| dc.description.abstract | Proteins play crucial roles in many biological processes like signalling, catalysis of metabolic processes, immune systems, and transporting molecules. To perform this wide range of functions, proteins interact with other biomolecules. Therefore, the characterization of a protein-protein interface is vital in understanding their binding affinity, function, and so forth and is of utmost importance in their experimental mutagenesis studies, predicting protein-protein networks, designing drug targets, and engineering proteins. There are several experimental methods, like X-ray crystallography, Nuclear Magnetic Resonance (NMR), and so forth, for identifying the interface residues in a complex. However, these experimental methods are time-consuming, labour-intensive, and have associated challenges like the protein not being amenable to experimental conditions for structure determination, difficulty in getting high-quality crystals, and purification and expression of protein samples prone to aggregation. Hence, various computational approaches for the same have become crucial in complementing these experimental approaches.
The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The first study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, our study recommends applying both distance and ASA methods so that some interface residues missed by either method but are crucial to the stability, recognition, dynamics, and function of protein–protein complexes are identified in a complementary manner.
Hotspots are interfacial residues in protein-protein complexes that contribute significantly to complex stability. In the second project, we introduce the concept of secondary shell hotspots, which are hotspots uniquely identified by the distance-based approach, staying buried in both the bound and isolated forms of the protein and yet forming direct interactions with the partner protein. From the analysis of the dataset curated from docking benchmark dataset v5.5, we find that secondary shell hotspots are more evolutionarily conserved and have higher Chou-Fasman propensities for hydrophobic and long chain residues and have distinct interaction patterns compared to other hotspots. From detailed case study analyses, we observe that the interaction network formed by the secondary shell hotspots is crucial for complex stability and activity, and they are potentially allosteric propagators that bridge interfacial and non-interfacial sites in the protein. Their mutations to any other amino acid types cause significant destabilization. Overall, this study sheds light on the uniqueness and importance of secondary shell hotspots in protein-protein complexes. Impaired PPIs can cause many diseases, such as neurological disorders and cancer. Moreover, the conserved structure of hotspots and their tremendous impact on the binding energy have made them attractive medical targets for designing inhibitor drugs. Such inhibitors can avoid unwanted protein-protein interactions and more effectively treat various diseases. Such drugs are designed by targeting hotspots with virtual ligand screening and template-directed combinatorial chemistry. In addition, prior knowledge of hotspot residues has been extensively employed in protein−protein docking. Hence, this study on secondary shell hotspots would help design drugs and provide better restraints for docking analyses.
The learnings from the first two projects have been applied to different collaborative projects. We developed a machine learning-based algorithm to predict the interacting pair of residues given the unbound structures of the constituent proteins in the complex (with Mr. Adithyan Unni). We used the docking benchmark dataset v5.5 to train the model using the CatBoost algorithm that applies gradient-boosting on decision trees. Our model exhibits performance comparable with other state-of-the-art methods. In another study, the experimental studies conducted by Prof. Ravi Sundaresan and the team showed that the NAD+-dependent protein deacetylase, SIRT6, has a crucial role in negatively regulating fatty acid uptake in cardiomyocytes. This is achieved by transcriptionally regulating the fatty acid transporters through SIRT6’s binding with the transcription factor PPARγ. Hence, we derived an in silico docked model of the SIRT6/PPARγ complex and suggested the most likely binding pose. Overall, the study could aid in exploiting SIRT6 as a potential therapeutic target for protecting the heart from metabolic diseases. In another collaborative project on toxin-antitoxins systems in Mycobacterium tuberculosis (Mtb), the growth inhibition and rescue experiments by Prof. Ramandeep Singh and the team showed the possibility of cross-talk between specific RelE toxins and specific VapB antitoxins. Through sequence and structure-based in silico analyses, we could identify certain residues that could potentially be important at the non-cognate interface. Further, using in silico docking, we derived models for the non-cognate complexes and suggested a few mutations that could possibly disrupt this cross-talk. Overall, we hope the studies conducted as part of this thesis help better understand the interface of protein-protein complexes and further aid in designing experimental mutagenesis with a wide range of applications in drug design, predicting protein signalling, and 3D modelling of large macromolecular complexes. | en_US |
