Regulatory DNA design
Generate and optimize enhancers, promoters, transcription-factor response elements, and noncoding regulatory regions for target activity and reduced off-target signal.
ENHANCERSPROMOTERSTF MOTIFS
Proto-enabled DNA & RNA design services
Design nucleic acids around function, not guesswork.
We translate biological objectives into multi-objective computational design campaigns—generating, optimizing, and ranking DNA and RNA candidates for focused experimental testing.
Regulatory DNARNA splicingUTR controlMulti-model scoringReproducible programs
Candidate architecture
Optimized
Variable enhancer · 500 bp
Promoter · 100 bp
Intron · 301 bp
3′ UTR switch · 400 bp
ACGGTACCTGAGTCTAGGCT...GTAGCTAACGTTGACCTGAA...CCTGAGGTAAGTCCAG...
Target-cell regulatory signal↑ 3.8×
Off-target predicted activity↓ 71%
Define variables
Generate
Score & optimize
Rank candidates
Sequence-to-function design
Splicing and post-transcriptional control
Multi-objective optimization
Reproducible design programs
Service portfolio
Computational design services across the nucleic acid stack.
Each engagement is scoped around a biological objective, an explicit design space, a set of predictive constraints, and a practical experimental handoff.
RNA splicing design
Design synthetic introns and splice-regulatory elements with target donor/acceptor usage, cell-context specificity, and explicit cryptic-splice risk analysis.
INTRONSSSUCRYPTIC SITES
UTR and RNA control
Optimize 5′/3′ UTR architecture, microRNA response elements, sequence composition, and post-transcriptional control logic for context-dependent expression.
3′ UTRmiRNARNA STABILITY
CRISPR and noncoding RNA systems
Build computational generation and filter cascades for guide, array, tracrRNA, and broader locus architectures, with sequence-quality and structural plausibility checks.
GUIDESARRAYSLOCUS FILTERING
Multi-layer regulatory cassettes
Coordinate enhancer, promoter, intron, and UTR design as a staged program so that several regulatory layers contribute complementary selectivity.
GENE CIRCUITSCELL SELECTIVITYSTAGED DESIGN
Custom Proto workflow engineering
Translate a research brief into a documented Proto program, select appropriate generators and constraints, run design sweeps, and deliver an auditable analysis package.
PROTO PROGRAMSMODEL INTEGRATIONREPRODUCIBILITY
Engagement workflow
From biological brief to an experiment-ready shortlist.
The program is designed around explicit trade-offs rather than a single prediction score. That makes the rationale, failure modes, and candidate selection criteria easier to inspect.
1
Scope the design objective
Define target and off-target contexts, mutable regions, fixed sequence context, success criteria, assay plan, and synthesis constraints.
2
Encode the design program
Map each sequence region to generators and constraints, select optimization stages, and establish positive, negative, and baseline controls.
3
Generate and optimize candidates
Run broad candidate generation followed by focused refinement using rejection sampling, MCMC, gradient-based search, or beam search where appropriate.
4
Stress-test the shortlist
Evaluate model agreement, sequence diversity, genomic or plasmid context robustness, motif liabilities, cryptic splice sites, and single-mutation sensitivity.
5
Deliver a decision package
Provide ranked sequences, per-constraint scorecards, design rationale, reproducible code or program files, and a recommended experimental panel.
Standard deliverables
Not just sequences—a traceable design decision.
Every candidate is accompanied by the context needed to decide what to synthesize, what to test, and what could fail.
Design package
✓
Ranked candidate libraryFASTA/CSV with sequence IDs and metadata
✓
Constraint score matrixPer-model and composite objective breakdown
✓
Context robustness reportFlank, locus, cell-type, or construct sensitivity
✓
Risk and liability checksMotifs, repeats, homopolymers, cryptic splice events
✓
Candidate diversity mapClustering to avoid redundant experimental picks
✓
Reproducible workflowProto program, configuration, and methods summary
Published platform evidence
Case studies that demonstrate the design pattern.
These examples are reported in the Proto preprint and are shown as evidence of platform capability—not as client results from this independent service concept.
32%of successfully assayed ProtoIntrons showed significant differential splicing in the intended direction.
Experimentally evaluated
Cell-line-specific synthetic introns
AlphaGenome and SpliceTransformer constraints were combined with MCMC optimization across multiple sequence contexts. Sixty-five candidates were screened, with hits recovered in three of four design directions.
Source: Merchant et al., 2026 preprint, Figure 3 and Results §2.3.
45 / 86designed σ70 promoters drove reporter expression more than tenfold above a no-promoter control.
Experimentally evaluated
Synthetic bacterial promoter design
Evo 2 generation, promoter-strength and motif constraints, rejection sampling, and MCMC refinement produced diverse promoters with preserved σ70 grammar. Seventy-one of 86 exceeded the activity of PLtetO1 in the reported assay.
Source: Merchant et al., 2026 preprint, Figure 4 and Results §2.4.
5 layersenhancer, promoter, intron, 3′ UTR off-switch, and an entry-bias component in one staged program.
Computational proof-of-concept
Cell-selective regulatory cassette
A published NSCLC-focused program coordinated several regulatory layers. Reported predictions included approximately 9× higher H3K27ac for the enhancer and 7.1× higher miRNA-mediated repression in healthy lung than A549 for the off-switch.
Source: Merchant et al., 2026 preprint, Figure 5 and Results §2.5. Not experimentally validated.
Interpretation: computational scores prioritize candidates and reduce search space; they do not guarantee biological function. Experimental validation remains essential.
Engagement models
Start with feasibility, then scale the design campaign.
Scopes below are intentionally modular and can be adapted to a single sequence element or a coordinated regulatory system.
Feasibility sprint
Determine whether the objective is computable with available models and define a credible campaign architecture.
- Objective and data review
- Model and constraint selection
- Small pilot run
- Go/no-go recommendation
Design campaign
Generate, optimize, stress-test, and rank a focused DNA or RNA candidate library.
- Custom Proto program
- Multi-stage optimization
- Cross-model and context analysis
- Experiment-ready shortlist
Integrated regulatory program
Coordinate multiple sequence elements and regulatory layers around a shared functional objective.
- Enhancer/promoter/intron/UTR design
- Stage-wise handoff and scoring
- System-level candidate selection
- Methods and reproducibility package
FAQ
What clients should know before starting.
What information is needed for an initial review?
A target function, the sequence region that may change, fixed flanking context, target and off-target biological contexts, preferred assay, sequence length limits, and any synthesis or IP constraints.
Does the service include wet-lab validation?
This landing page is structured around computational design and analysis. Wet-lab work can be scoped through a qualified partner, but computational predictions should never be presented as experimentally confirmed results.
Can existing sequences be optimized rather than designed de novo?
Yes. Local mutation and MCMC-style refinement are often appropriate when preserving most of an existing construct is important. De novo or autoregressive generation is more suitable when broader novelty is acceptable.
How are model errors and overfitting managed?
By separating optimization models from hold-out evaluation where possible, testing multiple sequence contexts, examining each constraint independently, retaining sequence diversity, and flagging cases in which predictors disagree.
Is this service affiliated with the Proto developers?
No. This is an independent service concept built around publicly available Proto workflows. Any commercial page should retain a clear non-affiliation statement unless a formal relationship exists.
