Molecular interaction among proteins drives the cellular processes through the formation of complexes that perform the requisite biochemical function. While some of the complexes are obligate (i.e., they fold together while complexation) others are non-obligate, and are formed through macromolecular recognition. Macromolecular recognition in proteins is highly specific, yet it can be both permanent and non permanent in nature. Hallmarks of permanent recognition complexes include large surface of interaction, stabilization by hydrophobic interaction and other noncovalent forces. Several amino acids which contribute critically to the free energy of binding at these interfaces are called as “hot spot” residues. The non permanent recognition complexes, on the other hand, usually show small interface of interaction, with limited stabilization from non covalent forces. For both the permanent and non permanent complexes, the specificity of molecular interaction is governed by the geometric compatibility of the interaction surface, and the noncovalent forces that anchor them. A great deal of studies has already been performed in understanding the basis of protein macromolecular recognition.1; 2 Based on these studies efforts have been made to develop protein-protein docking algorithms that can predict the geometric orientation of the interacting molecules from their individual unbound states. Despite advances in docking methodologies, several significant difficulties remain.1 Therefore, in this thesis, we start with literature review to understand the individual merits and demerits of the existing approaches (Chapter 1),3 and then, we attempt to address some of the problems by developing methods to infer protein quaternary structure from the crystalline state, and improve structural and chemical understanding of protein-protein interactions through biological complex prediction. The understanding of the interaction geometry is the first step in a protein-protein interaction study. Yet, no consistent method exists to assess the geometric compatibility of the interacting interface because of its highly rugged nature. This suggested that new sensitive measures and methods are needed to tackle the problem. We, therefore, developed two new and conceptually different measures using the Delaunay tessellation and interface slice selection to compute the surface complementarity and atom packing at the protein-protein interface (Chapter 2).4 We called these Normalized Surface Complementarity (NSc) and Normalized Interface Packing (NIP). We rigorously benchmarked the measures on the non redundant protein complexes available in the Protein Data Bank (PDB) and found that they efficiently segregate the biological protein-protein contacts from the non biological ones, especially those derived from X-ray crystallography. Sensitive surface packing/complementarity recognition algorithms are usually computationally expensive and thus limited in application to high-throughput screening. Therefore, special emphasis was given to make our measure compute-efficient as well. Our final evaluation showed that NSc, and NIP have very strong correlation among themselves, and with the interface area normalized values available from the Surface Complementarity program (CCP4 Suite: <http://smb.slac.stanford.edu/facilities/software/ccp4/html/sc.html>); but at a fraction of the computing cost. After building the geometry based surface complementarity and packing assessment methods to assess the rugged protein surface, we advanced our goal to determine the stabilities of the geometrically compatible interfaces formed. For doing so, we needed to survey the quaternary structure of proteins with various affinities. The emphasis on affinity arose due to its strong relationship with the permanent and non permanent life-time of the complex. We, therefore, set up data mining studies on two databases named PQS (Protein Quaternary structure database: http://pqs.ebi.ac.uk) and PISA (Protein Interfaces, Surfaces and Assemblies: www.ebi.ac.uk/pdbe/prot_int/pistart.html) that offered downloads on quaternary structure data on protein complexes derived from X-ray crystallographic methods. To our surprise, we found that above mentioned databases provided the valid quaternary structure mostly for moderate to strong affinity complexes. The limitation could be ascertained by browsing annotations from another curated database of protein quaternary structure (PiQSi:5 supfam.mrc-lmb.cam.ac.uk/elevy/piqsi/piqsi_home.cgi) and literature surveys. This necessitated that we at first develop a more robust method to infer quaternary structures of all affinity available from the PDB. We, therefore, developed a new scheme focused on covering all affinity category complexes, especially the weak/very weak ones, and heteromeric quaternary structures (Chapter 3).6 Our scheme combined the naïve Bayes classifier and point-group symmetry under a Boolean framework to detect all categories of protein quaternary structures in crystal lattice. We tested it on a standard benchmark consisting of 112 recognition heteromeric complexes, and obtained a correct recall in 95% cases, which are significantly better than 53% achieved by the PISA,7 a state-of-art quaternary structure detection method hosted at the European Bioinformatics Institute, Hinxton, UK. A few cases that failed correct detection through our scheme, offered interesting insights into the intriguing nature of protein contacts in the lattice. The findings have implications for accurate inference of quaternary states of proteins, especially weak affinity complexes, where biological protein contacts tend to be sacrificed for the energetically optimal ones that favor the formation/stabilization of the crystal lattice. We expect our method to be used widely by all researchers interested in protein quaternary structure and interaction. Having developed a method that allows us to sample all categories of quaternary structures in PDB, we set our goal in addressing the next problem that of accurately determining stabilities of the geometrically compatible protein surfaces involved in interaction. Reformulating the question in terms of protein-protein docking, we sought to ask how we could reliably infer the stabilities of any arbitrary interface that is formed when two protein molecules are brought sterically closer. In a real protein docking exercise this question is asked innumerable times during energy-based screening of thousands of decoys geometrically sampled (through rotation+translation) from the unbound subunits. The current docking methods face problems in two counts: (i), the number of interfaces from decoys to evaluate energies is rather large (64320 for a 9º rotation and translation for a dimeric complex), and (ii) the energy based screening is not quite efficient such that the decoys with native-like quaternary structure are rarely selected at high ranks. We addressed both the problems with interesting results. Intricate decoy filtering approaches have been developed, which are either applied during the search stage or the sampling stage, or both. For filtering, usually statistical information, such as 3D conservation information of the interfacial residues, or similar facts is used; more expensive approaches screen for orientation, shape complementarity and electrostatics. We developed an interface area based decoy filter for the sampling stage, exploiting an assumption that native-like decoys must have the largest, or close to the largest, interface (Chapter 4).8 Implementation of this assumption and standard benchmarking showed that in 91% of the cases, we could recover native-like decoys of bound and unbound binary docking-targets of both strong and weak affinity. This allowed us to propose that “native-like decoys must have the largest, or close to the largest, interface” can be used as a rule to exclude non native decoys efficiently during docking sampling. This rule can dramatically clip the needle-in-a-haystack problem faced in a docking study by reducing >95% of the decoy set available from sampling search. We incorporated the rule as a central part of our protein docking strategy. While addressing the question of energy based screening to rank the native-like decoys at high rank during docking, we came across a large volume of work already published. The mainstay of most of the energy based screenings that avoid statistical potential, involve some form of the Coulomb’s potential, Lennard Jones potential and solvation energy. Different flavors of the energy functions are used with diverse preferences and weights for individual terms. Interestingly, in all cases the energy functions were of the unnormalized form. Individual energy terms were simply added to arrive at a final score that was to be used for ranking. Proteins being large molecules, offer limited scope of applying semi-empirical or quantum mechanical methods for large scale evaluation of energy. We, therefore, developed a de novo empirical scoring function in the normalized form. As already stated, we found NSc and NIP to be highly discriminatory for segregating biological and non biological interface. We, therefore, incorporated them as parameters for our scoring function. Our data mining study revealed that there is a reasonable correlation of -0.73 between normalized solvation energy and normalized nonbonding energy (Coulombs + van der Waals) at the interface. Using the information, we extended our scoring function by combining the geometric measures and the normalized interaction energies. Tests on 30 unbound binary protein-protein complexes showed that in 16 cases we could identify at least one decoy in top three ranks with ≤10 Å backbone root-mean-square-deviation (RMSD) from true binding geometry. The scoring results were compared with other state-of-art methods, which returned inferior results. The salient feature of our scoring function was exclusion of any experiment guided restraints, evolutionary information, statistical propensities or modified interaction energy equations, commonly used by others. Tests on 118 less difficult bound binary protein-protein complexes with ≤35% sequence redundancy at the interface gave first rank in 77% cases, where the native like decoy was chosen among 1 in 10,000 and had ≤5 Å backbone RMSD from true geometry. The details about the scoring function, results and comparison with the other methods are extensively discussed in Chapter 5.9 The method has been implemented and made available for public use as a web server - PROBE (http://pallab.serc.iisc.ernet.in/probe). The development and use of PROBE has been elaborated in Chapter 7.10 On course of this work, we generated huge amounts of data, which is useful information that could be used by others, especially “protein dockers”. We, therefore, developed dockYard (http://pallab.serc.iisc.ernet.in/dockYard) - a repository for protein-protein docking decoys (Chapter 6).11 dockYard offers four categories of docking decoys derived from: Bound (native dimer co-crystallized), Unbound (individual subunits as well as the target are crystallized), Variants (match the previous two categories in at least one subunit with 100% sequence identity), and Interlogs (match the previous categories in at least one subunit with ≥90% or ≥50% sequence identity). There is facility for full or selective download based on search parameters. The portal also serves as a repository to modelers who may want to share their decoy sets with the community. In conclusion, although we made several contributions in development of algorithms for improved protein-protein docking and quaternary structure inference, a lot of challenges remain (Chapter 8). The principal challenge arises by considering proteins as flexible bodies, whose conformational states may change on quaternary structure formation. In addition, solvent plays a major role in the free energy of binding, but its exact contribution is not straightforward to estimate. Undoubtedly, the cost of computation is one of the limiting factors apart from good energy functions to evaluate the docking decoys. Therefore, the next generation of algorithms must focus on improved docking studies that realistically incorporate flexibility and solvent environment in all their evaluations.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / William H. Hsu / The term 'protein-protein interaction (PPI)' refers to the study of associations between proteins as manifested through biochemical processes such as formation of structures, signal transduction, transport, and phosphorylation. PPI play an important role in the study of biological processes. Many PPI have been discovered over the years and several databases have been created to store the information about these interactions. von Mering (2002) states that about 80,000 interactions between yeast proteins are currently available from various high-throughput interaction detection methods. Determining PPI using high-throughput methods is not only expensive and time-consuming, but also generates a high number of false positives and false negatives. Therefore, there is a need for computational approaches that can help in the process of identifying real protein interactions. Several methods have been designed to address the task of predicting protein-protein interactions using machine learning. Most of them use features extracted from protein sequences (e.g., amino acids composition) or associated with protein sequences directly (e.g., GO annotation). Others use relational and structural features extracted from the PPI network, along with the features related to the protein sequence. When using the PPI network to design features, several node and topological features can be extracted directly from the associated graph. In this thesis, important graph features of a protein interaction network that help in predicting protein interactions are identified. Two previously published datasets are used in this study. A third dataset has been created by combining three PPI databases. Several classifiers are applied on the graph attributes extracted from protein interaction networks of these three datasets. A detailed study has been performed in this present work to determine if graph attributes extracted from a protein interaction network are more predictive than biological features of protein interactions. The results indicate that the performance criteria (such as Sensitivity, Specificity and AUC score) improve when graph features are combined with biological features.

The oncoprotein MDM2 has an integral role in cancer development via multiple signalling pathways. Two proteomic mass spectrometry screens, label-free with spectral counting quantitation and 8-plex iTRAQ were used to identify proteins up or downregulated over time by the MDM2 targeting drug Nutlin. A subset of previously identified MDM2 binding partners were identified as altered after Nutlin treatment, along with proteins which have not as yet been linked to MDM2 or p53. Proteins altered two hours after Nutlin treatment were screened for sequence similarity to an MDM2 binding consensus motif based on the BOX-I region of p53. Peptides corresponding to this motif were validated for MDM2 binding, and the mode of binding investigated using competition ELISA and thermal denaturation assays. Known MDM2 ligands such as Nutlin were shown to have a range of effects on the binding of these newly identified MDM2 peptides, which may be attributed to allosteric regulation of MDM2. The effects of Nutlin on two full length proteins identified by the MS screens, CypB and NPM, were confirmed in vivo. In vitro binding of MDM2 to CypB and PK, which contain BOX-I like motifs, was also demonstrated validating proteomic mass spectrometry screens as a method to identify new protein-protein interactions. To further investigate the potential of linear motifs to modulate protein-protein interactions, a peptide aptamer targeting the protein AGR2 was tested for effect on AGR2 and p53 in a cancer cell line.

Human papilloma virus (HPV) is a ubiquitous virus implicated in a growing list of cancers, particularly cervical cancer‐ the second most common cancer among women worldwide. Although persistent infection with high‐risk oncogenic HPVs such as types ‐16 or ‐18 is necessary, additional factors like co‐infection with other viruses can play a role in cancer progression. Protein‐protein interactions play a central role in the infection, survival and proliferation of the virus in the host. Although some interactions of HPV proteins are well characterised, it is essential to discover other key viral interactions to further improve our understanding of the virus and to use this knowledge for the development of newer biomarkers and therapeutics. The aim of this study was to systematically analyse the interactions of HPV‐16 proteins using yeast two‐hybrid (Y2H). To achieve this, a clone collection of the viral proteome was generated by recombinatorial cloning and three independent Y2H screens were performed: (i) Intra‐viral screen to identify interactions among the HPV‐16 proteins; (ii) Inter‐viral screen to identify interactions with proteins of Herpes Simplex Virus (HSV) which is suggested to be a co‐factor; and (iii) Virus‐host screen to identify novel cellular binding partners. The intra‐viral Y2H screen confirmed some of the previously known interactions and also identified binding of the E1 and E7 proteins. Deletion mutagenesis was performed to map the interaction domains to the amino‐terminal 92 amino acids of E1 and carboxy‐terminal CxxC domain of E7. Replication assays suggest a possible repression of E1‐mediated episomal replication by direct binding of E7. The inter‐viral Y2H screen identified interactions of HPV proteins with seventeen HSV‐1 proteins including transcriptional regulator ICP4 and neurovirulance factor ICP34.5. The biological relevance of these interactions in the context of co‐infection is discussed. The virus‐host screen performed against a human cDNA library identified 54 interactions, a subset of which was validated by biochemical pull‐down assays. The functional relevance of an interaction between E7 and a proto‐oncogene spermatogenic leucine zipper protein (SPZ1) was further investigated suggesting a role of SPZ1 in E7‐mediated cell proliferation. The work presented in this thesis identifies several novel interactions of HPV proteins. Future work will involve the in‐depth elucidation of biological relevance of these interactions. In particular, the interactions of E7 with E1 and SPZ1 are of great interest to improve our understanding of the life cycle and pathogenesis of the virus which can be applied for improved strategies of prevention and treatment of malignancies caused by HPV.

The MDM2 and MDM4 oncoproteins are both involved in regulating the tumour suppressor, p53. While the MDM2–p53 interface is structurally and biophysically well characterised, the MDM4-p53 interaction has only recently attracted researchers’ attentions. The goal of this project was to establish structural and chemical ground rules for the disruption of the interactions between the N-terminal domains of MDM2/4 and p53, which is an attractive anticancer strategy. In the current work, successful recombinant production and purification protocols for both the N-terminal domains of MDM2 (i.e. MDM2-N, residues 11-118) and MDM4 (MDM4-N, residues 14-111) have been established, yielding protein in sufficient quantity and quality for analysis using nuclear magnetic resonance spectroscopy (NMR). Two screening strategies were employed to identify small-molecule antagonists of the MDM2-N:p53 interaction. First, a virtual screening exercise identified several compounds that were shown (by NMR) to bind to MDM2-N with μM KDs. Docking studies supported by NMR chemical shift perturbation analysis suggested proposals for binding modes. The results are discussed in relation to the previously reported binding to MDM2-N of well-characterised inhibitors of the MDM2:p53 interaction such as Nutlin-3. Second, a fragment-based library was screened against MDM2-N using TROSY-type NMR spectra to monitor binding. Several hits were identified and the results are discussed with regard to the “druggability” of the MDM2-N p53 interaction. To better understand the p53-binding groove of MDM4-N, multidimensional NMR was used to investigate the structure and backbone dynamics of double-isotopically labelled samples of MDM4-N, both free (i.e. apo-MDM4-N) and in complexes with a p53-derived peptide or Nutlin-3. The apo-MDM4-N is more conformationally dynamic than MDM2, since it contains unstructured regions. These regions appear to become structured upon binding of a ligand. MDM4 appears to bind its ligand through conformational selection and/or an induced fit mechanism involving reorganization of key sub-sites within the binding groove. This study highlighted Abstract differences between Nutlin-3 and peptide binding that suggest the rational design of specific inhibitors of the MDM4:p53 interaction.

published_or_final_version / Biological Sciences / Master / Master of Philosophy

published_or_final_version / Paediatrics and Adolescent Medicine / Doctoral / Doctor of Philosophy

In the past decade, automatisation has led to an immense increase of data in biology. Next generation sequencing techniques will produce a vast amount of sequences across all species in the coming years. In many cases, identifying the function and biological role of a protein from its sequence can be a complicated and time-intensive task. The identification of a protein's interaction partners is a tremendous help for understanding the biological context in which it is involved. In order to fully characterise a protein-protein interaction (PPIs), it is necessary to know the three-dimensional structure of the interacting partners. Despite optimisation efforts from projects such as the Protein Structure Initivative, determining the structure of a protein through crystallography remains a time- and cost-intensive procedure. The primary aim of the research described in this dissertation was to produce a World Wide Web resource that facilitates visual exploration and validation (or questioning) of data derived from functional genomics experiments, by building upon existing structural information about direct physical PPIs. Secondary aims were (i) to demonstrate the utility of the new resource, and (ii) its application in biological research. We created a database that emphasises specifically the intersection between the PPIs-results emerging from the structural biology and functional genomics communities. The BISC database holds BInary SubComplexes and Modellable Interactions in current functional genomics databases (BICS-MI). It is publicly available at hyyp://bisc.cse.ucsc.edu. BISC is divided in three sections that deliver three types of information of interest to users seeking to investigate or browse PPIs. The template section (BISCHom and BISCHet) is devoted to those PPIs that are characterised in structural detail, i.e. binary SCs extracted from experimentally determined three-dimensional structures. BISCHom and BISCHet contain the homodimeric (13,583 records) and heterodimeric (5612 records) portions of these, respectively. Besides interactive, embedded Jmol displays emphasising the interface, standard information and links are provided, e.g. sequence information and SPOP classification for both partners, interface size and energy scores (PISA). An automated launch of the MolSurfer program enables the user to investigate electrostatic and hydrophobic correlation between the partners, at the inter-molecular interface. The modellable interactions section (BISC0MI) identifies potentially modellable interactions in three major functional genomics interaction databases (BioGRID), IntAct, HPRD). To create BISC-MI all PPIs that are amenable to automated homology modelling based on conservative similarity cut-offs and whose partner protein sequences have recrods in the UniProt database, have been extracted. The modellable interaction services (BISC-MI Services) section offers, upon user request, modelled SC-structures for any PPIs in BISC-MI. This is enabled through an untomated template-based (homology) modelling protocol using the popular MODELLER program. First, a multiple sequence alignment (MSA) is generated using MUSCLE, between the target and homologous proteins collected from UniProt (only reviewed proteins from organisms whose genome has been completely sequenced are included to find putative orthologs). Then a sequence-to-profile alignment is generated to integrate the template structure in the MSA. All models are produced upon user request to ensure that the most recent sequence data for the MSAs are used. Models generated through this protocol are expected to be more accurate generally than models offered by other automated resources that rely on pairwise alignments, e.g. ModBase. Two small studies were carried out to demonstrate the usability and utility of BISC in biological research. (1) Interaction data in functional genomics databases often suffers from insufficient experimental and reporting standards. For example, multiple protein complexes are typically recorded as an inferred set of binary interactions. Using the 20S core particle of the yeast proteasome as an example, we demonstrate how the BISC Web resource can be used as a starting point for further investigation of such inferred interactions. (2) Malaria, a mosquito-borne disease, affects 3500-500 million people worldwide. Still very little is known about the malarial parasites' genes and their protein functions. For Plasmodium falciparum, the most lethal among the malaria parasites, only one experimentally derived medium scale PPIs set is available. The validity of this set has been doubted in the the malarial biologist community. We modelled and investigated eleven binary interactions from this set using the BISC modelling pipeline. Alongside we compared the BISC models of the individual partners to those obtained from ModBase.

No description available.

Non-competitive antagonists of the glutamate N-methyl-D-aspartate receptor (NMDAR) induce a broad range of schizophrenia-like symptoms in humans. Consequently hypothesis has emerged suggesting that glutamate or NMDAR hypofunction may occur in schizophrenia. The NMDAR is localised at dendritic spines of neurons and is embedded in a multi-protein complex called the post-synaptic density (PSD). The biochemical composition of the postsynaptic membrane and the structure of dendritic spines are continuously modulated by glutamatergic synaptic activity. The activity-dependent interaction between glutamate receptors and proteins of the PSD stimulate intracellular signalling pathways underlying learning and memory processes. These may be disturbed in schizophrenia. In the present study we hypothesised that molecules of the PSD may be disturbed in expression in the premotor cortex of patients with schizophrenia. Postmortem premotor cortex from patients with schizophrenia, major depressive disorder, bipolar disorder and healthy controls were processed for PSD extraction and purification. Protein expression of the PSD fraction was assessed using co-immunoprecipitation (co-IP) and Western blotting (WB) methods. The expression of NMDAR subunit NR2A, PSD-95, Ca2+/calmodulin-dependent protein kinase II subunit β (CaMKIIβ) and truncated isoform of the tropomyosin receptor kinase type B (TrkB-T1) were significantly reduced in schizophrenia. A significant decrease in the expression of NR2A was also observed in patients with major depressive disorder relative to controls. A decrease in the abundance of key PSD proteins in schizophrenia provides strong evidence that PSD function and possibly synaptic plasticity may be disturbed in the premotor cortex in the disease. There may also be more subtle disturbances in PSD function in major depressive disorder.