The effect of the electric field on the conformational properties of the protein 1BBL was investigated by molecular dynamics simulations. Our simulation results clearly capture the structural transitions of the protein sample from helix to turn or random coil conformation induced by the increasing strength of the electric field. During our analysis, we found that the conformational stability is weakened, and the protein sample is stretched as an unfolded structure when it was exposed in a sufficiently high electric field. The characteristic time when the jump occurs in the time evolution curves of root mean square deviation (RMSD) and radius of gyration R_g decreases with increasing electric strength, which demonstrates the rapidly conformational transition that occurs. The number of intra-protein hydrogen bonds, which is the key factor for stabilizing the protein structure, is related to the overall size of the protein. The value of the dipole moment and characteristic time are both influenced by the strength, but are independent of the direction of the external field. The protein sample becomes rotated with the electric field direction. These conclusions provide a theoretical realization of understanding the protein conformational transition in an electric field and the guidance for anticipative applications.

Molecular dynamics (MD) simulation was used to study the influence of electric field on Glucagon-like Peptide-2 (GLP-2). Different electric field strengths (0 V/nm ≤ E ≤ 1 V/nm) were mainly carried out on GLP-2. The structural changes in GLP-2 were analyzed by the Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), Solvent Accessible Surface Area (SASA), Secondary Structure and the number of hydrogen bonds. The stable α—helix structure of GLP-2 was unwound and transformed into an unstable Turn and Coil structure since the stability of the GLP-2 protein structure was reduced under the electric field. Our results show that the degree of unwinding of the GLP-2 structure was not linearly related to the electric field intensity. E = 0.5 V/nm was a special point where the degree of unwinding of the GLP-2 structure reached the maximum at this electric field strength. Under a weak electric field, E < 0.5 V/nm, the secondary structure of GLP-2 becomes loose, and the entropy of the chain increases. When E reaches a certain value (E > 0.5 V/nm), the electric force of the charged residues reaches equilibrium, along the z-direction. Considering the confinement of moving along another direction, the residue is less free. Thus, entropy decreases and enthalpy increases, which enhance the interaction of adjacent residues. It is of benefit to recover hydrogen bonds in the middle region of the protein. These investigations, about the effect of an electric field on the structure of GLP-2, can provide some theoretical basis for the biological function of GLP-2 in vivo.

Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants is still a bottleneck in protein engineering. In this paper, a new sequence‐activity relationship method was analyzed for its application in improving the thermal stability of Aspergillus terreus (R)‐ω‐selective amine transaminase. The experimental data from 6 single‐point mutated enzymes were used as a learning dataset to build models and predict the thermostability of 2⁶ mutants. Based on digital signal processing (DSP), this method digitized the amino acid sequence of proteins by fast Fourier transform (FFT) and then established the best model applying partial least squares regression (PLSR) to screen out all possible mutants, especially those with high performance. In protein engineering, the innovative sequence activity relationship (ISAR) method can make a reasonable prediction using limited experimental data and significantly reduce the experimental cost. The half‐life (T_1/2) of (R)‐ω‐transaminase was fitted with the amino acid sequence by the ISAR algorithm, resulting in an R² of 0.8929 and a cvRMSE of 4.89. At the same time, the mutants with higher T_1/2 than the existing ones were predicted, laying the groundwork for better (R)‐ω‐transaminase in the later stage. The ISAR algorithm is expected to provide a new technique for protein evolution and screening.