Introduction: Despite many advances in therapeutic and surgical techniques for glioblastoma multiforme (GBM), this form of brain cancer still remains incurable. A hallmark feature of GBM is the ability of the glioma cells to infiltrate surrounding brain tissue. The invasive nature of glioma cells is a key challenge in considering treatment for patients with GBM. Certain members of the matrix metalloproteinase (MMP) family play a role in tumor cell invasion and metastasis (Coussens, et al., 2002). A functional SNP resulting from an additional guanine at position -1607 in the MMP-1 promoter creates an erythroblastosis twenty six transcription factor protein (ETS) DNA consensus binding site, which results in significantly higher transcriptional activity of MMP-1 (Rutter et al., 1998). Several published studies show the incidence of this 2G allele is significantly higher in aggressive and metastatic tumors. Binding of an adjacent transcription factor DNA consensus site, activator protein -1 (AP1) site at -1607 has been shown to cooperate with ETS binding to activate transcription of the MMP-1 gene. We have reported a significant increase in the 2G/2G MMP-1 genotype in glioblastomas (pPurpose: To determine if a novel SNP decoy can inhibit the 2G genotype-dependent increase in MMP-1 transcriptional activity, three specific aims were tested: one, to verify specificity of binding of a transcription factor decoy designed to mimic the -1607 SNP site within the MMP-1 promoter; two, to determine the effect of transcription factor decoy ODN on transcriptional activity of an MMP-1 promoter containing the 2G SNP at -1607; and three, to assess the effect of the transcription factor decoy ODN on MMP-1 mRNA and protein expression in treated glioma cells. Methods: Modified and unmodified decoys were designed to mimic position -1607 to -1593 of the MMP-1 promoter. The SNP decoy contains both ETS and AP1 DNA consensus sites and MMP-1 flanking sequences. We first determined optimal binding conditions with electromobility shift assays (EMSAs). The EMSA assays were used to determine the presence of Ets-1 and AP1 DNA binding activity within the glioma cell lines, T98 and U87. EMSAs were also used to determine if these transcription factors could bind to the MMP-1 promoters with and without the SNP. Lastly, EMSAs were done to determine the binding characteristics of the two modified SNP decoys (LNA-locked nucleic acid, and a PS-phosphothioate modification). The effect of the decoy on MMP-1 transcriptional activity was assessed using a Dual-Luciferase Reporter Assay. The effect of the SNP decoys on mRNA was assessed using quantitative RT-PCR, and on protein expression using a sandwich enzyme-linked immunoassay (ELISAs). Statistical analysis was done using a two-way ANOVA to evaluate the effect of the decoy on MMP-1 transcriptional activity, and protein expression. Results: EMSA results indicate that Ets-1 and AP1 probes, and MMP-1 promoter probes effectively bind proteins from glioma cell nuclear extracts. Addition of excess decoy was able to inhibit protein interactions with the 2G MMP-1 promoter probe and to a lesser extent the 1G promoter probe. The scrambled decoy had no effect. Promoter studies showed a significant increase in transcriptional activity of the 2G promoter and addition of 5 mm PS-SNP decoy could effectively prevent the increase in activity (pConclusions: U87 and T98 cell lines contain DNA binding activity of the transcription factors of interest, namely ETS-1 and AP1. The candidate transcription factors can bind to the MMP-1 promoter in the presence or absence of the 2G. Both the LNA and PS-SNP modified decoys can inhibit nuclear proteins from binding to the MMP-1 2G promoter. The PS-SNP decoy was able to inhibit MMP-1 (2G) gene transcription in a dose dependent manner, whereas the control decoy showed a consistent non-specific effect. The PS-SNP decoy inhibited MMP-1 mRNA and protein expression in glioma cells containing the 2G genotype, and to lesser extent in glioma cells containing the 1G genotype. The results presented here support the conclusion that the chimeric SNP decoy can selectively inhibit the MMP-1 promoter containing the 2G genotype.

Elevated expression of proteins, such as those involved in native antibiotic resistance pathways or introduced to enable biosynthesis of a metabolic engineering target, frequently leads to increased fitness cost. This can result in reduced growth and places selective pressure on cells. In conditions where there is diversity in expression within the population, this can result in cells with higher fitness out-competing their low-fitness counterparts. In the antibiotic resistance context, differential fitness costs caused by antibiotic resistance machinery can be exploited to select against resistant bacteria. However, in biotechnology applications, introducing burdensome synthetic constructs often requires additional engineering to increase genetic stability and maintain production. In this thesis, we investigate the origin of fitness costs and strategies for either exploiting or reducing it, focusing on specific examples related to antibiotic resistance and metabolic engineering. In the resistance work, we study the multiple antibiotic resistance activator MarA and related proteins in Escherichia coli. We quantify the differential fitness cost impacts of salicylate on E. coli antibiotic resistance variants. We demonstrate that salicylate, the natural inducer of MarA, imposes a higher fitness cost on resistant cells compared to the susceptible counterparts, making it possible to bias bacterial population membership towards those cells that are susceptible. In a second study, we focus on the role of salicylate in antibiotic tolerant persister cell formation, finding that salicylate induces reactive oxygen species and consequently persistence. In the metabolic engineering parts of the thesis we first review the mechanisms of fitness cost and existing strategies to ameliorate cost and cell-to-cell variation. Next, we present a technique for reducing fitness cost while maintaining production that takes advantage of transcription factor decoy sites to regulate biosynthesis in E. coli. Using arginine production as a model system, the transcription factor decoy is able to increase production by 16-fold without detectable growth differences. Together, the thesis provides an understanding of the origins and mechanisms of fitness cost in the context of antibiotic resistance and metabolic engineering. It also introduces strategies to exploit fitness costs to select against resistant bacteria and engineering strategies to ameliorate cost while increasing production and genetic stability.

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.