Protein-protein interactions: a structural bioinformatics approach

Thesis by publication.Bibliography: pages 126-151.Chapter 1. Introduction -- Chapter 2. Methods and applications -- Chapter 3. Protein interface residue-level classes and their discriminatory structural features -- Chapter 4. Protein interfaces and biological function -- Chapter 5. Dissecting interfaces of interacting proteins : integrin αvβ6.uPAR interactions -- Chapter 6. Conclusions and future directions.Molecular function in cellular processes is governed by protein-protein interactions (PPIs). With the exponential growth of PPI in the drug discovery field, understanding the key principles governing PPI is of immense current interest. Investigation of protein interfaces of known complexes is an important step towards understanding the molecular basis of PPIs. The overall objective of this thesis is to study known PPI complexes from the Protein Data Bank (PDB) using computational tools to capture their driving force and relate structural interfaces to their biological functions. PPI features, analysis data and conclusions drawn are documented to facilitate prediction of interaction sites and partners and also to facilitate prediction of potential protein function of novel complexes.The interface features were analysed for all non-redundant protein heterodimers (278) in the PDB. The relative interface-surface polarities of each complex in the dataset were estimated to understand predominant forces driving binding. Structural analysis revealed two classes of interfaces - class A with less polar residues and class B with more polar residues, at the interface than the rest of the surface. Five distinguishing features (interface area, interface property abundance, interface charged residues, solvation free energy gain, binding energy) among these classes were identified. These results verify the need for classification of complexes based on residue-level properties in determining the features driving binding. Also, all functional categories are represented in the interface classes. This led to the study on relating structural features to their biological functions.PPIs are essential for catalysis, regulation, assembly, immunity and inhibition in a cell. However, it is unclear whether structural features can define protein functionality. Therefore, analysis of non-redundant protein complexes has been carried out to determine the structural basis for functional preferences. Structural interface of each complex has been characterized using a range of physico-chemical properties. The dataset is grouped using known function for molecular preferences. Five interface features (interface area, interface property abundance, hydrogen bonds, salt bridges, solvation free energy gain, and binding energy) are observed to be significantly different among functional groups.Preliminary application of using PPIs for the characterisation of protein interfaces in integrin αvβ6 heterodimer and its interactions with other proteins especially urokinase plasminogen activator receptor (uPAR) is carried out. The integrin αvβ6•uPAR interaction promotes cancer progression. Therefore, a comprehensive analysis of αvβ6 using modelling data and docking simulations helped gain insights into binding of αvβ6 with uPAR suggesting an interaction site. These results provide preliminary evidence for potential targets in cancer therapies.In conclusion, the work presented in this thesis investigates interface features of known protein complexes to gain insights into the binding principles of PPIs. Structural analysis of heterodimer dataset and grouping complexes based on interface classes and functional groups lead to the identification of discriminatory features amongst these groups. Incorporation of these combinatorial features is necessary to develop models for PPI prediction and analysis, and also in utilizing PPI information for the prediction of potential functions in future studies. Novel observations using modelling and docking data to obtain significant information on key PPIs (involved in cancer) are discussed.Mode of access: World wide web1 online resource (xvi, 161 pages) colour illustration

Protein-protein interactions and prediction : a comprehensive overview

Molecular function in cellular processes is governed by protein-Protein interactions (PPIs) within biological networks. Selective yet specific association of these protein partners contributes to diverse functionality such as catalysis, regulation, assembly, immunity, and inhibition in a cell. Therefore, understanding the principles of protein-Protein association has been of immense interest for several decades. We provide an overview of the experimental methods used to determine PPIs and the key databases archiving this information. Structural and functional information of existing protein complexes confers knowledge on the principles of PPI, based on which a classification scheme for PPIs is then introduced. Obtaining high-quality non-redundant datasets of protein complexes for interaction characterisation is an essential step towards deciphering their underlying binding principles. Analysis of physicochemical features and their documentation has enhanced our understanding of the molecular basis of protein-Protein association. We describe the diverse datasets created/collected by various groups and their key findings inferring distinguishing features. The currently available interface databases and prediction servers have also been compiled.11 page(s

Discrete structural features among interface residue-level classes

Background: Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. Results: Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. Conclusions: Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.8 page(s

Linking structural features of protein complexes and biological function

Protein–protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure–function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.9 page(s

Short peptide vaccine design and development : promises and challenges

Vaccine development for viral diseases is a challenge where subunit vaccines are often ineffective. Therefore, the need for alternative solutions is crucial. Thus, short peptide vaccine candidates promise effective answers under such circumstances. Short peptide vaccine candidates are linear T-cell epitopes (antigenic determinants that are recognized by the immune system) that specifically function by binding human leukocyte antigen (HLA) alleles of different ethnicities (including Black, Caucasian, Oriental, Hispanic, Pacific Islander, American Indian, Australian aboriginal, and mixed ethnicities). The population-specific allele-level HLA sequence data in the public IMGT/HLA database contains approximately 12542 nomenclature defined class I (9437) and class II (3105) HLA alleles as of March 2015 present in several ethnic populations.The bottleneck in short peptide vaccine design and development is HLA polymorphism on the one hand and viral diversity on the other hand. Hence, a crucial step in its design and development is HLA allele-specific binding of short antigen peptides. This is usually combinatorial and computationally labor intensive. Mathematical models utilizing structure-defined pockets are currently available for class I and class II HLA-peptide-binding peptides. Frameworks have been developed to design protocols to identify the most feasible short peptide cocktails as vaccine candidates with superantigen properties among known HLA supertypes. This approach is a promising solution to develop new viral vaccines given the current advancement in T-cell immuno-informatics, yet challenging in terms of prediction efficiency and protocol development.14 page(s

Characterization of the interaction between heterodimeric αvβ6 integrin and urokinase plasminogen activator receptor (uPAR) using functional proteomics

Urokinase plasminogen activator receptor (uPAR) and the epithelial integrin αvβ6 are thought to individually play critical roles in cancer metastasis. These observations have been highlighted by the recent discovery (by proteomics) of an interaction between these two molecules, which are also both implicated in the epithelial-mesenchymal transition (EMT) that facilitates escape of cells from tissue barriers and is a common signature of cancer metastases. In this study, orthogonal in cellulo and in vitro functional proteomic approaches were used to better characterize the uPAR·αvβ6 interaction. Proximity ligation assays (PLA) confirmed the uPAR·αvβ6 interaction on OVCA429 (ovarian cancer line) and four different colon cancer cell lines including positive controls in cells with de novo β6 subunit expression. PLA studies were then validated using peptide arrays, which also identified potential physical sites of uPAR interaction with αvβ6, as well as verifying interactions with other known uPAR ligands (e.g., uPA, vitronectin) and individual integrin subunits (i.e., αv, β1, β3, and β6 alone). Our data suggest that interaction with uPAR requires expression of the complete αβ heterodimer (e.g., αvβ6), not individual subunits (i.e., αv, β1, β3, or β6). Finally, using in silico structural analyses in concert with these functional proteomics studies, we propose and demonstrate that the most likely unique sites of interaction between αvβ6 and uPAR are located in uPAR domains II and III.9 page(s