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TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences

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Kabir, Md Wasi Ul, Alawad, Duaa Mohammad, Mishra, Avdesh, Hoque, Md Tamjidul·2023·Biology
DOI 10.3390/biology12071020PMC PMC10376102

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Abstract

Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. The flexibility is determined by the fluctuations in torsion angles, specifically phi (φ) and psi (ψ), which define the protein backbone. These angle fluctuations are derived from variations in backbone torsion angles observed in different models. By analyzing the fluctuations in Cartesian coordinate space, we can understand the structural flexibility of proteins. Predicting torsion angle fluctuations is valuable for determining protein function and structure when these angles act as constraints. In this study, a machine learning method called TAFPred is developed to predict torsion angle fluctuations using protein sequences directly. The method incorporates various features, such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, and more. TAFPred, employing an optimized Light Gradient Boosting Machine Regressor (LightGBM), achieved high accuracy with correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the φ and ψ angles, respectively. Compared to the state-of-the-art method, TAFPred demonstrated significant improvements of 10.08% in MAE and 24.83% in PCC for the phi angle and 9.93% in MAE, and 22.37% in PCC for the psi angle.

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Figure 1 Torsion angles phi (φ) and psi (ψ). The phi angle is the angle around the -N-CA- bond (where ‘CA’ is the alpha-carbon), and the psi angle is the angle around the -CA-C- bond.
Figure 1 Torsion angles phi (φ) and psi (ψ). The phi angle is the angle around the -N-CA- bond (where ‘CA’ is the alpha-carbon), and the psi angle is the angle around the -CA-C- bond.CC BY · PubMed Central
Figure 2 Illustration of the workflow of the torsion angle fluctuation predictions.
Figure 2 Illustration of the workflow of the torsion angle fluctuation predictions.CC BY · PubMed Central
Figure 3 Feature extraction from different tools.
Figure 3 Feature extraction from different tools.CC BY · PubMed Central
Figure 4 Selection of sliding window size with optimized LightGBM regressor (phi angle). Among the tested window sizes, it was found that a window size of 3 achieved the highest 1-MAE+PCC (mean absolute error + Pearson correlation coefficient) for the psi angle.
Figure 4 Selection of sliding window size with optimized LightGBM regressor (phi angle). Among the tested window sizes, it was found that a window size of 3 achieved the highest 1-MAE+PCC (mean absolute error + Pearson correlation coefficient) for the psi angle.CC BY · PubMed Central
Figure 5 Selection of sliding window size with optimized LightGBM regressor (psi angle). Among the tested window sizes, it was found that a window size of 3 yielded the highest value of 1-MAE+PCC for the psi angle.
Figure 5 Selection of sliding window size with optimized LightGBM regressor (psi angle). Among the tested window sizes, it was found that a window size of 3 yielded the highest value of 1-MAE+PCC for the psi angle.CC BY · PubMed Central
Figure 6 The torsion-angle fluctuation is depicted in its distribution, with the data points divided into 10 bins. The fluctuations of the phi and psi angles are visually represented using red and green colors, respectively.
Figure 6 The torsion-angle fluctuation is depicted in its distribution, with the data points divided into 10 bins. The fluctuations of the phi and psi angles are visually represented using red and green colors, respectively.CC BY · PubMed Central
Figure 7 The relationship between Δφ and Δψ is shown in the figure, revealing that the majority of residues exhibit small fluctuations below 0.2.
Figure 7 The relationship between Δφ and Δψ is shown in the figure, revealing that the majority of residues exhibit small fluctuations below 0.2.CC BY · PubMed Central
Figure 8 Relationship between torsion-angle fluctuation in the phi angle and disordered regions. The disordered probability was obtained from the SPOT-Disordered2 tool. The figure illustrates that regions with low disordered probability exhibit correspondingly low fluctuations in the phi angle, and conversely, regions with high disordered probability show higher fluctuations in the phi angle.
Figure 8 Relationship between torsion-angle fluctuation in the phi angle and disordered regions. The disordered probability was obtained from the SPOT-Disordered2 tool. The figure illustrates that regions with low disordered probability exhibit correspondingly low fluctuations in the phi angle, and conversely, regions with high disordered probability show higher fluctuations in the phi angle.CC BY · PubMed Central
Figure 9 Correlation between torsion-angle fluctuation in the psi angle and the presence of disordered regions. The disordered probability was obtained through the utilization of the SPOT-Disordered2 tool. The figure clearly illustrates that regions with low disordered probability exhibit lower fluctuations in the psi angle, while regions with high disordered probability tend to have higher fluctuations in the psi angle.
Figure 9 Correlation between torsion-angle fluctuation in the psi angle and the presence of disordered regions. The disordered probability was obtained through the utilization of the SPOT-Disordered2 tool. The figure clearly illustrates that regions with low disordered probability exhibit lower fluctuations in the psi angle, while regions with high disordered probability tend to have higher fluctuations in the psi angle.CC BY · PubMed Central

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Metadata

Authors
Kabir, M. W. U., Alawad, D. M., Mishra, A., & Hoque, M. T.
Affiliations
  • LSU New Orleans (US)
  • Texas A&M University – Kingsville (US)
Corresponding author
Hoque, Md Tamjidul
ORCID
Venue
Biology
Publisher
MDPI AG
Published
2023-07-19
Volume
12
Issue
7
Pages
1020
ISSN
2079-7737 (ISSN-L)
DOI
10.3390/biology12071020
PMC
PMC10376102
OpenAlex
W4384929351
Cited by
5
References
3
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 1
FWCI
0.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Biology (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Department of Homeland Security (DHS) (21STSLA00011-01-0)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
phi and psi angles, protein flexibility, sequence-based prediction, LightGBM regression

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CoChat. (2026). Citation check: TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences. https://app.cochat.ai/papers/10.3390/biology12071020
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Kabir, M. W. U., Alawad, D. M., Mishra, A., & Hoque, M. T. (2023). TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences. Biology. https://doi.org/10.3390/biology12071020