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TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences
Version 1
: Received: 30 June 2023 / Approved: 3 July 2023 / Online: 3 July 2023 (08:24:17 CEST)
A peer-reviewed article of this Preprint also exists.
Kabir, M.W.U.; Alawad, D.M.; Mishra, A.; Hoque, M.T. TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences. Biology 2023, 12, 1020. Kabir, M.W.U.; Alawad, D.M.; Mishra, A.; Hoque, M.T. TAFPred: Torsion Angle Fluctuations Prediction from Protein Sequences. Biology 2023, 12, 1020.
Abstract
Protein molecules show varying degrees of flexibility throughout their three-dimensional structures. This flexibility is associated with the fluctuation of the torsion angle in proteins. The protein backbone can be defined mainly by two torsion angles, phi (φ) and psi (ψ). The fluctuation of torsion angles is derived from different NMR models’ variations in backbone torsion angles. The angle fluctuations in the cartesian coordinate space are used to characterize the protein’s structural flexibility. Fluctuation of torsion angle is useful in predicting protein function and structure when the torsion angles are used as restraints. This study aims to develop a machine learning method to predict torsion angle fluctuations directly from protein sequences. It explores various useful features such as disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, monograms, bigram, position-specific estimated energy, half-sphere exposures, etc. Likewise, it explores well-known machine learning methods and proposes an optimized Light Gradient Boosting Machine Regressor (LightGBM) method, named TAFPred, to predict torsion angle fluctuations with the selected features. The proposed method achieves ten-fold cross-validated correlation coefficients of 0.746 and 0.737 and mean absolute errors of 0.114 and 0.123 for the angle fluctuation of φ and ψ, respectively, and attains an improvement of 10.08% in MAE, 24.83% in PCC in the phi angle, and 9.93% in MAE, 22.37% in PCC in psi angle compared to the state-of-the-art method proposed by Zhang et al.
Keywords
backbone torsion angle; torsion angle fluctuations; machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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