In 1983, the IUPAC-IUB Joint Commission on Biochemical Nomenclature published recommendations for the nomenclature and symbolism for amino acids and peptides. This described a system involving three letter codes for amino acids. For example, adrenorphin is represented by H-Tyr-Gly-Gly-Phe-Met-Arg-Arg-Val-NH2. It also described how to name modified peptides, e.g. [Cys2]adrenorphin. In addition, IUPAC provide guidelines for the generation of biochemical IUPAC names, e.g. L-tyrosyl-glycyl-glycyl-L-phenylalanyl-L-methionyl-L-arginyl-L-arginyl-L-valinamide. Some of the advantages of these representations are their human-readability and familiarity to practitioners in the field, as evidenced by their appearance in papers, Wikipedia articles, and vendor catalogues. However, alternative biopolymer representations have been developed which are intended for computer consumption; these formats include HELM, InChI, PDB, SCSR, LINUCS, GlycoCT and WURCS. We discuss the challenges of bridging these two worlds – the world of human-readable representations and that of machine-readable ones. To do this fully we also need to bridge the worlds of all-atom representations and monomer-based representations. For example, consider the structure represented by this (machine-readable) SMILES string: CC(C)C[C@H](NC(=O)[C@H](CCCN)NC(=O)[C@H](CCCCNC(=O)[C@H](Cc1ccccc1)NC(=O)[C@@H](N)CCCCN)NC(=O)[C@H](Cc1ccccc1)NC(=O)[C@@H](N)CCCCN)C(=O)NCCCC[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CCCN)NC(=O)[C@H](CCCCNC(=O)[C@H](Cc1ccccc1)NC(=O)[C@@H](N)CCCCN)NC(=O)[C@H](Cc1ccccc1)NC(=O)[C@@H](N)CCCCN)C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCCCN)C(N)=O This may also be written using an IUPAC-like representation as: H-Lys-Phe-Lys(1)-Orn-Leu-Lys(2)-Lys-Leu-Lys-NH2.H-Lys-Phe-Lys(3)-Orn-Leu-(2).H-Lys-Phe-(3).H-Lys-Phe-(1) And as HELM: PEPTIDE1{K.F.K.[Orn].L.K.K.L.K.[am]}|PEPTIDE2{K.F.K.[Orn].L}|PEPTIDE3{K.F}|PEPTIDE4{K.F}$PEPTIDE1,PEPTIDE2,6:R3-5:R2|PEPTIDE2,PEPTIDE3,3:R3-2:R2|PEPTIDE1,PEPTIDE4,3:R3-2:R2$$$ We discuss strengths and limitations of these formats, issues such as generating canonical representations (for duplicate detection), the handling of mixed biopolymers (e.g. glycoproteins, glycosylated nucleotides, fatty acid conjugated peptides), and the generation of meaningful similarity measures in the context of biopolymers.

Building a bridge between human-readable and machine-readable representations of biopolymers

256th National Meeting (2018)

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