1.
Protein-Protein Interactions Prediction Based on Graph Energy and Protein Sequence Information.
Xu, D, Xu, H, Zhang, Y, Chen, W, Gao, R
Molecules (Basel, Switzerland). 2020;(8)
Abstract
Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore, developing new reliable computational approaches has great practical significance for the identification of PPIs. In this paper, a novel prediction method is proposed for predicting PPIs using graph energy, named PPI-GE. Particularly, in the process of feature extraction, we designed two new feature extraction methods, the physicochemical graph energy based on the ionization equilibrium constant and isoelectric point and the contact graph energy based on the contact information of amino acids. The dipeptide composition method was used for order information of amino acids. After multi-information fusion, principal component analysis (PCA) was implemented for eliminating noise and a robust weighted sparse representation-based classification (WSRC) classifier was applied for sample classification. The prediction accuracies based on the five-fold cross-validation of the human, Helicobacter pylori (H. pylori), and yeast data sets were 99.49%, 97.15%, and 99.56%, respectively. In addition, in five independent data sets and two significant PPI networks, the comparative experimental results also demonstrate that PPI-GE obtained better performance than the compared methods.
2.
Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.
Brender, JR, Zhang, Y
PLoS computational biology. 2015;(10):e1004494
Abstract
The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.
3.
Use of thiol-disulfide exchange method to study transmembrane peptide association in membrane environments.
Cristian, L, Zhang, Y
Methods in molecular biology (Clifton, N.J.). 2013;:3-18
Abstract
The development of methods for reversibly folding membrane proteins in a two-state manner remains a considerable challenge for studies of membrane protein stability. In recent years, a variety of techniques have been established and studies of membrane protein folding thermodynamics in the native bilayer environments have become feasible. Here we present the thiol-disulfide exchange method, a promising experimental approach for investigating the thermodynamics of transmembrane (TM) helix-helix association in membrane-mimicking environments. The method involves initiating disulfide cross-linking of a protein under reversible redox conditions in a thiol-disulfide buffer and quantitative assessment of the extent of cross-linking at equilibrium. This experimental method provides a broadly applicable tool for thermodynamic studies of folding, oligomerization, and helix-helix interactions of membrane proteins.