Composition-related structural transition of random peptides: insight into the boundary between intrinsically disordered proteins and folded proteins

Abstract

Previous studies based on bioinformatics showed that there is a sharp distinction of structural features and residue composition between the intrinsically disordered proteins and the folded proteins. What induces such a composition-related structural transition? How do various kinds of interactions work in such processes? In this work, we investigate these problems based on a survey on peptides randomly composed of charged residues (including glutamic acids and lysines) and the residues with different hydrophobicity, such as alanines, glycines, or phenylalanines. Based on simulations using all-atom model and replica-exchange Monte Carlo method, a coil-globule transition is observed for each peptide. The corresponding transition temperature is found to be dependent on the contents of the hydrophobic and charged residues. For several cases, when the mean hydrophobicity is larger than a certain threshold, the transition temperature is higher than the room temperature, and vise versa. These thresholds of hydrophobicity and net charge are quantitatively consistent with the border line observed from the study of bioinformatics. These results outline the basic physical reasons for the compositional distinction between the intrinsically disordered proteins and the folded proteins. Furthermore, the contributions of various interactions to the structural variation of peptides are analyzed based on the contact statistics and the charge-pattern dependence of the gyration radii of the peptides. Our observations imply that the hydrophobicity contributes essentially to such composition-related transitions. Thus, we achieve a better understanding on composition–structure relation of the natural proteins and the underlying physics.

Keywords:

• intrinsically disordered protein
• hydrophobicity
• composition-related structural transition
• conformation
• Monte Carlo simulations

Acknowledgement

We thank the supports from the Natural Science Foundation of China (Nos: 11334004, 11174133, 81421091, 11774157) and 2017 National Training Program of Innovation and Entrepreneurship for Undergraduates (Grant No. 201710929002). We also thank Dr. Andreas Vitalis for the suggestions on the CAMPARI package and Prof. Wen-Fei Li, Dr. Qian-Yuan Tang, Mr. Ke Chen, and Mr. Chenliang Xia for their helps.