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Does reductionism, in the era of machine learning and now interpretable AI, facilitate or hinder scientific insight? The protein ribbon diagram, as a means of visual reductionism, is a case in point.
The authors have declared that no competing interests exist.
Figures
Fig 1. Cartoon ribbon diagrams as a…
Fig 1. Cartoon ribbon diagrams as a blessing and a curse.
The earliest era of…
Fig 1. Cartoon ribbon diagrams as a blessing and a curse.
The earliest era of structural biology made clear the necessity of molecular visualization for even small proteins, such as the 62-amino acid snake venom toxin shown here (PDB ID 3EBX). In this process, (a) atomic coordinates are visually rendered on a computer display as (b) lines, “sticks,” spheres, etc., thereby creating a representation of the protein’s 3D structure. Though useful for detailed, atomic-scale analyses, e.g., of enzyme mechanisms, such renditions are too visually cluttered and complicated (incomprehensible, essentially) to enable one to grasp a protein’s overall architecture and topology. For that purpose, (c) ribbon diagrams are a blessing: these diagrams are powerful abstractions of a single protein entity, but do they (d) mask other features and relationships.
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al.. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–589. doi: 10.1038/s41586-021-03819-2
-
DOI
-
PMC
-
PubMed
Moult J, Pedersen JT, Judson R, Fidelis K. A large-scale experiment to assess protein structure prediction methods. Proteins. 1995;23:ii–iv. doi: 10.1002/prot.340230303
-
DOI
-
PubMed
Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, et al.. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577:706–710. doi: 10.1038/s41586-019-1923-7
-
DOI
-
PubMed
Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al.. Accurate prediction of protein structures and interactions using a three-track neural network. Science (New York, NY). 2021;373:871–876. doi: 10.1126/science.abj8754
-
DOI
-
PMC
-
PubMed