delayed by 6-12 months due to unpredictability

In-Silico Approach

How we transform the process

Step 1

Biological Target Validation

CrisPRO analyzes potential CRISPR targets to understand variant impact and functional consequences before guide design.

Solutions:
  • Variant impact analysis predicts how edits will affect gene function
  • Biological reasoning explains which targets are most likely to have therapeutic effect
  • Functional analysis identifies targets with clear disease-driving mechanisms
  • De-risked pipeline by validating biological rationale before experimental work
Step 2

Biology-Informed Guide RNA Design

CrisPRO designs guide RNAs considering sequence context, chromatin accessibility, and off-target potential.

Solutions:
  • Sequence analysis identifies optimal cutting sites within target regions
  • Chromatin accessibility analysis predicts which guides can access their targets
  • Off-target prediction identifies potential unintended editing sites
  • Biological context informs guide selection for therapeutic outcomes
Step 3

Mechanism-Based Experiment Design

CrisPRO helps design experiments by predicting biological outcomes based on editing mechanisms and repair pathways.

Solutions:
  • Repair pathway analysis predicts how cells will respond to DNA breaks
  • Editing efficiency predictions based on sequence context and chromatin state
  • Biological reasoning guides experimental design for therapeutic goals
  • Mechanism-based predictions inform which experiments are most informative
Step 4

Biology-Driven Therapeutic Design

CrisPRO connects editing outcomes to therapeutic mechanisms, enabling more predictable therapeutic development.

Solutions:
  • Biological analysis predicts how edits will affect disease pathways
  • Mechanism-based reasoning explains therapeutic rationale
  • Repair pathway preferences inform editing strategy for desired outcomes
  • Therapeutic development guided by understanding of biological mechanisms

API Endpoints

/generate_optimized_guide_rna
/generate_therapeutic_protein
/generate_repair_template