PyTMs: a useful PyMOL plugin for modeling common post-translational modifications

Abstract

Background: Post-translational modifications (PTMs) constitute a major aspect of protein biology, particularly signaling events. Conversely, several different pathophysiological PTMs are hallmarks of oxidative imbalance or inflammatory states and are strongly associated with pathogenesis of autoimmune diseases or cancers. Accordingly, it is of interest to assess both the biological and structural effects of modification. For the latter, computer-based modeling offers an attractive option. We thus identified the need for easily applicable modeling options for PTMs.

Results: We developed PyTMs, a plugin implemented with the commonly used visualization software PyMOL. PyTMs enables users to introduce a set of common PTMs into protein/peptide models and can be used to address research questions related to PTMs. Ten types of modification are currently supported, including acetylation, carbamylation, citrullination, cysteine oxidation, malondialdehyde adducts, methionine oxidation, methylation, nitration, proline hydroxylation and phosphorylation. Furthermore, advanced settings integrate the pre-selection of surface-exposed atoms, define stereochemical alternatives and allow for basic structure optimization of the newly modified residues.

Conclusion: PyTMs is a useful, user-friendly modelling plugin for PyMOL. Advantages of PyTMs include standardized generation of PTMs, rapid time-to-result and facilitated user control. Although modeling cannot substitute for conventional structure determination it constitutes a convenient tool that allows uncomplicated exploration of potential implications prior to experimental investments and basic explanation of experimental data. PyTMs is freely available as part of the PyMOL script repository project on GitHub and will further evolve. Graphical Abstract PyTMs is a useful PyMOL plugin for modeling common post-translational modifications.

Graphical Abstract

PyTMs is a useful PyMOL plugin for modeling common post-translational modifications.

Figure 1

Overview. This figure gives an overview over all currently covered PTMs that can be introduced in PyMOL using PyTMs. Top: A table of the possible modifications in alphabetical order. PTM = post-translational modification. The columns from left to right contain: name of modification, the PyTMs command that is used to introduce it in the PyMOL API (when not using the GUI menu), a custom property selector name through which the modification can be selected in PyMOL (requires incentive PyMOL version 1.7 or higher), the target amino acid, information on whether N-termini can be modified using the respective function (*= modifiable, excluding Proline), a summary of charge effects, a group indicator (see below) and lastly selected references. Bottom: A depiction of representative modifications from all classes, grouped by amino acid and modification type. Note that the individual panels A-J correspond to the ‘group below’ column of the above table. All residues are aligned by the amino acid head, the terminal carboxyl group facing towards the viewer. The base is colored blue and the PTMs in red. Hydrogens are omitted. Groups are labeled as in the table above. For each category the displayed and total number of variants are indicated. The respective defaults for categories are underlined. Configurational diastereomers are indicated with the R or S nomenclature.

Figure 2

Example – inhibition of HPR1 by nitration. This figure is an application example of using PyTMs in the setting of enzyme inhibition. Briefly, the model of hydroxypyruvate reductase 1 from Arabidopsis thaliana was nitrated at three specific Tyrosine residues using PyTMS as described in the implementation section. This enzyme has previously been demonstrated to be nitrated at these residues and thereby functionally inhibited. NIY = Nitrotyrosine. A) An overview of the nitrated enzyme monomer is depicted in secondary structure cartoon representation. Additionally, the locations of (Nitro-)Tyrosine residues have been highlighted in stick representation. The nitro groups in residues 97, 108 and 198 are colored red. The co-factor (NADPH) in stick representation is colored blue. B + C) A close-up view of the co-factor (NADPH) binding site in the enzyme. B: native; C: nitrated. Left: Representation as cartoon with the (nitrated) TYR198. Steric clashes based on vdW overlap are represented by the colored discs. The increasing size and redness of the discs correlate with stronger vdW strain. The native enzyme does not have significant steric clashes. The nitration, however, introduces significant steric clashing with the co-factor and occludes the binding site. Right: identical view as in the left image, but depicted as surface with the extent of the nitration colored red. In conclusion, the denied binding of co-factor, mainly due to nitration at TYR198, is the structural basis for impaired enzyme activity.

Figure 3

Example – oxidation of the hERG potassium channel. This figure is an application example of using PyTMs in conjunction with SWISS modeling, introducing multiple modifications and demonstrating the possibility of modeling stereochemical variants. As the structure of hERG is only partially resolved, a complete model of the intracellular C-terminal domain of hERG1 (residues 667–865) was generated as described in the implementation using SWISS modeling. Using PyTMs, this model was then doubly modified by cysteine oxidation and methionine oxidations. Selected residues are displayed as spheres. Methionine oxidation was performed on object copies introducing either the configurational Methionine sulfoxide R-isomer (red oxygen) or the S-isomer (yellow oxygen). Note that these configurational isomers are superimposed to highlight the differential positioning and do not coexist on the same residue. Cysteine oxidation is not racemic (orange oxygen). Oxidation of hERG1, especially at CYS723, has been previously demonstrated to result in an accelerated deactivation of the associated ion channel. A) An overview of a homo-tetramer of hERG1 intracellular C-terminal domains, as viewed from above i.e. from the cell membrane or pore. The equivalent monomers are colored either blue or light gray for better distinction. The viewing positions for C) and D) are indicated. B) This view corresponds to A, but the complex is viewed from the side. Selected residues have been labeled, as indicated. Note the critical cysteine 723 at the dimer interface. C) A close-up view of the critical oxidized CYS723 at the dimer interface viewed from outside the complex (cf. A). Note how the polar oxygen extends the range of the cysteine residue to interfere with the adjacent loop (ASP767 and THR768). Expectedly, a resulting increase in steric displacement can explain the previously reported increase in deactivation rate. D) A close-up view from inside the complex focusing on adjacent Methionine sulfoxide residues.

Figure 4

MAA adducts on the surface of BSA. This figure is an application example of using PyTMs to selectively modify only solvent-accessible residues. As described, a model of Bovine Serum Albumin (BSA) was used to sub-select surface atoms before introducing Malondialdehyde-acetaldehyde (MAA) adducts. The brown surface corresponds to the original unmodified BSA. Both unmodified Lysines (43/118) and MAA-Lysines (75/118) are depicted in stick representation, but colored black or red, respectively. A) An overview of MAA-modified BSA with opaque surface. Note the surface decoration by MAA adducts (red sticks). B) An identical image with transparent surface. Note how the inaccessible/unmodified Lysines (black sticks without adduct) are likely to be shielded from modification within the protein’s interior volume. Additional MAA-modified-Lysines from the protein’s distal surface are also visible.

Figure 5

MHC–related applications. In this figure we tested the applicability of PyTMs with respect to altered peptide ligands (APLs). As described in the implementation, we started with the crystal structure of a native gp34 LCMV epitope complexed within the mouse MHC class I molecule H-2K^b. We introduced nitration into the p3TYR residue and refined the structure locally to resolve resulting clashes. The results were aligned and compared to the experimentally determined crystal structure. A) Orientation of the native gp34 in the resolved crystal structure [PDB: 3ROO]. The view is focused on the peptide-binding cleft. B) The refined model of a nitrated gp34 APL derived from the same crystal structure. Alternative backbone-dependent rotamers for GLU152 (steric displacement) and TYR116 (hydrogen bonding) have been chosen to accommodate this APL. C) The aligned gp34 APL and MHC pocket from the experimentally resolved crystal structure [PDB: 3ROL]. The essential adaptations are surprisingly similar. We therefore conclude that modeling APLs can be a valid predictive approach. However, the ligation of this APL induces more pronounced and global conformational changes that cannot be accounted for by local refinement. D) Relative positioning of the aligned peptides. Black and blue: Two nitrated gp34 epitopes according to modeling which correspond to 180° rotamers of each other. The first variant (model 1, black) clashed significantly with the helix of the MHC and TYR159, a feature that may contribute to the reduced affinity of the nitrated epitope (complete model not shown). The alternatively positioned model 2 was used for the modeling above and fits significantly better, especially after local refinement. Note how the orientation of this APL is essentially identical to that of the experimentally resolved variant (green).