PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

Abstract

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA‐binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA‐binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA‐binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA‐binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°–120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function

“…It is calculated from the atomic displacement parameters obtained from X-ray crystallography experiments. The B-factor reflects a protein's atomic vibrations and thermal motion, with higher values indicating greater mobility and lower stability (Wang W et al, 2022). The B-factor is often used to identify regions of a protein that are flexible or disordered (Vander Meersche et al, 2021).…”

Local Structures in Proteins from Microsecond Molecular Dynamics Simulations: A Symmetry-Based Perspective

“…1−3 A central feature of protein dynamics is internal protein mobility. 4,5 Internal/local motions are necessarily spatially restricted/ordered by their immediate protein surroundings. They are characterized by a structural element (the local restrictions), a kinetic element (the type of motion and the pertinent rate constants), and related features of local geometry.…”

“…The dynamic structure of proteins is intimately related to biophysical processes of interest such as ligand-binding, oligomerization, the stability and mixing of biomolecular condensates, phase separation in the cell, and so forth. − A central feature of protein dynamics is internal protein mobility. , Internal/local motions are necessarily spatially restricted/ordered by their immediate protein surroundings. They are characterized by a structural element (the local restrictions), a kinetic element (the type of motion and the pertinent rate constants), and related features of local geometry. , Here, we focus on the structural aspect from a new perspective, using as basis microsecond-long molecular dynamics (MD) simulations.…”

Prediction of Protein B-factor Profiles based on Bidirectional Long Short-Term Memory Network

“…Similarly, in MEDUSA 23 , the evolutionary information extracted from protein homologous sequences and the physicochemical properties of amino acids are used as inputs to the convolutional neural network to assign a flexible class to each protein sequence position. Recently, a flexible predictor named DihProFle 24 was developed to enhance classification accuracy for DNA-binding proteins and coronavirus proteins, using a combination of SVM and fully connected neural networks (FC). In addition, Bramer et al 25 used a combination of convolutional neural networks (CNN) and Multiscale Weighted Colored Graph 26 (MWCG) for regression prediction of B-factor profiles.…”