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. 2026 Feb 12;42(2):btag063. doi: 10.1093/bioinformatics/btag063

TrIPP: a trajectory iterative pKa predictor

Christos Matsingos 1,2,‡, Ka Fu Man 3,‡, Arianna Fornili 4,✉
Editor: Daisuke Kihara
PMCID: PMC12930849  PMID: 41678666

Abstract

Summary

The protonation propensity of ionizable residues in proteins can change in response to changes in the local residue environment. The link between protein dynamics and pKa is particularly important in pH regulation of protein structure and function. Here, we introduce TrIPP (Trajectory Iterative pKa Predictor), a Python tool to track and analyze changes in the pKa of ionizable residues along Molecular Dynamics trajectories of proteins. We show how TrIPP can be used to identify residues with physiologically relevant variations in their predicted pKa values during the simulations and link them to changes in the local and global environment.

Availability and implementation

TrIPP is available at https://github.com/fornililab/TrIPP.

1 Introduction

The conformation of proteins can depend on different factors, including the pH. Indeed, several examples exist of proteins that undergo structural shifts in response to physiological pH changes (Di Russo et al. 2012, Schönichen et al. 2013, Warwicker 2022, Matsingos et al. 2024). The link between pH and protein structure is mediated by the dependence between pKa values, which regulate the protonation propensity of ionizable residues, and the local residue environment, which can significantly change with the protein conformation.

Due to the difficulty in experimentally measuring pKa values, different computational methods (Rabenstein and Knapp 2001, Gordon et al. 2005, Alexov et al. 2011, Olsson et al. 2011, Anandakrishnan et al. 2012, Reis et al. 2020, Gokcan and Isayev 2022, Wei et al. 2023) have been developed to predict pKa deviations from their reference values (isolated amino acid in solution). These approaches are normally applied to single structures, e.g. to define the protonation state of ionizable residues before starting a Molecular Dynamics (MD) simulation. Few studies have also considered pKa calculations over structural ensembles to take into account that structural variations observed during MD simulations can influence predicted pKa values (Lee et al. 2014, Matsingos et al. 2024). Building on this multiple-structure perspective, we introduce TrIPP (Trajectory Iterative pKa Predictor), a tool to track, analyze, and visualize pKa changes when postprocessing MD trajectories.

TrIPP is based on the iterative use of PROPKA 3 (Olsson et al. 2011), one of the most popular empirical approaches for single-structure pKa prediction. In addition, TrIPP provides optional support to run the deep learning predictor pKAI (Reis et al. 2022), trained on Poisson Boltzmann-based predictions from PypKa (Reis et al. 2020). We show that TrIPP can be used to visualize pKa distributions and time evolutions over MD trajectories and identify residues with conformation-dependent pKa values. Pseudo-mutations can be carried out to assess the influence of specific residues on pKa regulation. In addition, TrIPP can cluster trajectory frames according to the local environment of selected ionizable residues, to identify structures representative of the environments leading to different pKa values. Insights from TrIPP can be used to guide further studies and formulate mechanistic hypotheses on pH-dependent regulation of protein function.

2 Implementation

TrIPP has been implemented using Python 3.9 along with several Python packages, including PROPKA 3.5.0 (Olsson et al. 2011), pKAI 1.2.0 (Reis et al. 2022), MDAnalysis 2.5.0 (Michaud‐Agrawal et al. 2011, Gowers et al. 2016), Pandas 2.0.3 (McKinney 2010), NumPy 1.25.0 (Harris et al. 2020), scikit-learn 1.5.0 (Pedregosa et al. 2011), scikit-learn-extra 0.3.0, scipy 1.13.0 (Virtanen et al. 2020), and seaborn 0.13.2 (Waskom 2021). The software has been packaged with Poetry 1.7.1 and is available for installation through PyPI.

2.1 TrIPP workflow

The workflow comprises four stages: data input, data pre-processing, pKa prediction, and data analysis (Fig. 1A).

Figure 1.

Figure 1

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TrIPP overview and application to the hen egg white lysozyme. (A) Different stages in the TrIPP workflow. (B) Distributions of predicted PROPKA pKa values for ionizable residues in lysozyme during one of the trajectories (MD1). For each residue, pKa values are binned along the x-axis and more frequent values are indicated with a darker shading. A dotted vertical line indicates the model value for the corresponding residue type.

Data input. TrIPP uses MDAnalysis to load input trajectory and topology files. Parallelization is carried out by splitting each trajectory into as many subsets of frames as the number of cores used in the calculation. Each subset is processed by a different core in the subsequent steps.

Data preprocessing. Preprocessing includes an optional pseudo-mutation feature, which can be activated by the user to replace selected residues with alanine. Where required, residue names are also converted into versions recognized by PROPKA. Each snapshot is then temporarily converted to a PDB file, which is the format required by PROPKA.

Prediction. The chosen pKa predictor (PROPKA by default) is iteratively run on each snapshot (via Python API) and its output is parsed to extract the pKa values and (optionally only for PROPKA predictions) the buried ratio (given as %) of all the ionizable residues. Temporary files (e.g. PDB snapshots and PROPKA output) are deleted after processing. The extracted values are stored in CSV files and sorted by snapshot timeframe.

Data analysis. Plots of pKa distributions and time evolutions can be generated from the pKa values predicted for each snapshot. TrIPP can write PyMOL sessions (PSE format) to colour-map different properties on the 3D structure, including the pKa values of all the ionizable residues for a selected frame or averaged over a set of trajectories. The difference between the average and reference model pKa values used by the specific predictor can be also mapped. If the projection of the trajectories on selected principal components is provided by the user, an automatic scanning of the correlation coefficients between projections and pKa values can be run to identify possible pKa-collective motions coupling.

The generated data can be used to extract representative structures from the trajectory using one of the available clustering algorithms: K-Medoids (Kaufman 2005), greedy (Micheletti et al. 2000), DBSCAN (Ester et al. 1996), and HDBSCAN (Campello et al. 2013). The feature matrix used for the clustering is built from pKa values of user-selected residues and (optionally) their buried ratio (PROPKA only) and inter-residue distances. Features are Z-score normalized before clustering and (optionally) subjected to Principal Component Analysis (PCA). For each method, clustering hyperparameters can be optionally optimized via grid search, using silhouette widths (Rousseeuw 1987) to assess the quality of the clustering.

At last, TrIPP provides a function to automatically scan all ionizable residues for possible correlations between their pKa values and a time-dependent property provided by the user. For example, projections from a principal component analysis of the trajectories can be used to represent collective motions in the protein.

2.2 TrIPP classes

TrIPP provides its functionalities through 3 main classes: Trajectory (input, preprocessing and prediction), Visualization (3D mapping of pKa- related values) and Clustering. A comprehensive tutorial demonstrating the use of these classes is included in the TrIPP github distribution as Jupyter notebook.

3 Application

TrIPP was applied to MD simulations of the hen egg-white lysozyme to illustrate its features. This enzyme was chosen since the pKa values of its residues have been extensively studied both experimentally and computationally (Williams et al. 2010, Webb et al. 2011). In particular, the pKa of acidic residues in its active site has been shown to be modulated by interactions with nearby residues (Williams et al. 2010). MD simulations were run for 300 ns (production) in five replicates (MD1–MD5), system preparation and MD protocol are described in the Supplementary Methods and Table 1, available as supplementary data at Bioinformatics online. TrIPP was run on each replica using frames sampled every 100 ps and PROPKA as predictor.

Average pKa values show small variations across replicas, indicating good reproducibility, and a good agreement with the available experimental values (Table 2, available as supplementary data at Bioinformatics online). Inspecting the distribution of pKa values observed for all ionizable residues (Fig. 1B and Fig. 1, available as supplementary data at Bioinformatics online) highlights the residues that have pKa values (blue shades) significantly shifted from their model pKa (dotted line). Among the acidic residues, Asp52 and Glu35 stand out because their pKa values are upshifted towards the region of physiological pH. This can be also easily spotted in the PyMOL visualizations of the average pKa values for acidic residues and their deviation from model values (Fig. 2, available as supplementary data at Bioinformatics online), where Asp52 and Glu35 are the only acidic residues with an increase in the average pKa from the model values. While the average increase is small, inspection of the pKa time evolution for these residues indicates that values can be as high as ∼7 for Glu35 and almost 6 for Asp52 (Fig. 2A). Interestingly, Glu35 and Asp52 are located in the lysozyme active site and are directly involved in the catalytic mechanism (Ramos et al. 2025).

Figure 2.

Figure 2

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TrIPP-based analysis of MD simulations of lysozyme. (A) Time evolution of pKa values predicted for Glu35, Asp52, and Arg114 during one of the lysozyme trajectories (MD1). The running average (5-ns window, dark shade) is shown together with the instantaneous values (light shade). (B) Porcupine representation of the first Principal Component (PC1) from the MD1 and MD2 lysozyme trajectories. Orange spikes show the direction and relative amplitude of motion of each residue along the PC (spikes shorter than 20% of the maximum length are omitted). Key residues are labelled and their position indicated with magenta spheres. Additional labels indicate the α and β domains in the left panel, and the C and D helices in the right panel. (C) K-Medoid cluster representatives (cartoon) illustrating different structural environments leading to different pKa values for Glu35, Asp52, Arg114, Tyr20, and Tyr23. Key residues are highlighted as sticks and labelled. Polar contacts from PyMOL are represented with dashed lines. The feature matrix used for clustering was first subjected to dimensionality reduction via a Principal Component Analysis. The middle panel shows a projection of all trajectories on the first two principal components x1 and x2, with each frame coloured according to its cluster ID.

It is worth noting that these simulations were performed using conventional MD, in which the protonation states of ionizable residues are fixed. Comparing the pKa values sampled during the simulations with the range over which both protonation states are expected to have significant populations (grey shaded region in Fig. 1, available as supplementary data at Bioinformatics online) can flag residues for which alternative fixed protonation states or constant-pH MD (Oliveira et al. 2022) may be warranted, to ensure that conformational sampling remains consistent with protonation propensities over the course of the simulation. Notably, a subset of frames falls within this range for Glu35 (Fig. 1, available as supplementary data at Bioinformatics online), indicating appreciable population would be expected for the protonated form, consistent with protonation of this residue during catalysis (Ramos et al. 2025).

To detect possible couplings between pKa changes and protein dynamics, the correlation coefficient between the pKa time evolution and the main collective motions observed during the simulations (as described by projections of each trajectory on its top-ranking principal components) was calculated for each ionizable residue (Fig. 3, available as supplementary data at Bioinformatics online). The largest correlations (0.5 or higher) with the first two principal components (PC1 and PC2) were observed for Tyr20, Glu35, Asp52, Arg114, and to a less extent Tyr23, suggesting that the dominant protein motions can modulate the pKa value of these residues.

Two different types of recurring motions were identified. The first one, exemplified by PC1 from MD1 (Fig. 2B, left), is the previously observed hinge-bending motion of the α- and β-domains(Brooks and Karplus 1985), where Asp52, Glu35, Arg114 are part of or close to the moving regions. The projections of the different trajectories (Fig. 4A, available as supplementary data at Bioinformatics online) show patterns that are clearly correlated (Glu35) or anti-correlated (Asp52, Arg114) with pKa time evolutions (Fig. 5, available as supplementary data at Bioinformatics online). PC1 from MD2 instead describes a more localized restructuring of the loop between helices C and D, which is positioned above Tyr20 and Tyr23 (Fig. 2B, right).

Trajectories were then clustered to identify structures representative of the different environments around residues with motion-sensitive pKa values (Fig. 2C). Clusters 0 and 1 feature a salt-bridge between Glu35 and Arg114, which is instead absent in clusters 2 and 3. This explains their distinct pKa distributions across the clusters (Fig. 6, available as supplementary data at Bioinformatics online), since acidic and basic residues forming salt bridges are expected to have down- and up-shifted pKa values, respectively, compared to their non-interacting states. Clusters 1 differs from cluster 0 because of the presence of additional hydrogen bonding between Asp52 and Asn44, which explains the lower Asp52 pKa values for this cluster, and between Tyr20 and the CD loop backbone, which leads to increased Tyr20 pKa values.

The environment of Glu35 and Asp52 was further analyzed using the pseudo-mutation tool implemented in TrIPP, where pKa values are re-calculated after replacing selected residues with alanine, while keeping all the other coordinates in the protein unchanged. It is important to note that the pseudo-mutation feature is meant to help tracking the contribution of nearby residues to pKa shifts from model values in the original (wild type) protein, rather than providing estimates of pKa in mutants. Such quantitative assessment would require directly sampling the mutant ensemble with new MD simulations.

Replacing residues Asn44, Asn46 and Asn59 with alanine shows an increase of Asp52 pKa across all the replicas (Fig. 7A and C, available as supplementary data at Bioinformatics online). This is consistent with the formation of hydrogen bonds between all these residues and Asp52. Interestingly, instead of just shifting the Asp52 pKa time evolution profile, Asn44 also modulates its shape. Indeed, the Asn44Ala pseudo-mutant shows smaller pKa fluctuations compared to the wild type (Fig. 7C, available as supplementary data at Bioinformatics online), consistently with the formation and breaking of Asn44-Asp52 hydrogen bonds during the trajectory. The role of Asn44 in stabilizing Asp52 negative charge and modulating its availability for substrate binding has been highlighted in recent structural studies of lysozyme (Ramos et al. 2025). Similarly, replacing Arg114 with alanine induces an increase of the Asp52 pKa but only in the parts of the trajectory where the two residues are interacting with each other (Fig. 7B and D, available as supplementary data at Bioinformatics online). These examples illustrate how analyzing the effect of pseudo-mutations on the shape of the pKa time evolution profile of a given residue can help identifying the residues that mediate the dynamic response of its pKa value.

4 Concluding remarks

We developed a Python tool to track, analyze and visualize changes in pKa values of ionizable residues in the post-processing of MD trajectories.

Information from TrIPP can be used to guide protein modeling, especially when a pH sensing behaviour is known or suspected (Matsingos et al. 2024). It is important to highlight that TrIPP is not meant to replace in any way constant-pH simulations (Williams et al. 2010, Oliveira et al. 2022), where the protonation state of ionizable residues is allowed to change. Here, we have shown how TrIPP can be used in conjunction with conventional MD simulations. While the sampled frames will reflect fixed protonation states, detection of pKa changes during the trajectory can highlight residues that might require further simulations with alternative protonation states. Observing large correlations between residues pKa and global motions might suggest that pH modulation of protein conformation is involved, warranting further investigation. Moreover, clustering and pseudo-mutation analyses can be used to build testable hypotheses about the molecular mechanisms underlying pH-dependent structural and functional changes.

Note on related software. An open-source Python implementation for running PROPKA across MD trajectory frames is also available (Irfan Dotson et al. 2020). Complementary features of TrIPP are support for parallel execution and for an alternative pKa prediction engine (pKAI), residue-name preprocessing that allows for ligand containing systems and an extensive downstream analysis framework including pseudo-mutations and clustering of pKa-related features as described in this contribution.

Supplementary Material

btag063_Supplementary_Data
btag063_supplementary_data.pdf (3.3MB, pdf)

Acknowledgements

We would like to thank Yu-Yuan Yang and Dr Alessandro Pandini for testing and providing feedback on earlier versions of TrIPP.

Contributor Information

Christos Matsingos, Department of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom; Saints-Pères Paris Institute for the Neurosciences, Université Paris Cité, Paris 75270, France.

Ka Fu Man, Department of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.

Arianna Fornili, Department of Chemistry, School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.

Author contributions

Christos Matsingos (Conceptualization [lead], Data curation [supporting], Formal analysis [equal], Investigation [equal], Methodology [lead], Project administration [supporting], Software [equal], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Ka Fu Man (Conceptualization [equal], Data curation [lead], Formal analysis [equal], Investigation [equal], Methodology [equal], Project administration [equal], Software [lead], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), and Arianna Fornili (Conceptualization [equal], Funding acquisition [lead], Investigation [equal], Methodology [equal], Project administration [equal], Supervision [lead], Writing—original draft [lead], Writing—review & editing [equal])

Supplementary material

Supplementary material is available at Bioinformatics online.

Conflicts of interest

None declared.

Funding

This work was supported by the Biotechnology and Biological Sciences Research Council [BB/M009513/1] and the Engineering and Physical Sciences Research Council [EP/T518086/1], and made use of time on HPC granted via the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim (http://hecbiosim.ac.uk), supported by the Engineering and Physical Sciences Research Council [EP/X035603/1].

Data availability

TrIPP source code and tutorial files are available at https://github.com/fornililab/TrIPP.

References

  1. Alexov E, Mehler EL, Baker N  et al.  Progress in the prediction of p Ka values in proteins. Proteins  2011;79:3260–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Dotson D, Alibay I, Sexton R  et al. (2020). Becksteinlab/propkatraj: 1.1.x. Zenodo.   10.5281/zenodo.3228425 [DOI]
  3. Anandakrishnan R, Aguilar B, Onufriev AV.  H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res  2012;40:W537–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Brooks B, Karplus M.  Normal modes for specific motions of macromolecules: application to the hinge-bending mode of lysozyme. Proc Natl Acad Sci USA  1985;82:4995–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Campello RJGB, Moulavi D, Sander J  et al.  Density-based clustering based on hierarchical density estimates. In: Pei J, Tseng VS, Cao L (eds.), Advances in Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer, 2013, 160–172. [Google Scholar]
  6. Di Russo NV, Estrin DA, Martí MA  et al.  pH-dependent conformational changes in proteins and their effect on experimental pKas: the case of nitrophorin 4. PLoS Comput Biol  2012;8:e1002761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Ester M, Kriegel H-P, Sander J  et al.  A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96. Portland, Oregon: AAAI Press, 1996, 226–31. [Google Scholar]
  8. Gokcan H, Isayev O.  Prediction of protein p Ka with representation learning. Chem Sci  2022;13:2462–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gordon JC, Myers JB, Folta T  et al.  H++: a server for estimating p Ka s and adding missing hydrogens to macromolecules. Nucleic Acids Res  2005;33:W368–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gowers RJ, Linke M, Barnoud J  et al. MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations. In S. Benthall and S. Rostrup (eds), Proceedings of the 15th Python in Science Conference, Austin, TX: SciPy, 2016, 98–105. 10.25080/majora-629e541a-00e [DOI]
  11. Harris CR, Millman KJ, Van Der Walt SJ  et al.  Array programming with NumPy. Nature  2020;585:357–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kaufman L.  Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ: Wiley, 2005.
  13. Lee C, Yashiro S, Dotson DL  et al.  Crystal structure of the sodium–proton antiporter NhaA dimer and new mechanistic insights. J Gen Physiol  2014;144:529–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Matsingos C, Howell LA, McCormick PJ  et al.  Elucidating the activation mechanism of the proton-sensing GPR68 receptor. J Mol Biol  2024;436:168688. [DOI] [PubMed] [Google Scholar]
  15. McKinney W.  Data Structures for Statistical Computing in Python. Austin, Texas: SciPy, 2010, 56–61. 10.25080/Majora-92bf1922-00a [DOI]
  16. Michaud‐Agrawal N, Denning EJ, Woolf TB  et al.  MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem  2011;32:2319–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Micheletti C, Seno F, Maritan A.  Recurrent oligomers in proteins: an optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies. Proteins  2000;40:662–74. [DOI] [PubMed] [Google Scholar]
  18. Oliveira VMd, Liu R, Shen J.  Constant pH molecular dynamics simulations: current status and recent applications. Curr Opin Struct Biol  2022;77:102498. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Olsson MHM, Søndergaard CR, Rostkowski M  et al.  PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput  2011;7:525–37. [DOI] [PubMed] [Google Scholar]
  20. Pedregosa F, Varoquaux G, Gramfort A  et al.  Scikit-learn: machine learning in Python. J Mach Learn Res  2011;12:2825–30. [Google Scholar]
  21. Rabenstein B, Knapp E-W.  Calculated pH-dependent population and protonation of carbon-monoxy-myoglobin conformers. Biophys J  2001;80:1141–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ramos J, Laux V, Mason SA  et al.  Structure and dynamics of the active site of hen egg-white lysozyme from atomic resolution neutron crystallography. Structure  2025;33:136–48.e3. [DOI] [PubMed] [Google Scholar]
  23. Reis PBPS, Bertolini M, Montanari F  et al.  A fast and interpretable deep learning approach for accurate electrostatics-driven p Ka predictions in proteins. J Chem Theory Comput  2022;18:5068–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Reis PBPS, Vila-Viçosa D, Rocchia W  et al.  PypKa: a flexible Python module for Poisson–Boltzmann-based pKa calculations. J Chem Inf Model  2020;60:4442–8. [DOI] [PubMed] [Google Scholar]
  25. Rousseeuw PJ.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math  1987;20:53–65. [Google Scholar]
  26. Schönichen A, Webb BA, Jacobson MP  et al.  Considering protonation as a posttranslational modification regulating protein structure and function. Annu Rev Biophys  2013;42:289–314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Virtanen P, Gommers R, Oliphant TE  et al. ; SciPy 1.0 Contributors. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods  2020;17:261–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Warwicker J.  The physical basis for pH sensitivity in biomolecular structure and function, with application to the spike protein of SARS-CoV-2. Front Mol Biosci  2022;9:834011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Waskom M.  seaborn: statistical data visualization. JOSS  2021;6:3021. [Google Scholar]
  30. Webb H, Tynan‐Connolly BM, Lee GM  et al.  Remeasuring HEWL pKa values by NMR spectroscopy: methods, analysis, accuracy, and implications for theoretical pKa calculations. Proteins  2011;79:685–702. [DOI] [PubMed] [Google Scholar]
  31. Wei W, Hogues H, Sulea T.  Comparative performance of high-throughput methods for protein p Ka predictions. J Chem Inf Model  2023;63:5169–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Williams SL, De Oliveira CAF, McCammon JA.  Coupling constant pH molecular dynamics with accelerated molecular dynamics. J Chem Theory Comput  2010;6:560–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Dotson D, Alibay I, Sexton R  et al. (2020). Becksteinlab/propkatraj: 1.1.x. Zenodo.   10.5281/zenodo.3228425 [DOI]

Supplementary Materials

btag063_Supplementary_Data
btag063_supplementary_data.pdf (3.3MB, pdf)

Data Availability Statement

TrIPP source code and tutorial files are available at https://github.com/fornililab/TrIPP.

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