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Dr. Son's Personalised mRNA Cancer Vaccine, First Steps

From 200 gigabytes
to 0.5 millilitres

Read one dog's tumour and blood whole, and you get 200 gigabytes. Five months later that file had become 0.5 millilitres of clear liquid in a syringe.
These 20 lessons take apart, in the order the story tells them, every term wedged in between โ€” somatic variant calling, Mutect2, VEP, RMSD, OpenMM, NetMHCpan, sequence design, IVT and RIN, SM-102, LNP, DLS, endosomal escape. Python runs right inside this page. There is nothing to install.

๐Ÿ Python๐Ÿงฌ somatic variants๐Ÿงฉ structure prediction ๐ŸŽฏ neoantigens๐Ÿ’Š LNP๐Ÿ”ฌ endosomal escape๐Ÿ‡ฌ๐Ÿ‡ง English
Start lesson 1 โ†’

How you'll learn

๐Ÿ“–In the story's own order

The order is the novel's, not a textbook's. Read lesson 3 and chapter 6 opens up; read lesson 16 and chapter 11 does.

๐Ÿ”ขWith the story's own numbers

VAF 0.847, RMSD 3.87 ร…, IC50 87 nM, RIN 8.4, apparent pKa 6.41, 88 nm. Not one invented value. We work out why each number is the size it is.

๐ŸšงIncluding the batch that failed

The first LNP failed at 210 nm; the second passed every QC check and still failed inside the cell. You learn nothing from the batch that worked.

๐Ÿ› ๏ธLearning by handling it

Every lesson has a laboratory widget โ€” the VAF threshold, the DFG angle, the pKa slider, the flow-rate ratio โ€” turned by you, so you see the conclusion flip.

Full curriculum

01
๋‹ค์„ฏ ๋‹ฌ โ€” ์ด์•ผ๊ธฐ์˜ ์ง€๋„
ใ€Ž์ฝ”๋”ฉ ํฌ ๋กœ์ง€ใ€ ํ”„๋กค๋กœ๊ทธ~์ œ15์žฅ ์ „์ฒด
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02
91๋‚˜๋…ธ๋ฏธํ„ฐ โ€” ํฌ๊ธฐ์˜ ์ž๋ฅผ ๋จผ์ € ๋งŒ๋“ ๋‹ค
์ œ11์žฅ ใ€Œ91๋‚˜๋…ธ๋ฏธํ„ฐ. PDI 0.09ใ€
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03
200๊ธฐ๊ฐ€๋ฐ”์ดํŠธ โ€” ์กฐ๊ฐ์„ ์ œ์ž๋ฆฌ์— ๋†“๋Š”๋‹ค
์ œ6์žฅ ใ€Œ200๊ธฐ๊ฐ€๋ฐ”์ดํŠธ์˜ ์„ธ๊ณ„ใ€
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04
์ฒด์„ธํฌ ๋ณ€์ด ๋ถ€๋ฅด๊ธฐ โ€” ๋‘ ํŒŒ์ผ์˜ ๋บ„์…ˆ
์ œ6์žฅ ใ€Œ์†Œ๋งคํ‹ฑ ๋ฐฐ๋ฆฌ์–ธํŠธ ์ฝœ๋งใ€ ยท GATK Mutect2
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05
VEP โ€” ๋ณ€์ด๋ฅผ ๋‹จ๋ฐฑ์งˆ์˜ ๋ง๋กœ ์˜ฎ๊ธด๋‹ค
์ œ6์žฅ ใ€ŒVEP(Variant Effect Predictor)๋กœ ๊ฐ ๋ณ€์ด์˜ ๊ธฐ๋Šฅ์  ์˜ํ–ฅ์„ ์ฃผ์„ ๋‹ฌ์•˜๋‹คใ€
โœ“
06
4,847๊ฐœ์—์„œ ์…‹์œผ๋กœ โ€” ์šด์ „์ž์™€ ์Šน๊ฐ
์ œ6์žฅ ใ€ŒKIT p.Asp816Val. VAF 0.847ใ€
โœ“
07
AlphaFold โ€” ์„œ์—ด์—์„œ ์ขŒํ‘œ๋กœ
์ œ7์žฅ ใ€Œ๋ณด์ด์ง€ ์•Š๋Š” ๊ฒƒ์„ ๋ณด๋‹คใ€ ยท ColabFold, pLDDT 87.4
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08
RMSD โ€” ๋‘ ๊ตฌ์กฐ๊ฐ€ ์–ผ๋งˆ๋‚˜ ๋‹ค๋ฅธ๊ฐ€
์ œ7์žฅ ใ€ŒRMSD 0.41 ์˜น์ŠคํŠธ๋กฌใ€ / ํ™œ์„ฑํ™” ๋ฃจํ”„ 3.87 ร…
โœ“
09
๊ณ ์žฅ๋‚œ ์Šค์œ„์น˜ โ€” DFG-in ๊ณผ DFG-out
์ œ7์žฅ ใ€Œ์•ผ์ƒํ˜• 142.3๋„, ๋ณ€์ดํ˜• 67.8๋„ใ€
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10
ํ‘œ๋ฉด ๋…ธ์ถœ โ€” ๋ฌปํžŒ ๊ฒƒ์€ ๋ณด์ด์ง€ ์•Š๋Š”๋‹ค
์ œ7์žฅ ยท DSSP ๋กœ ์žฐ RSA 0.83
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11
OpenMM โ€” ์‚ฌ์ง„ ํ•œ ์žฅ์—์„œ 100๋‚˜๋…ธ์ดˆ์งœ๋ฆฌ ์˜์ƒ์œผ๋กœ
์ œ7์žฅ ใ€Œ37๋„์”จ, ์ƒ๋ฆฌ์  ์กฐ๊ฑด์—์„œ 100๋‚˜๋…ธ์ดˆ. DFG-in 94.3%ใ€
โœ“
12
NetMHCpan โ€” ์•„ํ™‰ ๊ธ€์ž์™€ ํ™ˆ
์ œ8์žฅ ใ€ŒSVDGQVYIP๊ฐ€ IC50 87๋‚˜๋…ธ๋ชฐ์ด์—ˆ๋‹คใ€ ยท pVAC-Seq, DLA-88
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13
mRNA ์„œ์—ด ์„ค๊ณ„ โ€” ์บก๋ถ€ํ„ฐ ํด๋ฆฌA๊นŒ์ง€
์ œ8์žฅ ใ€Œm7G ์บก๊ณผ IgE ์‹ ํ˜ธ ํŽฉํƒ€์ด๋“œ๋ฅผ ๋‹ฌ๊ณ , ๋’ค์—๋Š” MITD์™€ polyA ๊ผฌ๋ฆฌ๋ฅผใ€
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14
์ฝ”๋ˆ ์ตœ์ ํ™” โ€” ๊ฐ™์€ ๋‹จ๋ฐฑ์งˆ, ๋‹ค๋ฅธ ๋ฌธ์žฅ
์ œ8์žฅ ใ€ŒCAI๊ฐ€ 0.61์—์„œ 0.89๋กœ ์˜ฌ๋ผ๊ฐ”๋‹คใ€ ยท MFE โˆ’24.3 kcal/mol
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15
IVT โ€” ์‹œํ—˜๊ด€์—์„œ mRNA ๋ฅผ ๋งŒ๋“ ๋‹ค, ๊ทธ๋ฆฌ๊ณ  RIN ์œผ๋กœ ์žฐ๋‹ค
์ œ11์žฅ ใ€ŒIVT ๋ฐ˜์‘์œผ๋กœ mRNA๋ฅผ ํ•ฉ์„ฑํ–ˆ๋‹ค. ์ˆ˜์œจ 148 ยตg/mL. RIN 8.4ใ€
โœ“
16
SM-102 โ€” pKa ๊ฐ€ ์ „๋ถ€๋‹ค. ๋‹ค๋งŒ ์–ด๋А pKa ์ธ๊ฐ€
์ œ11์žฅ ใ€ŒSM-102. pKa 6.68ใ€ โ†’ ๊ฒ‰๋ณด๊ธฐ pKa 6.41
โœ“
17
LNP ์ œ์กฐ โ€” ๋„ค ๊ฐ€์ง€ ์ง€์งˆ๊ณผ ๋งˆ์ดํฌ๋กœ์œ ์ฒด ํ˜ผํ•ฉ
์ œ9์žฅ ใ€ŒDLin-MC3-DMA ์ด์˜จํ™” ์ง€์งˆ, DSPC, ์ฝœ๋ ˆ์Šคํ…Œ๋กค, PEG-DMG. ์ด ๋„ค ๊ฐ€์ง€๋ฅผโ€ฆใ€
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18
DLS โ€” 91 ๋‚˜๋…ธ๋ฏธํ„ฐ๋ฅผ ์–ด๋–ป๊ฒŒ ์Ÿ€๋‚˜
์ œ9์žฅยท์ œ11์žฅ ใ€Œ210๋‚˜๋…ธ๋ฏธํ„ฐใ€ โ†’ ใ€Œ91๋‚˜๋…ธ๋ฏธํ„ฐ. PDI 0.09ใ€ โ†’ ใ€Œ88๋‚˜๋…ธ๋ฏธํ„ฐใ€
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19
๋ชธ ์•ˆ์—์„œ โ€” ์ฃผ์‚ฌ์—์„œ ์„ธํฌ์ž๋ฉธ์‚ฌ๊นŒ์ง€
์ œ12์žฅ ใ€Œํด๋ผ์ด๋งฅ์Šค: ์—”๋„์†œ์˜ ํƒˆ์ถœใ€ ยท ์ œ13์žฅ ใ€Œํˆฌ์—ฌใ€
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20
๋งํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ๊ณผ ๋งํ•  ์ˆ˜ ์—†๋Š” ๊ฒƒ
์ œ14์žฅยท์ œ15์žฅยท์ž‘๊ฐ€ ํ›„๊ธฐ
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