SPyCi-PDB: A modular command-line interface for back-calculating experimental datatypes of protein structures.

Abstract

Structural determination of proteins has been a central scientific focus since the early 1960s (Dill et al., 2008) with technological advances facilitating experimental structures of stable, folded proteins by nuclear magnetic resonance (NMR) spectroscopy (Kanelis et al., 2001), X-ray crystallography (Smyth, 2000), and cryo-electron microscopy (Malhotra et al., 2019), as well as the recent computational prediction of structures (Baek et al., 2021; Jumper et al., 2021). Modeling intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs), however, remains challenging due to their highly dynamic nature and low propensity to form low energy folded structures (Mittag & Forman-Kay, 2007). Currently, approaches to model IDPs/IDRs generally start with initial pools of structures that sample potentially accessible conformations and then utilize experimental data to narrow the pool. One method to generate initial conformational ensembles of IDPs/IDRs uses sampling techniques such as in TraDES (Feldman & Hogue, 2000, 2001), Flexible-meccano (Ozenne et al., 2012), FastFloppyTail (Ferrie & Petersson, 2020), IDPConformerGenerator (Teixeira et al., 2022), and others (Estaña et al., 2019), that rely on the torsion angle distributions found in high-resolution folded protein structures deposited in the RCSB Protein Data Bank (Berman, 2000). Another more computationally expensive approach generates conformational ensembles using molecular dynamics (MD) simulations with different force-fields (Robustelli et al., 2018; Salvi et al., 2016).