PyMOL Script Research Articles

A Semester-Long Learning Path Teaching Computational Skills via Molecular Graphics in PyMOL

ABSTRACT Structural biology describes biological processes at the molecular level and is an integral part of undergraduate study programs in molecular biosciences. Students are often fascinated by the visualizations created by molecular graphics software, which allow them to see the molecular world for the first time. Today, molecular visualization and structural analysis do not require expensive high-end computers but can be performed on the students' own laptops and are therefore highly suited for active learning approaches. We have designed a semester-long learning path that integrates molecular graphics and structural analysis using PyMOL into an undergraduate course in biomolecular structure and function. Compared to stand-alone PyMOL introductions, the semester-long learning path allows for an improved pedagogical design. The path progressively introduces more advanced functions in relevant scientific contexts and allows for spaced repetition. Advanced analysis functions in PyMOL are available only via the command line, so the learning path also teaches basic scripting and serves as an accessible introduction to computational thinking because a few lines of code can produce stunning results. Student surveys carried out at the end of the course suggest that the learning path supported the ability to perform structural analysis to a high degree. Moreover, a simulated exam showed that almost all students were able to carry out basic visualization tasks using PyMOL scripts, while three-quarters could undertake advanced structural analysis after following the course. In summary, integration of molecular graphics software with teaching of structural biochemistry allows a hands-on approach to analyzing molecular mechanisms and introduces biologically oriented students to computational thinking.

The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes.

Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

Probing Universal Protein Dynamics Using Hydrogen-Deuterium Exchange Mass Spectrometry-Derived Residue-Level Gibbs Free Energy.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful technique to monitor protein intrinsic dynamics. The technique provides high-resolution information on how protein intrinsic dynamics are altered in response to biological signals, such as ligand binding, oligomerization, or allosteric networks. However, identification, interpretation, and visualization of such events from HDX-MS data sets is challenging as these data sets consist of many individual data points collected across peptides, time points, and experimental conditions. Here, we present PyHDX, an open-source Python package and webserver, that allows the user to batch extract the universal quantity Gibbs free energy at residue levels over multiple protein conditions and homologues. The output is directly visualized on a linear map or 3D structures or is exported as .csv files or PyMOL scripts.

ProteoVision: web server for advanced visualization of ribosomal proteins.

ProteoVision is a web server designed to explore protein structure and evolution through simultaneous visualization of multiple sequence alignments, topology diagrams and 3D structures. Starting with a multiple sequence alignment, ProteoVision computes conservation scores and a variety of physicochemical properties and simultaneously maps and visualizes alignments and other data on multiple levels of representation. The web server calculates and displays frequencies of amino acids. ProteoVision is optimized for ribosomal proteins but is applicable to analysis of any protein. ProteoVision handles internally generated and user uploaded alignments and connects them with a selected structure, found in the PDB or uploaded by the user. It can generate de novo topology diagrams from three-dimensional structures. All displayed data is interactive and can be saved in various formats as publication quality images or external datasets or PyMol Scripts. ProteoVision enables detailed study of protein fragments defined by Evolutionary Classification of protein Domains (ECOD) classification. ProteoVision is available at http://proteovision.chemistry.gatech.edu/.

JEDi: java essential dynamics inspector \u2014 a molecular trajectory analysis toolkit

BackgroundPrincipal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software.ResultsEmploying multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA.ConclusionJEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations.

Templates for writing PyMOL scripts.

PyMOL commands are used to exert exquisite control over the appearance of a molecular model. This control has made PyMOL popular for making images of protein structures for publications and presentations. However, many users have poor recall of the commands due to infrequent use of PyMOL. This poor recall hinders the writing of new code in scripts. One solution is to build the new script by using code fragments as templates for modular parts of the task at hand. The code fragments can be accessed from a library while writing the code from inside a text editor (e.g., Visual Studio Code, Vim, and Emacs). We developed a library of PyMOL code templates or snippets called pymolsnips to ease the writing of PyMOL code in scripts. We made pymolsnips available on GitHub in formats for 18 popular text editors. Most of the supported text editors are available for Mac, Windows, and Linux operating systems. The GitHub site includes animations that complement the instructions for installing the library for each text editor. We expect that the library will help many PyMOL users to be more productive when writing PyMOL script files.

The pymolsnips library to aid the writing of PyMOL scripts

The pymolsnips library to aid the writing of PyMOL scripts

SCOT: Rethinking the classification of secondary structure elements.

Secondary structure classification is one of the most important issues in structure-based analyses due to its impact on secondary structure prediction, structural alignment and protein visualization. There are still open challenges concerning helix and sheet assignments which are currently not addressed by a single multi-purpose software. We introduce SCOT (Secondary structure Classification On Turns) as a novel secondary structure element assignment software which supports the assignment of turns, right-handed α-, 310- and π-helices, left-handed α- and 310-helices, 2.27- and polyproline II helices, β-sheets and kinks. We demonstrate that the introduction of helix Purity values enables a clear differentiation between helix classes. SCOT's unique strengths are highlighted by comparing it to six state-of-the-art methods (DSSP, STRIDE, ASSP, SEGNO, DISICL and SHAFT). The assignment approaches were compared concerning geometric consistency, protein structure quality and flexibility dependency and their impact on secondary structure element-based structural alignments. We show that only SCOT's combination of hydrogen bonds, geometric criteria and dihedral angles enables robust assignments independent of the structure quality and flexibility. We demonstrate that this combination and the elaborate kink detection lead to SCOT's clear superiority for protein alignments. As the resulting helices and strands are provided in a PDB conform output format, they can immediately be used for structure alignment algorithms. Taken together, the application of our new method and the straight-forward visualization using the accompanying PyMOL scripts enable the comprehensive analysis of regular backbone geometries in proteins. https://this-group.rocks. Supplementary data are available at Bioinformatics online.

PrankWeb: a web server for ligand binding site prediction and visualization.

PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

ConfBuster: Open-Source Tools for Macrocycle Conformational Search and Analysis

Macrocycles are cyclic macromolecules that have gained an increased interest in drug development. To our knowledge, the current bioinformatics tools that are available to investigate and predict macrocycles 3D conformations are limited in their availability. In this paper, we introduce ConfBuster, a suite of tools written in Python with the goal of sampling the lower energy conformations of macrocycles. The suite also includes tools for the analysis and visualisation of the conformational search results. Coordinate sets of single molecules in MOL2 or PDB format are required as input, and a set of lower energy conformation coordinates is returned as output, as well as PyMOL script and graphics for results analysis. In addition to Python and the optional R programming languages with freely available packages, the tools require Open Babel and PyMOL to work properly. For several examples, ConfBuster found macrocycle conformations that are within few tenths of Å of the experimental structures in minutes. To our knowledge, this is the only open-source tools for macrocycle conformational search available to the scientific community

JED: a Java Essential Dynamics Program for comparative analysis of protein trajectories

BackgroundEssential Dynamics (ED) is a common application of principal component analysis (PCA) to extract biologically relevant motions from atomic trajectories of proteins. Covariance and correlation based PCA are two common approaches to determine PCA modes (eigenvectors) and their eigenvalues. Protein dynamics can be characterized in terms of Cartesian coordinates or internal distance pairs. In understanding protein dynamics, a comparison of trajectories taken from a set of proteins for similarity assessment provides insight into conserved mechanisms. Comprehensive software is needed to facilitate comparative-analysis with user-friendly features that are rooted in best practices from multivariate statistics.ResultsWe developed a Java based Essential Dynamics toolkit called JED to compare the ED from multiple protein trajectories. Trajectories from different simulations and different proteins can be pooled for comparative studies. JED implements Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) as options to construct covariance (Q) or correlation (R) matrices. Statistical methods are implemented for treating outliers, benchmarking sampling adequacy, characterizing the precision of Q and R, and reporting partial correlations. JED output results as text files that include transformed coordinates for aligned structures, several metrics that quantify protein mobility, PCA modes with their eigenvalues, and displacement vector (DV) projections onto the top principal modes. Pymol scripts together with PDB files allow movies of individual Q- and R-cPCA modes to be visualized, and the essential dynamics occurring within user-selected time scales. Subspaces defined by the top eigenvectors are compared using several statistical metrics to quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes can be generated for both cPCA and dpPCA.ConclusionsJED offers a convenient toolkit that encourages best practices in applying multivariate statistics methods to perform comparative studies of essential dynamics over multiple proteins. For each protein, Cartesian coordinates or internal distance pairs can be employed over the entire structure or user-selected parts to quantify similarity/differences in mobility and correlations in dynamics to develop insight into protein structure/function relationships.

PAGAL - Properties and corresponding graphics of alpha helical structures in proteins

Alpha helices (AH) are peptide fragments characterized by regular patterns of hydrogen bonding between the carbonyl oxygen and amino nitrogen of residues regularly spaced in sequence, resulting in spiral conformations. Their preponderance in protein structures underlines their importance. Interestingly, AHs are present in most anti-microbial peptides, although they might remain in random-coil conformations depending on the solvent dielectric. For example, the cecropin component of the chimeric anti-microbial protein designed previously by our group comprises of two AHs linked by a short stretch of random coil. These anti-microbial peptides are often amphipathic (quantified by a hydrophobic moment), aligning hydrophobic residues on one surface and charged residues on the others. In the current work, we reproduce previously described computational methods to compute the hydrophobic moment of AHs - and provide open access to the source code (PAGAL). We simultaneously generated input files for TikZ (a package for creating high resolution graphics programmatically) to obtain the Edmundson wheel and showing the direction and magnitude of the hydrophobic moment, and Pymol scripts to generate color coded protein surfaces. Additionally, we have observed an empirical structural property of AHs: the distance between the Cα atoms of the ith and (i+4)th residue is equal to the distance between the carbonyl oxygens of the ith and (i+4)th residue. We validated this using 100 non-homologous high resolution structures from the PISCES database. The source code and manual is available at http://github.com/sanchak/pagal and on http://dx.doi.org/10.5281/zenodo.11136.

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

BackgroundPost-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.ResultsWe 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.ConclusionPyTMs 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 PyTMs is a useful PyMOL plugin for modeling common post-translational modifications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0370-6) contains supplementary material, which is available to authorized users.

PAGAL - Properties and corresponding graphics of alpha helical structures in proteins.

Alpha helices (AH) are peptide fragments characterized by regular patterns of hydrogen bondingbetween the carbonyl oxygen and amino nitrogen of residues regularly spaced in sequence, resulting inspiral conformations. Their preponderance in protein structures underlines their importance. Interestingly, AHs are present in most anti-microbial peptides, although they might remain in random-coil conformations depending on the solvent dielectric. For example, the cecropin componentof the chimeric anti-microbial protein designed previously by our group comprises of two AHs linked bya short stretch of random coil. These anti-microbial peptides are often amphipathic (quantified by ahydrophobic moment), aligning hydrophobic residues on one surface and charged residues on the others. In the current work, we reproduce previously described computational methods to compute thehydrophobic moment of AHs - and provide open access to the source code (PAGAL). We simultaneously generated input files for TikZ (a package for creating high resolution graphics programmatically)to obtain the Edmundson wheel and showing the direction and magnitude of the hydrophobic moment,and Pymol scripts to generate color coded protein surfaces. Additionally, we have observed an empiricalstructural property of AHs: the distance between the Cα atoms of the ith and (i+4)th residue is equalto the distance between the carbonyl oxygens of the ith and (i+4)th residue. We validated this using100 non-homologous high resolution structures from the PISCES database. The source code and manual is available at http://github.com/sanchak/pagal and on http://dx.doi.org/10.5281/zenodo.11136.

PAGAL - Properties and corresponding graphics of alpha helical structures in proteins

Alpha helices (AH) are peptide fragments characterized by regular patterns of hydrogen bonding between the carbonyl oxygen and amino nitrogen of residues regularly spaced in sequence, resulting in spiral conformations. Their preponderance in protein structures underlines their importance. Interestingly, AHs are present in most anti-microbial peptides, although they might remain in random-coil conformations depending on the solvent dielectric. For example, the cecropin component of the chimeric anti-microbial protein designed previously by our group comprises of two AHs linked by a short stretch of random coil. These anti-microbial peptides are often amphipathic (quantified by a hydrophobic moment), aligning hydrophobic residues on one surface and charged residues on the others. In the current work, we reproduce previously described computational methods to compute the hydrophobic moment of AHs - and provide open access to the source code (PAGAL). We simultaneously generated input files for TikZ (a package for creating high resolution graphics programmatically) to obtain the Edmundson wheel and showing the direction and magnitude of the hydrophobic moment, and Pymol scripts to generate color coded protein surfaces. Additionally, we have observed an empirical structural property of AHs: the distance between the Cα atoms of the ith and (i+4)th residue is equal to the distance between the carbonyl oxygens of the ith and (i+4)th residue. We validated this using 100 non-homologous high resolution structures from the PISCES database. The source code and manual is available at http://github.com/sanchak/pagal and on http://dx.doi.org/10.5281/zenodo.11136.

PAGAL - Properties and corresponding graphics of alpha helical structures in proteins

Alpha helices (AH) are peptide fragments characterized by regular patterns of hydrogen bonding between the carbonyl oxygen and amino nitrogen of residues regularly spaced in sequence, resulting in spiral conformations. Their preponderance in protein structures underlines their importance. Interestingly, they are invariably present in all anti-microbial peptides. For example, the cecropin component of the chimeric anti-microbial protein designed previously by our group comprises of two AHs linked by a short stretch of random coil. These anti-microbial peptides are often amphipathic (quantified by a hydrophobic moment), aligning hydrophobic residues on one surface and charged residues on the others. In the current work, we reproduce previously described computational methods to compute the hydrophobic moment of AHs - and provide open access to the source code (PAGAL). We simultaneously generated input files for TikZ (a package for creating high resolution graphics programmatically) to obtain the Edmundson wheel and showing the direction and magnitude of the hydrophobic moment, and Pymol scripts to generate color coded protein surfaces. Additionally, we have observed an empirical structural property of AHs: the distance between the Cα atoms of the ith and (i+4)th residue is equal to the distance between the carbonyl oxygens of the ith and (i+4)th residue. We validated this using 100 non-homologous high resolution structures from the PISCES database. The source code and manual is available at http://github.com/sanchak/pagal and on http://dx.doi.org/10.5281/zenodo.11136.

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

BackgroundPost-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.ResultsWe 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.ConclusionPyTMs 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.

CSBB-ConeExclusion, Adapting Structure Based Solution Virtual Screening to Libraries on Solid Support

Combinatorial chemical libraries produced on solid support offer fast and cost-effective access to a large number of unique compounds. If such libraries are screened directly on-bead, the speed at which chemical space can be explored by chemists is much greater than that addressable using solution based synthesis and screening methods. Solution based screening has a large supporting body of software such as structure-based virtual screening tools which enable the prediction of protein-ligand complexes. Use of these techniques to predict the protein bound complexes of compounds synthesized on solid support neglects to take into account the conjugation site on the small molecule ligand. This may invalidate predicted binding modes, the linker may be clashing with protein atoms. We present CSBB-ConeExclusion, a methodology and computer program which provides a measure of the applicability of solution dockings to solid support. Output is given in the form of statistics for each docking pose, a unique 2D visualization method which can be used to determine applicability at a glance, and automatically generated PyMol scripts allowing visualization of protein atom incursion into a defined exclusion volume. CSBB-ConeExclusion is then exemplarically used to determine the optimum attachment point for a purine library targeting cyclin-dependent kinase 2 CDK2.

DR_bind: a web server for predicting DNA-binding residues from the protein structure based on electrostatics, evolution and geometry

DR_bind is a web server that automatically predicts DNA-binding residues, given the respective protein structure based on (i) electrostatics, (ii) evolution and (iii) geometry. In contrast to machine-learning methods, DR_bind does not require a training data set or any parameters. It predicts DNA-binding residues by detecting a cluster of conserved, solvent-accessible residues that are electrostatically stabilized upon mutation to Asp−/Glu−. The server requires as input the DNA-binding protein structure in PDB format and outputs a downloadable text file of the predicted DNA-binding residues, a 3D visualization of the predicted residues highlighted in the given protein structure, and a downloadable PyMol script for visualization of the results. Calibration on 83 and 55 non-redundant DNA-bound and DNA-free protein structures yielded a DNA-binding residue prediction accuracy/precision of 90/47% and 88/42%, respectively. Since DR_bind does not require any training using protein–DNA complex structures, it may predict DNA-binding residues in novel structures of DNA-binding proteins resulting from structural genomics projects with no conservation data. The DR_bind server is freely available with no login requirement at http://dnasite.limlab.ibms.sinica.edu.tw.

PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse

PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive, manually curated and interactive resource for studying experimentally observed post-translational modifications, primarily of human and mouse proteins. It encompasses 1 30 000 non-redundant modification sites, primarily phosphorylation, ubiquitinylation and acetylation. The interface is designed for clarity and ease of navigation. From the home page, users can launch simple or complex searches and browse high-throughput data sets by disease, tissue or cell line. Searches can be restricted by specific treatments, protein types, domains, cellular components, disease, cell types, cell lines, tissue and sequences or motifs. A few clicks of the mouse will take users to substrate pages or protein pages with sites, sequences, domain diagrams and molecular visualization of side-chains known to be modified; to site pages with information about how the modified site relates to the functions of specific proteins and cellular processes and to curated information pages summarizing the details from one record. PyMOL and Chimera scripts that colorize reactive groups on residues that are modified can be downloaded. Features designed to facilitate proteomic analyses include downloads of modification sites, kinase–substrate data sets, sequence logo generators, a Cytoscape plugin and BioPAX download to enable pathway visualization of the kinase–substrate interactions in PhosphoSitePlus®.