Protein Structure Description with ρ, θ and ϕ: A Case Study with Caenopore-5

Since its establishment in 1971, the Protein Data Bank (PDB) has been using Cartesian coordinate system (CCS) as the standard framework for protein structure description with x, y, z . Despite the interconvertibility of CCS and spherical coordinate systems (SCS, ρ, θ and ϕ), CCS remains to date the default and the only framework for protein structure description in PDB. Recent advances in protein structure prediction (e.g., AlphaFold) revolutionized the field by integrating deep learning algorithms with experimental structural data, achieving unprecedented accuracy of protein structure prediction and relying on Cartesian representation of protein structures to extract geometric features, such as inter-residue distances and dihedral angles, which are essential for building and visualizing protein structures with x, y, z . To this end, questions remain about the potential for further improvement in the performance of protein structure prediction, i.e., what is going to drive the next stage of its continued development with improved accuracy and efficiency. Therefore, this article introduces an alternative coordinate system for protein structure description and feature extraction. Using Caenopore-5 as an example, this article redefines protein backbone structures using atomic bonding networks (ABN) within the SCS framework (ABN-SCS), leading to the extraction of a set of spherical parameters (ρ, θ and ϕ) from the NMR ensemble of Caenopore-5, encompassing 477 covalent bonds and 80 peptide bonds within its backbone for each structural model in its NMR ensemble. Overall, this work offers an alternative spherical framework (ABN-SCS), i.e., a new language (ρ, θ, ϕ), for protein structure description and ABN-SCS-based extraction of spherical geometric features for protein structure prediction, aiming to contribute a little bit to its performance and application in drug discovery and design.