# Oligonucleotides > Design small interfering RNA and antisense oligonucleotide sequences against a transcript, and screen them for the failure modes specific to nucleic-acid drugs. Use this skill to tile a target transcript, apply positional and thermodynamic selection rules including duplex asymmetry and nearest-neighbour melting temperature, scan candidates for seed-region complementarity to off-target transcripts… - **Type:** Skill - **Install:** `agentstack add skill-k-dense-ai-drug-discovery-agent-skills-oligonucleotides` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [K-Dense-AI](https://agentstack.voostack.com/s/k-dense-ai) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [K-Dense-AI](https://github.com/K-Dense-AI) - **Source:** https://github.com/K-Dense-AI/drug-discovery-agent-skills/tree/main/skills/oligonucleotides - **Website:** www.k-dense.ai ## Install ```sh agentstack add skill-k-dense-ai-drug-discovery-agent-skills-oligonucleotides ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Oligonucleotide Therapeutics The modality that sidesteps the protein entirely. If a target has no druggable pocket, no extracellular epitope, and no ligandable cysteine, an siRNA or antisense oligonucleotide can still silence its transcript — and the design is sequence arithmetic rather than chemistry intuition. **No installation, no network, no key.** Sequence tiling, nearest-neighbour thermodynamics, and seed scanning are implemented in the standard library. Transcriptome-wide off-target scanning needs a FASTA that you supply. **Thermodynamics:** SantaLucia (1998) unified nearest-neighbour parameters. Read [references/sirna-and-aso-design.md](references/sirna-and-aso-design.md) before choosing a site, [references/chemical-modifications.md](references/chemical-modifications.md) before drawing a pattern, and [references/delivery-and-safety.md](references/delivery-and-safety.md) before committing to the modality — **that one is judgement, not syntax, and it is where programmes fail.** ## The three scripts | Script | Answers | |---|---| | `oligo_design.py` | Which sites, and are their thermodynamics right? | | `offtarget_scan.py` | What else will this silence? | | `chemistry_plan.py` | What modifications, and where? | ## Two mechanisms, two incompatible rule sets **siRNA** loads into Argonaute-2 and is cleaved by RISC in the **cytoplasm** — it needs an RNA-like duplex throughout. **Gapmer ASO** recruits **RNase H1**, works in the **nucleus**, and needs an unmodified DNA core. Two consequences. ASOs can target **introns and pre-mRNA**; siRNA cannot, because RISC only sees mature mRNA. And the chemistry is not interchangeable: a DNA gap in an siRNA breaks Argonaute loading, while fully modifying an ASO silently removes RNase H recruitment — the molecule binds its target beautifully and does nothing. ## Duplex asymmetry decides which strand is loaded The siRNA rule that matters most. RISC keeps the strand whose **5' end is less thermodynamically stable**. Get it backwards and RISC loads the sense strand, silences something else, and your molecule looks simply inactive — sending you to hunt for delivery problems that do not exist. ```bash python skills/oligonucleotides/scripts/oligo_design.py tile --sequence ACGT... --modality sirna ``` ``` position sense antisense gc tm_c asymmetry antisense_loaded seed passes flags 12 CGTCCAGATCGGATCCAAGTT AACTTGGATCCGATCTGGACG 0.524 73.5 3.2 true ACTTGGA true 10 TACGTCCAGATCGGATCCAAG CTTGGATCCGATCTGGACGTA 0.524 72.6 2.4 true TTGGATC false as_pos1_not_au ``` The thermodynamics are the SantaLucia 1998 unified parameters and reproduce the paper's worked example exactly — CGTTGA gives ΔH = −41.2 kcal/mol and ΔS = −115.4 cal/mol/K. **GC content is a window, not a direction.** Below ~30% the duplex is too weak to hybridise; above ~60% it is too stable for RISC to unwind. Optimising GC upward is a common silent error. ## Zero off-targets is not achievable Antisense positions **2–8** are the seed, and seed pairing with a 3' UTR gives microRNA-like repression with no full-length complementarity at all. A full-length aligner scores that as a non-hit, which is why BLAST is the wrong tool here. A 7-mer occurs often enough to hit hundreds of transcripts in any real transcriptome. The useful question is comparative: ```bash python skills/oligonucleotides/scripts/offtarget_scan.py seeds --antisense AACTTGG... --fasta tx.fa python skills/oligonucleotides/scripts/offtarget_scan.py contig --antisense AACTTGG... --fasta tx.fa ``` `contig` searches the other risk: RNase H cleaves on **partial** complementarity, so a contiguous 12–14 nt match elsewhere is a real gapmer hepatotoxicity liability. ## The gap must be at least eight DNA residues ```bash python skills/oligonucleotides/scripts/chemistry_plan.py gapmer --sequence GCTAGCTACGTAGCTAGCTA \ --wing moe --wing-length 5 ``` ``` # 5-10-5 gapmer, MOE wings # pattern: WWWWWddddddddddWWWWW # 10 nt DNA gap -- RNase H needs at least ~8 to cleave the heteroduplex # 1 CpG site(s) marked for 5-methylcytosine. Unmethylated CpG is a TLR9 agonist; this is not optional. ``` Every 2' modification blocks RNase H, which is the entire reason gapmers have an unmodified core. The script refuses to emit a short gap, because that failure is silent. **Phosphorothioate is the central trade-off.** It gives nuclease resistance *and* the plasma protein binding that drives hepatic uptake — and that same protein binding causes complement activation, thrombocytopenia, and injection-site reactions. The delivery and the toxicity are one mechanism. ## Delivery is the whole problem **Every approved siRNA targets a hepatic gene.** That is a fact about delivery, not about biology. GalNAc conjugation gives 10–30× potency into hepatocytes via ASGPR and nothing anywhere else. | Tissue | Status | |---|---| | Liver | solved — GalNAc, subcutaneous, multiple approvals | | CNS | works, intrathecal | | Eye | works, intravitreal | | Muscle, lung, tumour, elsewhere | unsolved | If the target tissue is not liver, CNS, or eye, say so before designing anything. ## Four ways this misleads 1. **Accessibility dominates and is not modelled here.** mRNA is folded and protein-coated; a thermodynamically perfect site inside stable secondary structure is inaccessible. Use ViennaRNA or SHAPE data, or tile densely and screen. 2. **The rules are necessary, nowhere near sufficient.** Published hit rates for rule-compliant designs run one in three to one in ten. 3. **A single designed molecule is not a deliverable.** Synthesise and screen 20–50. 4. **The essential control is a panel with different seeds.** If five sequences produce the phenotype it is on-target; if one does, it probably is not. Worth more than any prediction here. ## Composing with the rest of the bundle - `binding-site-analysis` → here: when a target has no druggable pocket, this is one of the remaining routes. - `target-safety` → before: knocking down a constrained gene carries the same warning as inhibiting one. - `degraders` → alongside: the other way to act on an "undruggable" target, at the protein level rather than the transcript. - `pkpd-translation` → after: oligonucleotide PK is unusual — tissue half-lives of weeks decouple plasma exposure from effect. ## Reporting results honestly Say which modality and why. Give the rules applied and note they are necessary, not sufficient. State that accessibility is not modelled. Report seed off-target counts comparatively, never as an absolute. Name the tissue and route, and if it is not liver, CNS, or eye, say plainly that delivery is unsolved. Recommend a panel and a seed-mismatch control — not a molecule. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [K-Dense-AI](https://github.com/K-Dense-AI) - **Source:** [K-Dense-AI/drug-discovery-agent-skills](https://github.com/K-Dense-AI/drug-discovery-agent-skills) - **License:** MIT - **Homepage:** www.k-dense.ai Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-k-dense-ai-drug-discovery-agent-skills-oligonucleotides - Seller: https://agentstack.voostack.com/s/k-dense-ai - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.