Next Article in Journal
Fault Diagnosis Systems for Robots: Acoustic Sensing-Based Identification of Detached Components for Fault Localization
Previous Article in Journal
Effect of High-Pressure Processing on Proteolysis, Texture and Sensorial Attributes of Raw Ewe’s Cheeses Throughout Storage
Previous Article in Special Issue
ProLinker–Generator: Design of a PROTAC Linker Base on a Generation Model Using Transfer and Reinforcement Learning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 15
Issue 12
10.3390/app15126563
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Automated Chemical Shift Assignments of MAS Solid-State NMR Spectra of Complex Protein Systems by ssPINE/ssPINE-POKY

by
Andrea Estefania Lopez Giraldo
,
Mehdi Rahimi
and
Woonghee Lee
*
Department of Chemistry, University of Colorado Denver, Denver, CO 80204, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(12), 6563; https://doi.org/10.3390/app15126563
Submission received: 16 April 2025 / Revised: 31 May 2025 / Accepted: 10 June 2025 / Published: 11 June 2025
(This article belongs to the Special Issue Development and Application of Computational Chemistry Methods)
Browse Figures
Versions Notes

Abstract

:
Solid-state nuclear magnetic resonance (ssNMR) spectroscopy enables studying complex macromolecules with low solubility. Compared to solution NMR, few tools exist for biomacromolecule ssNMR data analysis. A key challenge is assigning spin systems due to low peak dispersion. Broad peaks from large dipolar couplings and shift anisotropy cause significant overlap and missing peaks. To address this, we introduce ssPINE-POKY, a user-friendly graphical user interface (GUI) integrated into the POKY suite. ssPINE-POKY streamlines the automation of spin system recognition and chemical shift assignment in multidimensional ssNMR spectra by integrating the ssPINE algorithm within an intuitive interface. The platform allows easy and fast job submission, real-time result visualization, and enhanced analysis through additional built-in tools, significantly improving the efficiency of ssNMR data interpretation.

1. Introduction

Liquid-state nuclear magnetic resonance (NMR) spectroscopy provides detailed information about the structure and dynamics of biomolecules in solution, such as proteins, nucleic acids, and carbohydrates. However, some biomolecules, such as membrane proteins, amyloid fibrils, and large macromolecular assemblies, are challenging to study using liquid-state NMR due to their low solubility and structural complexity. Therefore, magic angle spinning (MAS) solid-state NMR (ssNMR) spectroscopy has become a powerful alternative for studying such systems, as it enables the investigation of complex biomolecules [1,2,3,4].
Recent advances in MAS ssNMR have improved the ability to study complex biomolecular systems at atomic resolution. For instance, techniques for solvent suppression, hydrogen/deuterium (H/D) exchange, and deuterium spectroscopy have been developed to improve spectral quality [5]. The use of perdeuterated and labeled proteins combined with ultrafast MAS has demonstrated remarkable improvements in spectral resolution [6]. Additionally, deuterium quadrupolar interactions have enabled site-specific characterization of molecular motions without the need for proton decoupling [7]. Furthermore, the emergence of proton-detected multidimensional experiments under ultrafast MAS conditions has opened new possibilities for studying protein structure and dynamics, even in the absence of complete deuteration [8,9]. These methodological innovations, together with the application of hybrid structural approaches, have contributed to the high-resolution structure determination of different biomolecule complexes [10,11,12].
Often, this investigation involves multidimensional NMR techniques, and the pulse sequences have been actively developed to observe different aspects and responses of proteins. The number of pulse sequences is increasing rapidly [2,13,14,15,16]; however, the analysis of the resulting multidimensional NMR spectra remains a significant challenge. Once NMR spectra are acquired from a protein sample, most tasks are performed on computers. Therefore, lack of user-friendly software tools for automation would be a huge drawback, which is partly true with biomolecular ssNMR spectroscopy. Considering the potential of ssNMR on challenging and complex biomolecules described above, it is important to develop critical tools analogous to existing in solution NMR.
Tasks in solution NMR are more streamlined than in ssNMR. For example, the POKY suite provides all-in-one features for routinely conducted analytical activities, which is a great advancement compared to its ancestor, NMRFAM-SPARKY [17,18]. Initially, the APES plugin (two-letter-code ae) automates peak picking, cross-validates between them and filters out noise peaks [19]. Subsequently, the I-PINE webserver and the PINE-SPARKY.2 plugin (two-letter-code ep) provide automated chemical shift assignments for both backbone and sidechain atoms using the identified peaks as inputs. They support twenty-nine solution NMR experiments and versions, including the POKY capability of integrating artificial intelligence + three-dimensional structure (AI-based 3D-structure methods) and chemical prediction methods in the assignment. Additionally, the PONDEROSA plugin (two-letter-code c3) offers combined automated peak picking, assignment of NOESY spectra and calculation of 3D structure, requiring near-complete backbone and sidechain chemical shifts to run optimally. Moreover, the user can intervene in any of these steps to improve the quality of results, and the existence of these automation tools in the POKY suite provides seamless user experience of the Integrative NMR research platform unlike other automation programs.
This kind of streamlined routine automation tool has focused more on data from solution NMR despite ssNMR’s potential in challenging systems. The large number of peaks and complex coupling patterns in these spectra make it difficult to assign the signals to specific atomic sites and extract meaningful structural information. Currently only a limited number of tools are available for the analysis of complex ssNMR datasets, and most of these require operation through a command-line interface, requiring coding knowledge to be run. Additionally, users often need to manually prepare input files, making the process time-consuming and not user-friendly.
Some of the available programs include CCPNMR Analysis [20], which provides a graphical user interface (GUI) that supports manual assignment and analysis of solid-state NMR spectra. In contrast, ARTINA [21] offers a fully automated, web-based workflow for protein assignment and structure determination using raw NMR data, but it does not allow user intervention or interactive data manipulation at any stage of the process.
In terms of data processing, ssNAKE [22] is a script-based tool designed for the processing and visualization of ssNMR data. Similarly, MestReNova [23] offers a GUI for processing 1D and 2D NMR spectra and supports solid-state NMR magic angle spinning (MAS) and Cross-Polarization/MAS (CP/MAS) data. Several automated assignment tools based on scripting have also been developed for ssNMR. Some of these include ssFLYA [24,25], which performs probabilistic assignments, and ASAP [26], which is particularly effective for congested ssNMR spectra, amongst others [27,28,29]. However, these tools typically operate via command-line scripts and require users to configure input parameters manually.
In contrast, POKY offers an integrated, GUI-based environment that unifies multiple steps of the ssNMR workflow [30]. Within a single platform, users can process data, visualize spectra, perform manual and automated assignments, verify analyses, and conduct structure calculations, among others. The ssPINE-POKY program enhances this capability by providing a locally installable, comprehensive GUI toolkit, enabling users to perform a wide range of ssNMR tasks without requiring scripting knowledge—making it a more accessible and efficient solution for the community.
We have been continuously implementing new tools in the POKY suite. For example, the iPick peak picking program has been made to support both solution NMR and ssNMR. It adopts APES’s cross-validation feature across different experiments. While APES is limited in fixed solution NMR experiments, iPick’s versatile design enables the user to use a wider array of solution and solid-state NMR experiments. Because ssNMR spectra are more difficult for peak picking programs, we have made REDEN to provide a way to investigate clustered regions with multiple peaks on top of each other resulting in a poor resolution. The ssPINE program has been made as an automated algorithm for ssNMR spectra [31]. It automates the process of recognizing, categorizing, and assigning signals from various types of multidimensional ssNMR spectra. This software uses a combination of automated methods to facilitate assignment and analysis of ssNMR data. With the sequence of the protein and peak lists from fourteen different 2D and 3D ssNMR experiments as inputs, ssPINE generates backbone and sidechain assignments. We have made a web submission form to run ssPINE and receive results via email. Once chemical shifts are assigned, PONDEROSA-C/S in the POKY suite can be used to automate analysis of long-mixing-time versions of 2D-(H)CC(H), NCACX and NCOCX spectra for dipolar interaction assignments, restraint generation and automated 3D structure calculation. However, the utilization of ssPINE still represents a laborious web submission process, involving several steps: generation of peak lists, navigating web browsers, file uploads, job submissions, result downloads, extraction of data, and integration with the project.
In this paper, we introduce a new graphical user interface (GUI), ssPINE-POKY, which is now integrated with the popular POKY suite for NMR analysis and biomolecular structure determination, with 146 current citations and growing trend as of May 2025. ssPINE-POKY’s interface allows users to submit ssPINE jobs easily and supports the visualization, verification, browsing, and downloading of results, making the analysis of complex protein MAS ssNMR data straightforward and faster, compared with the previous ssPINE online version. With ssPINE-POKY, researchers can quickly obtain automated peak assignments and accelerate 3D structure determination. Furthermore, in contrast to the previous alpha version of the ssPINE webserver, ssPINE-POKY integrates different analysis tools, including random coil index, hydrophobic core prediction, secondary structure prediction and secondary chemical shift analysis, protein backbone φ/ψ dihedral angle prediction, and web result visualization, among others.
The combination of the ssPINE algorithm and the ssPINE-POKY GUI offers a robust solution for analyzing complex ssNMR data and can significantly facilitate the determination of the structures of large macromolecular assemblies with high structural complexity and low solubility. As such, we recommend ssPINE-POKY as an essential tool for structural biologists studying challenging biomolecules using MAS ssNMR spectroscopy.

2. Materials and Methods

Our ssPINE program for automated ssNMR assignments was previously implemented and made available through a web submission form. While useful, the multiple required steps made the process complex and time-consuming, limiting accessibility. To streamline the workflow, we introduce ssPINE-POKY, which provides a user-friendly graphical user interface (GUI) that eliminates those extra steps and simplifies the use of ssPINE. This GUI enables easy submission, manipulation, and visualization of results, along with a new feature called “web results”, allowing users to have a custom visualization of the results on their web browser. Figure 1 provides a summary of the steps needed to submit an ssPINE job and view the results.
ssPINE-POKY software was developed using Python, Bash, and HTML and is integrated with our ServerSide application, which provides a secure and unified web development environment. The client–server architecture of ssPINE and ssPINE-POKY, built around the POKY framework, is illustrated in Figure 2. Users can submit jobs either through ssPINE-POKY or directly via the web page. These requests are handled by the ServerSide application, which forwards the jobs to the MANDU server—where the ssPINE backend is executed. MANDU hosts most of the POKY suite applications.
For structure prediction, ESMFold is run through the ESM Metagenomic Atlas developed by Meta AI. If users do not provide a 3D structure, the predicted structure from ESMFold is used as input for UCBShift and SHIFTX2, both of which are installed within a VirtualEnv on MANDU. The predicted chemical shifts from UCBShift or SHIFTX2, along with data from PACSY, serve as prior probabilities for Bayesian probabilistic assignments. All modules required by ssPINE and ssPINE-POKY are pre-configured to run automatically, requiring no additional user intervention after submission.
To support the web result feature, we developed multiple HTML templates that are dynamically and interactively updated in real time. Each submission is linked to a customized result page, allowing individualized access and visualization of assignment outcomes.

3. Results

3.1. Implementation and Functionalities

To run ssPINE-POKY, users may upload a POKY project containing the ssNMR experiments or generate a practice ssNMR dataset automatically, corresponding to a BMRB code, by running the POKY feature SIM-PRAC (two-letter-code em). The minimum set of experiments required to run ssPINE consists of 2D-CC, 2D-NCA, 2D-NCO, 3D-NCACX, 3D-NCOCX, and 3D-CAN(CO)CX, though additional experiments can improve assignment quality (2D-NCACB, 3D-NCACB, 3D-NCOCACB, 3D-NCOCA, NCACO, CANCO, 3D-CAN(CO)CA, 3D-CAN(CO)CACB). For 1H-detected ssNMR data, the user can use the I-PINE webserver and PINE-SPARKY.2 plugin [32].
Peak picking must be performed before running ssPINE, which can be automated using the integrated iPick program in POKY (two-letter-code iP [33]). Users can access ssPINE-POKY via the main window under Poky > Automated assignment > Run SSPINE automated assignment (ssPINE-POKY), or by entering the two-letter code EP.
At the top of the ssPINE-POKY automation window, users can optionally enter their name and email address (Figure 3A). If provided, the server will use the email to notify users about job status and send the results upon completion. A zip file containing the results will be attached, along with a URL for accessing the visualized data in a web browser. Providing this information is not required; however, users will need the generated key (Figure 3J) to access their results.
The sequence file input section (Figure 3B) supports three formats: (1) a three-letter code sequence file with indices (*.seq), (2) a one-letter code sequence file (*.fasta), or (3) a three-letter code sequence file without indices (*.txt). Similarly to PINE-SPARKY.2 [34], the three-letter format file will be automatically set if the user has submitted the sequence to POKY using the Sequence Entry window (two-letter code sq).
Users may optionally provide additional data to refine ssPINE results, such as pre-assignment (Figure 3C) and a PDB file (Figure 3H). Additionally, they can choose to run CS-Rosetta [35,36,37,38] (Figure 3D) for protein structure prediction based on ssPINE outputs. The “Spectra to run ssPINE/ssPINE-POKY” section (Figure 3E) allows users to specify the spectra for ssPINE assignment. This is achieved by selecting a spectrum and an experiment type (Figure 3F), then clicking the “Add” button in the Button section (Figure 3G).
Additional buttons in Figure 3G provide further functionality: the “Delete” button removes selected spectra, while the “Clear” button empties the spectra box. Once all required inputs are set, users can initiate job submission by clicking the “Submit” button, which sends the job to the ssPINE web server. Additionally, the “ssPINE Web” button redirects users to the ssPINE website (https://poky.clas.ucdenver.edu/ssPINE, accessed on 30 May 2025), where they can also submit jobs by providing manually prepared peak lists and the sequence file. The “User group” button links to the online discussion forum, which includes previously asked questions from other users and provides a platform to start a new conversation in case any issues arise while using the software.
The ssPINE-POKY importer section (Figure 3J) provides tools to visualize the job results. A key number in the Key box is generated automatically when the “Submit” button is clicked. It is a unique identifier that facilitates communication between ssPINE-POKY and the ssPINE web server.

3.2. Output Files

Compared to our previously published ssPINE version with a web submission form, ssPINE-POKY introduces several improvements. In addition to providing assignments and a bar graph displaying the assignment probability for each residue, ssPINE-POKY can automatically generate labels in the NMR spectra uploaded to the POKY project, significantly reducing the time required for manual assignments.
The earlier version of ssPINE included two primary tools for providing structural and dynamic parameters: PECAN (Protein Energetic Conformational Analysis from NMR chemical shifts) for protein secondary structure prediction [39] and LACS (Linear Analysis of Chemical Shifts) for chemical shift referencing errors [40]. In contrast, ssPINE-POKY integrates several additional analysis tools. These include RCI-S2 (random coil index order parameter value) for predicting protein flexibility [41], PACSY hydrophobic core prediction based on hydrophobicity scales derived from solvent-accessible surface area [42], and GetSBY secondary structure prediction, which utilizes chemical shifts of N, HN, Cα, Cβ, CO and Hα in the protein backbone [19]. Additionally, PSIPRED [43] predicts secondary structure based on the position-specific scoring matrices generated by PSI-BLAST [44], while TALOS-N applies a chemical shift-based method to predict secondary structure [41] and protein backbone φ/ψ dihedral angles, also visualized as PACSY RAMA plots [45]. For 3D structure prediction, ESMFold uses the primary protein sequence [46], and CS-Rosetta uses the chemical shifts.

3.3. Web Results

In addition to viewing results within POKY, users can access them via a web browser. Figure 4 presents a screenshot of ssPINE Web Results displayed in the browser. These results were obtained using ssNMR data from the protein GB1, which is considered to be a model system for ssNMR development [47,48,49,50,51,52]; however, complex proteins have been previously tested using ssPINE and the results can be accessed in the original ssPINE algorithm paper [31].
The ssPINE Web Results feature is accessible by clicking the “Web…” button in the ssPINE-POKY interface (Figure 3J). The web interface provides an interactive graphical representation of the results, allowing users to explore data dynamically, providing different drop-down menus for result visualization and allowing the users to download the raw data directly for further analysis.

3.4. Visual Verification and Resubmission

As illustrated in Figure 1, the “Check” button can be used to import results back into the running POKY project. Like the PINE-SPARKY.2 plugin, ssPINE-POKY communicates with the user interactively when the button is clicked. Standalone visual plots that also exist on the web results will be popped up. Floating color-coded ssPINE labels showing probability will be created. The user will be asked if they want to assign peaks to the best ssPINE candidates with probabilities over 0.5. Once a peak is assigned, the remaining floating color-coded ssPINE candidates will be removed. Similar to its solution NMR counterparts (I-PINE, PINE-SPARKY, and PINE-SPARKY.2), ssPINE-POKY allows the user to use different visual verification tools and resubmission with fixed assignments for incremental assignment iteration. These tools include probability cutoffs for the assignment (Assign-the-best; two-letter-code ab), graphical peak-by-peak probabilistic assignment (PINE Assigner; two-letter-code pr), graphical residue-by-residue probabilistic assignment (PINE Graph Assigner; two-letter-code pp) and selecting floating labels (two-letter code se) [53]. Additionally, in cases of highly overlapped areas of the spectra or challenging residues, a semi-automated approach via Versatile Assigner [30] (two-letter-code va) can be used. This can be followed by selecting the “Use pre-assignment” option (Figure 3C) for incremental completion of assignment.

3.5. Tutorial

A YouTube video tutorial for ssPINE-POKY submission can be found at https://youtu.be/b6m2AnC7kiM (accessed on 30 May 2025). Additionally, the user can find the POKY team’s help by pressing the buttons “Question?” and “User Forum” located in the POKY main window for general questions regarding using this software.

4. Discussion and Conclusions

In conclusion, ssPINE-POKY offers a valuable solution for analyzing complex solid-state NMR (ssNMR) data in structural biology. The integration of ssPINE into the user-friendly graphical interface of ssPINE-POKY, in conjunction with the widely used POKY software for protein structure determination, significantly enhances software functionality and ease of use. Researchers can quickly submit ssPINE jobs, visualize results, and expedite the determination of 3D structures. This combined tool addresses the challenges of analyzing complex ssNMR data, especially for large macromolecular assemblies with high structural complexity and low solubility. This integration positions ssPINE-POKY as a key tool for structural biologists using ssNMR spectroscopy.
The ssPINE-POKY interface simplifies submission, integrates results into ongoing projects, and allows customizable visualization, empowering users to interpret outcomes according to their preferences. However, there are still notable limitations to be addressed. The quality of ssNMR data, particularly due to overlapping spectral signals, poses an ongoing challenge, leading to time-consuming processes for identifying peaks and analyzing data. Dealing with complex spectra containing multiple peaks and overlapping signals requires further improvement.
Looking forward, we aim to improve ssPINE-POKY by providing more options for users to customize their experience, adding features to make the initial steps of NMR assignment quicker, adding pre-assignment and labeling capabilities to expedite NMR assignment, implementing advanced spectral processing techniques for peak separation, and integrating automatic structural calculation based on NMR assignments. Additionally, considering the inclusion of new experiments to ssPINE’s supported repertoire would provide valuable structural information. By addressing these limitations and implementing improvements, ssPINE-POKY can enhance usability, accuracy, and efficiency in structural biology research.

Author Contributions

Conceptualization, W.L.; methodology, A.E.L.G., M.R. and W.L.; software, A.E.L.G., M.R. and W.L.; validation, A.E.L.G. and W.L.; formal analysis, A.E.L.G. and W.L.; investigation, A.E.L.G.; resources, W.L.; data curation, A.E.L.G.; writing—original draft preparation, A.E.L.G. and W.L.; writing—review and editing, A.E.L.G. and W.L.; visualization, A.E.L.G. and W.L.; supervision, W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Foundation, grant numbers DBI-2413041, DBI-2051595 and DBI-1902076 to W.L., and the University of Colorado Denver.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The ssPINE-POKY program presented in this study is openly available as a plugin in the POKY suite at https://poky.clas.ucdenver.edu (accessed on 30 May 2025). No new data were created or analyzed in this study. One may simulate datasets as demonstrated in https://youtu.be/HnLubuDoZxM (accessed on 30 May 2025) using POKY.

Acknowledgments

We acknowledge useful discussions in the NMR POKY/SPARKY user group (https://groups.google.com/g/nmr-sparky (accessed on 30 May 2025)).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Shcherbakov, A.A.; Medeiros-Silva, J.; Tran, N.; Gelenter, M.D.; Hong, M. From Angstroms to Nanometers: Measuring Interatomic Distances by Solid-State NMR. Chem. Rev. 2022, 122, 9848–9879. [Google Scholar] [CrossRef] [PubMed]
  2. Chow, W.Y.; De Paëpe, G.; Hediger, S. Biomolecular and Biological Applications of Solid-State NMR with Dynamic Nuclear Polarization Enhancement. Chem. Rev. 2022, 122, 9795–9847. [Google Scholar] [CrossRef]
  3. Wang, S.; Munro, R.A.; Shi, L.; Kawamura, I.; Okitsu, T.; Wada, A.; Kim, S.-Y.; Jung, K.-H.; Brown, L.S.; Ladizhansky, V. Solid-State NMR Spectroscopy Structure Determination of a Lipid-Embedded Heptahelical Membrane Protein. Nat. Methods 2013, 10, 1007–1012. [Google Scholar] [CrossRef]
  4. Shahid, S.A.; Bardiaux, B.; Franks, W.T.; Krabben, L.; Habeck, M.; van Rossum, B.-J.; Linke, D. Membrane-Protein Structure Determination by Solid-State NMR Spectroscopy of Microcrystals. Nat. Methods 2012, 9, 1212–1217. [Google Scholar] [CrossRef]
  5. Reif, B. Deuteration for High-Resolution Detection of Protons in Protein Magic Angle Spinning (MAS) Solid-State NMR. Chem. Rev. 2022, 122, 10019–10035. [Google Scholar] [CrossRef] [PubMed]
  6. Reif, B. Ultra-High Resolution in MAS Solid-State NMR of Perdeuterated Proteins: Implications for Structure and Dynamics. J. Magn. Reson. 2012, 216, 1–12. [Google Scholar] [CrossRef] [PubMed]
  7. Hologne, M.; Faelber, K.; Diehl, A.; Reif, B. Characterization of Dynamics of Perdeuterated Proteins by MAS Solid-State NMR. J. Am. Chem. Soc. 2005, 127, 11208–11209. [Google Scholar] [CrossRef]
  8. Zhang, R.; Mroue, K.H.; Ramamoorthy, A. Proton-Based Ultrafast Magic Angle Spinning Solid-State NMR Spectroscopy. Acc. Chem. Res. 2017, 50, 1105–1113. [Google Scholar] [CrossRef]
  9. Le Marchand, T.; Schubeis, T.; Bonaccorsi, M.; Paluch, P.; Lalli, D.; Pell, A.J.; Andreas, L.B.; Jaudzems, K.; Stanek, J.; Pintacuda, G. 1H-Detected Biomolecular NMR under Fast Magic-Angle Spinning. Chem. Rev. 2022, 122, 9943–10018. [Google Scholar] [CrossRef]
  10. Mote, K.R.; Gopinath, T.; Veglia, G. Determination of Structural Topology of a Membrane Protein in Lipid Bilayers Using Polarization Optimized Experiments (POE) for Static and MAS Solid State NMR Spectroscopy. J. Biomol. NMR 2013, 57, 91–102. [Google Scholar] [CrossRef]
  11. Aguion, P.I.; Marchanka, A.; Carlomagno, T. Nucleic Acid–Protein Interfaces Studied by MAS Solid-State NMR Spectroscopy. J. Struct. Biol. X 2022, 6, 100072. [Google Scholar] [CrossRef] [PubMed]
  12. Ahlawat, S.; Mote, K.R.; Lakomek, N.-A.; Agarwal, V. Solid-State NMR: Methods for Biological Solids. Chem. Rev. 2022, 122, 9643–9737. [Google Scholar] [CrossRef] [PubMed]
  13. Hong, M.; Schmidt-Rohr, K. Magic-Angle-Spinning NMR Techniques for Measuring Long-Range Distances in Biological Macromolecules. Acc. Chem. Res. 2013, 46, 2154–2163. [Google Scholar] [CrossRef]
  14. Brown, S.P.; Spiess, H.W. Advanced Solid-State NMR Methods for the Elucidation of Structure and Dynamics of Molecular, Macromolecular, and Supramolecular Systems. Chem. Rev. 2001, 101, 4125–4156. [Google Scholar] [CrossRef]
  15. Ahlawat, S.; Mopidevi, S.M.V.; Taware, P.P.; Raran-Kurussi, S.; Mote, K.R.; Agarwal, V. Assignment of Aromatic Side-Chain Spins and Characterization of Their Distance Restraints at Fast MAS. J. Struct. Biol. X 2023, 7, 100082. [Google Scholar] [CrossRef]
  16. Blahut, J.; Brandl, M.J.; Sarkar, R.; Reif, B.; Tošner, Z. Optimal Control Derived Sensitivity-Enhanced CA-CO Mixing Sequences for MAS Solid-State NMR–Applications in Sequential Protein Backbone Assignments. J. Magn. Reson. Open 2023, 16–17, 100122. [Google Scholar] [CrossRef]
  17. Lee, W.; Rahimi, M.; Lee, Y.; Chiu, A. POKY: A Software Suite for Multidimensional NMR and 3D Structure Calculation of Biomolecules. Bioinformatics 2021, 37, 3041–3042. [Google Scholar] [CrossRef]
  18. Lee, W.; Tonelli, M.; Markley, J.L. NMRFAM-SPARKY: Enhanced Software for Biomolecular NMR Spectroscopy. Bioinformatics 2015, 31, 1325–1327. [Google Scholar] [CrossRef] [PubMed]
  19. Shin, J.; Lee, W.; Lee, W. Structural Proteomics by NMR Spectroscopy. Expert Rev. Proteom. 2008, 5, 589–601. [Google Scholar] [CrossRef]
  20. Stevens, T.J.; Fogh, R.H.; Boucher, W.; Higman, V.A.; Eisenmenger, F.; Bardiaux, B.; van Rossum, B.-J.; Oschkinat, H.; Laue, E.D. A Software Framework for Analysing Solid-State MAS NMR Data. J. Biomol. NMR 2011, 51, 437–447. [Google Scholar] [CrossRef]
  21. Klukowski, P.; Riek, R.; Güntert, P. Rapid Protein Assignments and Structures from Raw NMR Spectra with the Deep Learning Technique ARTINA. Nat. Commun. 2022, 13, 6151. [Google Scholar] [CrossRef]
  22. van Meerten, S.G.J.; Franssen, W.M.J.; Kentgens, A.P.M. ssNake: A Cross-Platform Open-Source NMR Data Processing and Fitting Application. J. Magn. Reson. 2019, 301, 56–66. [Google Scholar] [CrossRef] [PubMed]
  23. Willcott, M.R. MestRe Nova. J. Am. Chem. Soc. 2009, 131, 13180. [Google Scholar] [CrossRef]
  24. Schmidt, E.; Güntert, P. A New Algorithm for Reliable and General NMR Resonance Assignment. J. Am. Chem. Soc. 2012, 134, 12817–12829. [Google Scholar] [CrossRef]
  25. Schmidt, E.; Gath, J.; Habenstein, B.; Ravotti, F.; Székely, K.; Huber, M.; Buchner, L.; Böckmann, A.; Meier, B.H.; Güntert, P. Automated Solid-State NMR Resonance Assignment of Protein Microcrystals and Amyloids. J. Biomol. NMR 2013, 56, 243–254. [Google Scholar] [CrossRef]
  26. Chen, B. ASAP: An Automatic Sequential Assignment Program for Congested Multidimensional Solid State NMR Spectra. J. Magn. Reson. 2024, 361, 107664. [Google Scholar] [CrossRef] [PubMed]
  27. Tycko, R.; Hu, K.-N. A Monte Carlo/Simulated Annealing Algorithm for Sequential Resonance Assignment in Solid State NMR of Uniformly Labeled Proteins with Magic-Angle Spinning. J. Magn. Reson. 2010, 205, 304–314. [Google Scholar] [CrossRef]
  28. Nielsen, J.T.; Kulminskaya, N.; Bjerring, M.; Nielsen, N.C. Automated Robust and Accurate Assignment of Protein Resonances for Solid State NMR. J. Biomol. NMR 2014, 59, 119–134. [Google Scholar] [CrossRef]
  29. Weber, D.K.; Wang, S.; Markley, J.L.; Veglia, G.; Lee, W. PISA-SPARKY: An Interactive SPARKY Plugin to Analyze Oriented Solid-State NMR Spectra of Helical Membrane Proteins. Bioinformatics 2020, 36, 2915–2916. [Google Scholar] [CrossRef]
  30. Manthey, I.; Tonelli, M.; II, L.C.; Rahimi, M.; Markley, J.L.; Lee, W. POKY Software Tools Encapsulating Assignment Strategies for Solution and Solid-State Protein NMR Data. J. Struct. Biol. X 2022, 6, 100073. [Google Scholar] [CrossRef]
  31. Dwarasala, A.; Rahimi, M.; Markley, J.L.; Lee, W. ssPINE: Probabilistic Algorithm for Automated Chemical Shift Assignment of Solid-State NMR Data from Complex Protein Systems. Membranes 2022, 12, 834. [Google Scholar] [CrossRef]
  32. Gopinath, T.; Manu, V.S.; Weber, D.K.; Veglia, G. PHRONESIS: A One-Shot Approach for Sequential Assignment of Protein Resonances by Ultrafast MAS Solid-State NMR Spectroscopy. ChemPhysChem 2022, 23, e202200127. [Google Scholar] [CrossRef] [PubMed]
  33. Rahimi, M.; Lee, Y.; Markley, J.L.; Lee, W. iPick: Multiprocessing Software for Integrated NMR Signal Detection and Validation. J. Magn. Reson. 2021, 328, 106995. [Google Scholar] [CrossRef]
  34. Lee, W.; Markley, J.L. PINE-SPARKY.2 for Automated NMR-Based Protein Structure Research. Bioinformatics 2018, 34, 1586–1588. [Google Scholar] [CrossRef]
  35. Shen, Y.; Lange, O.; Delaglio, F.; Rossi, P.; Aramini, J.M.; Liu, G.; Eletsky, A.; Wu, Y.; Singarapu, K.K.; Lemak, A.; et al. Consistent Blind Protein Structure Generation from NMR Chemical Shift Data. Proc. Natl. Acad. Sci. USA 2008, 105, 4685–4690. [Google Scholar] [CrossRef] [PubMed]
  36. Shen, Y.; Vernon, R.; Baker, D.; Bax, A. De Novo Protein Structure Generation from Incomplete Chemical Shift Assignments. J. Biomol. NMR 2009, 43, 63–78. [Google Scholar] [CrossRef] [PubMed]
  37. Shen, Y.; Bryan, P.N.; He, Y.; Orban, J.; Baker, D.; Bax, A. De Novo Structure Generation Using Chemical Shifts for Proteins with High-Sequence Identity but Different Folds. Protein Sci. 2010, 19, 349–356. [Google Scholar] [CrossRef]
  38. Lange, O.F.; Rossi, P.; Sgourakis, N.G.; Song, Y.; Lee, H.-W.; Aramini, J.M.; Ertekin, A.; Xiao, R.; Acton, T.B.; Montelione, G.T.; et al. Determination of Solution Structures of Proteins up to 40 kDa Using CS-Rosetta with Sparse NMR Data from Deuterated Samples. Proc. Natl. Acad. Sci. USA 2012, 109, 10873–10878. [Google Scholar] [CrossRef]
  39. Eghbalnia, H.R.; Wang, L.; Bahrami, A.; Assadi, A.; Markley, J.L. Protein Energetic Conformational Analysis from NMR Chemical Shifts (PECAN) and Its Use in Determining Secondary Structural Elements. J. Biomol. NMR 2005, 32, 71–81. [Google Scholar] [CrossRef]
  40. Wang, L.; Eghbalnia, H.R.; Bahrami, A.; Markley, J.L. Linear Analysis of Carbon-13 Chemical Shift Differences and Its Application to the Detection and Correction of Errors in Referencing and Spin System Identifications. J. Biomol. NMR 2005, 32, 13–22. [Google Scholar] [CrossRef]
  41. Berjanskii, M.V.; Wishart, D.S. A Simple Method To Predict Protein Flexibility Using Secondary Chemical Shifts. J. Am. Chem. Soc. 2005, 127, 14970–14971. [Google Scholar] [CrossRef] [PubMed]
  42. Lee, W.; Yu, W.; Kim, S.; Chang, I.; Lee, W.; Markley, J.L. PACSY, a Relational Database Management System for Protein Structure and Chemical Shift Analysis. J. Biomol. NMR 2012, 54, 169–179. [Google Scholar] [CrossRef]
  43. Jones, D.T. Protein Secondary Structure Prediction Based on Position-Specific Scoring matrices11Edited by G. Von Heijne. J. Mol. Biol. 1999, 292, 195–202. [Google Scholar] [CrossRef]
  44. Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs. Nucleic Acids Res. 1997, 25, 3389–3402. [Google Scholar] [CrossRef]
  45. Lee, W.; Cornilescu, G.; Dashti, H.; Eghbalnia, H.R.; Tonelli, M.; Westler, W.M.; Butcher, S.E.; Henzler-Wildman, K.A.; Markley, J.L. Integrative NMR for Biomolecular Research. J. Biomol. NMR 2016, 64, 307–332. [Google Scholar] [CrossRef] [PubMed]
  46. Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; et al. Evolutionary-Scale Prediction of Atomic-Level Protein Structure with a Language Model. Science 2023, 379, 1123–1130. [Google Scholar] [CrossRef]
  47. Franks, W.T.; Zhou, D.H.; Wylie, B.J.; Money, B.G.; Graesser, D.T.; Frericks, H.L.; Sahota, G.; Rienstra, C.M. Magic-Angle Spinning Solid-State NMR Spectroscopy of the Β1 Immunoglobulin Binding Domain of Protein G (GB1):  15N and 13C Chemical Shift Assignments and Conformational Analysis. J. Am. Chem. Soc. 2005, 127, 12291–12305. [Google Scholar] [CrossRef]
  48. Frericks Schmidt, H.L.; Sperling, L.J.; Gao, Y.G.; Wylie, B.J.; Boettcher, J.M.; Wilson, S.R.; Rienstra, C.M. Crystal Polymorphism of Protein GB1 Examined by Solid-State NMR Spectroscopy and X-Ray Diffraction. J. Phys. Chem. B 2007, 111, 14362–14369. [Google Scholar] [CrossRef] [PubMed]
  49. Ishii, Y.; Wickramasinghe, A.; Matsuda, I.; Endo, Y.; Ishii, Y.; Nishiyama, Y.; Nemoto, T.; Kamihara, T. Progress in Proton-Detected Solid-State NMR (SSNMR): Super-Fast 2D SSNMR Collection for Nano-Mole-Scale Proteins. J. Magn. Reson. 2018, 286, 99–109. [Google Scholar] [CrossRef]
  50. Matsunaga, T.; Matsuda, I.; Yamazaki, T.; Ishii, Y. Decoherence Optimized Tilted-Angle Cross Polarization: A Novel Concept for Sensitivity-Enhanced Solid-State NMR Using Ultra-Fast Magic Angle Spinning. J. Magn. Reson. 2021, 322, 106857. [Google Scholar] [CrossRef]
  51. Shi, X.; Rienstra, C.M. Site-Specific Internal Motions in GB1 Protein Microcrystals Revealed by 3D 2H–13C–13C Solid-State NMR Spectroscopy. J. Am. Chem. Soc. 2016, 138, 4105–4119. [Google Scholar] [CrossRef] [PubMed]
  52. Franks, W.T.; Wylie, B.J.; Stellfox, S.A.; Rienstra, C.M. Backbone Conformational Constraints in a Microcrystalline U-15N-Labeled Protein by 3D Dipolar-Shift Solid-State NMR Spectroscopy. J. Am. Chem. Soc. 2006, 128, 3154–3155. [Google Scholar] [CrossRef] [PubMed]
  53. Lee, W.; Westler, W.M.; Bahrami, A.; Eghbalnia, H.R.; Markley, J.L. PINE-SPARKY: Graphical Interface for Evaluating Automated Probabilistic Peak Assignments in Protein NMR Spectroscopy. Bioinformatics 2009, 25, 2085–2087. [Google Scholar] [CrossRef] [PubMed]
Applsci 15 06563 g001
Figure 1. Simplified ssPINE-POKY workflow. The process begins with opening the POKY project, which includes NMR spectra, peak list, and the protein sequence. The user then opens ssPINE-POKY and provides the required input parameters. Upon submission, multiple analytical programs and servers, including ssPINE, are executed automatically. Once the process is complete, results can be reviewed directly within POKY using the “Check” button or accessed via the web-based interface using the “Web…” button.
Figure 1. Simplified ssPINE-POKY workflow. The process begins with opening the POKY project, which includes NMR spectra, peak list, and the protein sequence. The user then opens ssPINE-POKY and provides the required input parameters. Upon submission, multiple analytical programs and servers, including ssPINE, are executed automatically. Once the process is complete, results can be reviewed directly within POKY using the “Check” button or accessed via the web-based interface using the “Web…” button.
Applsci 15 06563 g001
Applsci 15 06563 g002
Figure 2. Client–server architecture of ssPINE and ssPINE-POKY implemented around POKY.
Figure 2. Client–server architecture of ssPINE and ssPINE-POKY implemented around POKY.
Applsci 15 06563 g002
Applsci 15 06563 g003
Figure 3. Screenshot of ssPINE-POKY. The user-friendly graphical user interface (GUI) can be launched as a plugin in POKY using the two-letter-code EP. (A) Name and email fields (optional) for receiving results via email. (B) Sequence file input. (C) Option to use a pre-assignment. (D) Selection of CS-Rosetta for protein structure prediction based on ssPINE outputs. (E) List of user-added spectra. (F) Dropdown menus for spectrum type selection. (G) Row of functional buttons. (H) PDB file upload option to enhance ssPINE results. (I) Threshold values for chemical shift prediction. (J) Section for importing ssPINE results; the key is automatically generated upon clicking the “Submit” button. (K) Adjustable 15N and 13C tolerances and sample pH settings, the tolerance unit “ppm” can be visualized when the mouse hovers over on the box.
Figure 3. Screenshot of ssPINE-POKY. The user-friendly graphical user interface (GUI) can be launched as a plugin in POKY using the two-letter-code EP. (A) Name and email fields (optional) for receiving results via email. (B) Sequence file input. (C) Option to use a pre-assignment. (D) Selection of CS-Rosetta for protein structure prediction based on ssPINE outputs. (E) List of user-added spectra. (F) Dropdown menus for spectrum type selection. (G) Row of functional buttons. (H) PDB file upload option to enhance ssPINE results. (I) Threshold values for chemical shift prediction. (J) Section for importing ssPINE results; the key is automatically generated upon clicking the “Submit” button. (K) Adjustable 15N and 13C tolerances and sample pH settings, the tolerance unit “ppm” can be visualized when the mouse hovers over on the box.
Applsci 15 06563 g003
Applsci 15 06563 g004
Figure 4. Screenshot showing the web-based result visualization feature of ssPINE-POKY. This feature enables users to effortlessly view and download all generated results directly from the web browser interface.
Figure 4. Screenshot showing the web-based result visualization feature of ssPINE-POKY. This feature enables users to effortlessly view and download all generated results directly from the web browser interface.
Applsci 15 06563 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Giraldo, A.E.L.; Rahimi, M.; Lee, W. Automated Chemical Shift Assignments of MAS Solid-State NMR Spectra of Complex Protein Systems by ssPINE/ssPINE-POKY. Appl. Sci. 2025, 15, 6563. https://doi.org/10.3390/app15126563

AMA Style

Giraldo AEL, Rahimi M, Lee W. Automated Chemical Shift Assignments of MAS Solid-State NMR Spectra of Complex Protein Systems by ssPINE/ssPINE-POKY. Applied Sciences. 2025; 15(12):6563. https://doi.org/10.3390/app15126563

Chicago/Turabian Style

Giraldo, Andrea Estefania Lopez, Mehdi Rahimi, and Woonghee Lee. 2025. "Automated Chemical Shift Assignments of MAS Solid-State NMR Spectra of Complex Protein Systems by ssPINE/ssPINE-POKY" Applied Sciences 15, no. 12: 6563. https://doi.org/10.3390/app15126563

APA Style

Giraldo, A. E. L., Rahimi, M., & Lee, W. (2025). Automated Chemical Shift Assignments of MAS Solid-State NMR Spectra of Complex Protein Systems by ssPINE/ssPINE-POKY. Applied Sciences, 15(12), 6563. https://doi.org/10.3390/app15126563

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop