Prediction of Phi and Psi Angle Fluctuations from Protein Sequences

Protein molecules differ in flexibility across their three-dimensional configurations. The phi ($\varphi$) and psi ($\psi$) torsion angles primarily define the protein backbone. The flexibility of proteins is related to the fluctuation of the torsion angle. The fluctuation of torsion angles is caused by the differences in backbone torsion angles between various NMR models. The angle fluctuations in the cartesian coordinate space are utilized to define the structural flexibility of proteins and help predict protein function and structure when the torsion angles are employed as constraints. This research attempts to develop a machine-learning method for directly predicting fluctuations in the torsion angle of protein sequences. We collect a number of helpful characteristics of proteins, including disorder probability, position-specific scoring matrix profiles, secondary structure probabilities, monograms, bigrams, position-specific estimated energy, half-sphere exposures. Similarly, we explore well-known machine learning algorithms and present an optimized Light Gradient Boosting Machine Regressor (LightGBM) method, named TAFPred, to predict torsion angle variations using the selected features. The proposed method achieves ten-fold cross-validated correlation coefficients of0.746 and 0.737, as well as mean absolute errors of 0.114 and 0.123, for the angle fluctuation of $\varphi$ and $\psi$, respectively, and an improvement of 6.59% in MAE, 24.50% in PCC in the phi angle, and 6.09% in MAE, 21.84% in PCC in the psi angle, compared to the state-of-the-art method proposed by Zhang et al.