NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Edward Shih,Ming-Jing Hwang

doi:10.3390/biology4020282

Edward Shih, Ming-Jing Hwang

Open Access

https://doi.org/10.3390/biology4020282

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Journal: Biology	Publication Date: Mar 24, 2015
Citations: 75	License type: CC BY 4.0

Affiliation: Institute of Biomedical Sciences, Academia Sinica

Abstract

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.

Highlights

Living cells are a crowded environment in which most proteins interact with other proteins to exert cellular functions
Despite the low success rates of NPPD at a low N, it is interesting that, as shown in Table 1, many of the complexes that NPPD succeeded at predicting were different from those predicted by IRAD and vice versa
Since many factors can affect the performance of protein-protein docking (PPD) scoring functions, one example being the evaluation of docking poses produced by different sampling methods as mentioned above, it was important to evaluate NPPD further

Summary

Introduction

Living cells are a crowded environment in which most proteins interact with other proteins to exert cellular functions. Owing to the appeal of network analysis in the era of post-genomics research, there has been an increase in the number of studies utilizing AANs to predict a protein’s functional sites [45,46,47], protein-protein [48,49,50,51] and protein-nucleic acid interaction [52,53], and to probe protein dynamics [42,54,55], folding [56,57,58] and structure [59,60,61,62,63] Of these studies using AANs, two reports by Pons et al [37] and Chang et al [38] on PPD are directly relevant to the present work.

Experimental Section

Amino Acid Networks and Network Parameters

Bayesian Network

Performance of NPPD and IRAD

Comparison with Other Network-Based Methods

Some Limitations and Prospects

Conclusions