--- name: oligonucleotides description: 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, and lay out a chemical modification pattern โ€” gapmer architecture, 2'-O-methyl and 2'-MOE wings, locked nucleic acid, and phosphorothioate placement. Also trigger on siRNA, antisense oligonucleotide, ASO, gapmer, RNase H, seed region, duplex asymmetry, 2'-MOE, locked nucleic acid, phosphorothioate, or GalNAc conjugate. license: MIT allowed-tools: Read Write Edit Bash compatibility: Requires Python 3.10+ only. Sequence tiling, nearest-neighbour thermodynamics, and seed-match scanning are implemented in the standard library, so there is no install and no network access. Transcriptome-wide off-target scanning needs a local FASTA file that you supply; no reference sequence is bundled. metadata: version: "1.0" skill-author: K-Dense Inc. openclaw: emoji: "๐Ÿงต" homepage: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822268/ hermes: category: research --- # 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 โ€” 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 โ€” **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** โ€” 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 โ€” 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 โ€” 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 โ€” CGTTGA gives ฮ”H = โˆ’41.2 kcal/mol and ฮ”S = โˆ’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 **2โ€“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 12โ€“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 โ€” 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 10โ€“30ร— potency into hepatocytes via ASGPR and nothing anywhere else. | Tissue | Status | |---|---| | Liver | solved โ€” 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 20โ€“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` โ†’ here: when a target has no druggable pocket, this is one of the remaining routes. - `target-safety` โ†’ before: knocking down a constrained gene carries the same warning as inhibiting one. - `degraders` โ†’ alongside: the other way to act on an "undruggable" target, at the protein level rather than the transcript. - `pkpd-translation` โ†’ after: oligonucleotide PK is unusual โ€” 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 โ€” not a molecule.