Predicting Protein-Protein Interactions Using BiGGER: Case Studies

Docking-Based Prediction of Peptide Binding to MHC Proteins

Major histocompatibility complex (MHC) proteins are the most polymorphic and polygenic proteins in humans. They bind peptides, derived from cleavage of different pathogenic antigens, and are responsible for presenting them to T cells. The peptides recognized by the T cell receptors are denoted as epitopes and they trigger an immune response.In this chapter, we describe a docking protocol for predicting the peptide binding to a given MHC protein using the software tool GOLD. The protocol starts with the construction of a combinatorial peptide library used in the docking and ends with the derivation of a quantitative matrix (QM) accounting for the contribution of each amino acid at each peptide position.

Identifying protein complexes based on an edge weight algorithm and core-attachment structure

Background

Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.

Results

In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy.

Conclusions

In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA.

Reduction of hydrogen peroxide in gram-negative bacteria - bacterial peroxidases

Bacteria display an array of enzymes to detoxify reactive oxygen species that cause damage to DNA and to other biomolecules leading to cell death. Hydrogen peroxide is one of these species, with endogenous and exogenous sources, such as lactic acid bacteria, oxidative burst of the immune system or chemical reactions at oxic-anoxic interfaces. The enzymes that detoxify hydrogen peroxide will be the focus of this review, with special emphasis on bacterial peroxidases that reduce hydrogen peroxide to water. Bacterial peroxidases are periplasmic cytochromes with either two or three c-type haems, which have been classified as classical and non-classical bacterial peroxidases, respectively. Most of the studies have been focus on the classical bacterial peroxidases, showing the presence of a reductive activation in the presence of calcium ions. Mutagenesis studies have clarified the catalytic mechanism of this enzyme and were used to propose an intramolecular electron transfer pathway, with far less being known about the intermolecular electron transfer that occurs between reduced electron donors and the enzyme. The physiological function of these enzymes was not very clear until it was shown, for the non-classical bacterial peroxidase, that this enzyme is required for the bacteria to use hydrogen peroxide as terminal electron acceptor under anoxic conditions. These non-classical bacterial peroxidases are quinol peroxidases that do not require reductive activation but need calcium ions to attain maximum activity and share similar catalytic intermediates with the classical bacterial peroxidases.

Topography of human cytochrome b5/cytochrome b5 reductase interacting domain and redox alterations upon complex formation

Cytochrome b₅ is the main electron acceptor of cytochrome b₅ reductase. The interacting domain between both human proteins has been unidentified up to date and very little is known about its redox properties modulation upon complex formation. In this article, we characterized the protein/protein interacting interface by solution NMR and molecular docking. In addition, upon complex formation, we measured an increase of cytochrome b₅ reductase flavin autofluorescence that was dependent upon the presence of cytochrome b_5. Data analysis of these results allowed us to calculate a dissociation constant value between proteins of 0.5±0.1μM and a 1:1 stoichiometry for the complex formation. In addition, a 30mV negative shift of cytochrome b₅ reductase redox potential in presence of cytochrome b₅ was also measured. These experiments suggest that the FAD group of cytochrome b₅ reductase increase its solvent exposition upon complex formation promoting an efficient electron transfer between the proteins.

Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks

Protein complexes play significant roles in cellular processes. Identifying protein complexes from protein-protein interaction (PPI) networks is an effective strategy to understand biological processes and cellular functions. A number of methods have recently been proposed to detect protein complexes. However, most of methods predict protein complexes from static PPI networks, and usually overlook the inherent dynamics and topological properties of protein complexes. In this paper, we proposed a novel method, called NABCAM (Neighbor Affinity-Based Core-Attachment Method), to identify protein complexes from dynamic PPI networks. Firstly, the centrality score of every protein is calculated. The proteins with the highest centrality scores are regarded as the seed proteins. Secondly, the seed proteins are expanded to complex cores by calculating the similarity values between the seed proteins and their neighboring proteins. Thirdly, the attachments are appended to their corresponding protein complex cores by comparing the affinity among neighbors inside the core, against that outside the core. Finally, filtering processes are carried out to obtain the final clustering result. The result in the DIP database shows that the NABCAM algorithm can predict protein complexes effectively in comparison with other state-of-the-art methods. Moreover, many protein complexes predicted by our method are biologically significant.