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Unambiguous discrimination of all 20 proteinogenic amino acids and their modifications by nanopore.

Kefan WangShanyu ZhangXiao ZhouXian YangXinyue LiYuqin WangPingping FanYunqi XiaoWen SunPan-Ke ZhangWenfei LiShuo Huang
Published in: Nature methods (2023)
Natural proteins are composed of 20 proteinogenic amino acids and their post-translational modifications (PTMs). However, due to the lack of a suitable nanopore sensor that can simultaneously discriminate between all 20 amino acids and their PTMs, direct sequencing of protein with nanopores has not yet been realized. Here, we present an engineered hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore containing a sole Ni 2+ modification. It enables full discrimination of all 20 proteinogenic amino acids and 4 representative modified amino acids, N ω ,N' ω -dimethyl-arginine (Me-R), O-acetyl-threonine (Ac-T), N 4 -(β-N-acetyl-D-glucosaminyl)-asparagine (GlcNAc-N) and O-phosphoserine (P-S). Assisted by machine learning, an accuracy of 98.6% was achieved. Amino acid supplement tablets and peptidase-digested amino acids from peptides were also analyzed using this strategy. This capacity for simultaneous discrimination of all 20 proteinogenic amino acids and their PTMs suggests the potential to achieve protein sequencing using this nanopore-based strategy.
Keyphrases
  • amino acid
  • single molecule
  • machine learning
  • solid state
  • mycobacterium tuberculosis
  • nitric oxide
  • artificial intelligence
  • risk assessment
  • deep learning
  • heavy metals
  • transition metal