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Updated: May 31, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
Predicting the activity of ACE inhibitory peptides with a novel mode of pseudo amino acid composition
Mao Shu1, Xiaoming Cheng, Yunru Zhang
1School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, PR China.
Abstract:
In this study, physicochemical scale (P-scale), was recruited as a novel set of physicochemical descriptors derived from component analysis on four short of physicochemical properties variables (hydrophobic, electronic, steric and hydrogen bond contribution) of 20 coded amino acids, By using partial least squares (PLS), we applied P-scale for the study of quantitative structure activity relationship models (QASRs) on three angiotensin I converting enzyme (ACE) inhibitory peptides datasets (58 dipeptides, 55 tripeptides, and 50 tetrapeptides).The results of QSARs were superior to that of the earlier studies, with correlation coefficient (r(2)) and cross-validated(q(2)) equal to 0.902, 0.86; 0.985, 0.951 and 0.872, 0.77, respectively. By analysis, hydrophobic and steric properties of ACE-inhibitory peptide sequences play important roles in their bioactivities, and novel peptide sequence could be designed based on these properties of the amino acid residues. These results showed that P-scale descriptors can well represent the peptide sequence. Furthermore, the robust models show that P-scale descriptors can be further expanded for polypeptides and can serve as a useful quantitative tool for the rational drug design and discovery.
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