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A functional feature analysis on diverse protein–protein interactions: application for the prediction of binding affinity

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Abstract

Protein–protein interactions (PPIs) play crucial roles in diverse cellular processes. There are different types of PPIs based on the composition, affinity and whether the association is permanent or transient. Analyzing the diversity of PPIs at the atomic level is crucial for uncovering the key features governing the interactions involved in PPI. A systematic physico-chemical and conformational studies were implemented on interfaces involved in different PPIs, including crystal packing, weak transient heterodimers, weak transient homodimers, strong transient heterodimers and homodimers. The comparative analysis shows that the interfaces tend to be larger, less planar, and more tightly packed with the increase of the interaction strength. Meanwhile the strong interactions undergo greater conformational changes than the weak ones involving main chains as well as side chains. Finally, using 18 features derived from our analysis, we developed a support vector regression model to predict the binding affinity with a promising result, which further demonstrate the reliability of our studies. We believe this study will provide great help in more thorough understanding the mechanism of diverse PPIs.

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Acknowledgments

This work was funded by the National Natural Science Foundation of China (No. 20905054 and 21175095).

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Authors and Affiliations

  1. College of Chemistry, Sichuan University, Chengdu, 610064, Sichuan, People’s Republic of China

    Jiesi Luo, Yanzhi Guo, Yun Zhong, Duo Ma, Wenling Li & Menglong Li

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  1. Jiesi Luo
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  2. Yanzhi Guo
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  3. Yun Zhong
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  4. Duo Ma
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  5. Wenling Li
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Correspondence to Yanzhi Guo or Menglong Li.

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Luo, J., Guo, Y., Zhong, Y. et al. A functional feature analysis on diverse protein–protein interactions: application for the prediction of binding affinity. J Comput Aided Mol Des 28, 619–629 (2014). https://doi.org/10.1007/s10822-014-9746-y

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  • DOI: https://doi.org/10.1007/s10822-014-9746-y

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