Sequence specific recognition and cleavage of DNA by synthetic molecules

DNA recognition is an essential biological process responsible for the regulation of cellular functions including protein synthesis and cell division and is implicated in the mechanism of action of some anticancer drugs. Studies directed towards defining the elements responsible for sequence specific DNA recognition through the study of the interactions of synthetic organic ligands with DNA are described.

DNA recognition by poly-N-methylpyrrolecarboxamides was studied by the synthesis and characterization of a series of molecules where the number of contiguous N-methylpyrrolecarboxamide units was increased from 2 to 9. The effect of this incremental change in structure on DNA recognition has been investigated at base pair resolution using affinity cleaving and MPE•Fe(II) footprinting techniques. These studies led to a quantitative relationship between the number of amides in the molecule and the DNA binding site size. This relationship is called the n + 1 rule and it states that a poly-N methylpyrrolecarboxamide molecule with n amides will bind n + 1 base pairs of DNA. This rule is consistent with a model where the carboxamides of these compounds form three center bridging hydrogen bonds between adjacent base pairs on opposite strands of the helix. The poly-N methylpyrrolecarboxamide recognition element was found to preferentially bind poly dA•poly dT stretches; however, both binding site selection and orientation were found to be affected by flanking sequences. Cleavage of large DNA is also described.

One approach towards the design of molecules that bind large sequences of double helical DNA sequence specifically is to couple DNA binding subunits of similar or diverse base pair specificity. Bis-EDTA-distamycin-fumaramide (BEDF) is an octaamide dimer of two tri-N methylpyrrolecarboxamide subunits linked by fumaramide. DNA recognition by BEDF was compared to P7E, an octaamide molecule containing seven consecutive pyrroles. These two compounds were found to recognize the same sites on pBR322 with approximately the same affinities demonstrating that fumaramide is an effective linking element for Nmethylpyrrolecarboxamide recognition subunits. Further studies involved the synthesis and characterization of a trimer of tetra-N-methylpyrrolecarboxamide subunits linked by β-alanine ((P4)_(3)E). This trimerization produced a molecule which is capable of recognizing 16 base pairs of A•T DNA, more than a turn and a half of the DNA helix.

DNA footprinting is a powerful direct method for determining the binding sites of proteins and small molecules on heterogeneous DNA. It was found that attachment of EDTA•Fe(II) to spermine creates a molecule, SE•Fe(II), which binds and cleaves DNA sequence neutrally. This lack of specificity provides evidence that at the nucleotide level polyamines recognize heterogeneous DNA independent of sequence and allows SE•Fe(II) to be used as a footprinting reagent. SE•Fe(II) was compared with two other small molecule footprinting reagents, EDTA•Fe(II) and MPE•Fe(II).

Design, synthesis and characterization of sequence-specific DNA-cleaving metalloproteins

This thesis describes research pursued in two areas, both involving the design and synthesis of sequence specific DNA-cleaving proteins. The first involves the use of sequence-specific DNA-cleaving metalloproteins to probe the structure of a protein-DNA complex, and the second seeks to develop cleaving moieties capable of DNA cleavage through the generation of a non-diffusible oxidant under physiological conditions.

Chapter One provides a brief review of the literature concerning sequence-specific DNA-binding proteins. Chapter Two summarizes the results of affinity cleaving experiments using leucine zipper-basic region (bZip) DNA-binding proteins. Specifically, the NH_2-terminal locations of a dimer containing the DNA binding domain of the yeast transcriptional activator GCN4 were mapped on the binding sites 5'-CTGACTAAT-3' and 5'ATGACTCTT- 3' using affinity cleaving. Analysis of the DNA cleavage patterns from Fe•EDTA-GCN4(222-281) and (226-281) dimers reveals that the NH_2-termini are in the major groove nine to ten base pairs apart and symmetrically displaced four to five base pairs from the central C of the recognition site. These data are consistent with structural models put forward for this class of DNA binding proteins. The results of these experiments are evaluated in light of the recently published crystal structure for the GCN4-DNA complex. Preliminary investigations of affinity cleaving proteins based on the DNA-binding domains of the bZip proteins Jun and Fos are also described.

Chapter Three describes experiments demonstrating the simultaneous binding of GCN4(226-281) and 1-Methylimidazole-2-carboxamide-netropsin (2-ImN), a designed synthetic peptide which binds in the minor groove of DNA at 5'-TGACT-3' sites as an antiparallel, side-by-side dimer. Through the use of Fe•EDTA-GCN4(226-281) as a sequence-specific footprinting agent, it is shown that the dimeric protein GCN4(226-281) and the dimeric peptide 2- ImN can simultaneously occupy their common binding site in the major and minor grooves of DNA, respectively. The association constants for 2-ImN in the presence and in the absence of Fe•EDTA-GCN4(226-281) are found to be similar, suggesting that the binding of the two dimers is not cooperative.

Chapter Four describes the synthesis and characterization of PBA-β-OH-His- Hin(139-190), a hybrid protein containing the DNA-binding domain of Hin recombinase and the putative iron-binding and oxygen-activating domain of the antitumor antibiotic bleomycin. This 54-residue protein, comprising residues 139-190 of Hin recombinase with the dipeptide pyrimidoblamic acid-β-hydroxy-L-histidine (PBA-β-OH-His) at the NH2 terminus, was synthesized by solid phase methods. PBA-β-OH-His-Hin(139- 190) binds specifically to DNA at four distinct Hin binding sites with affinities comparable to those of the unmodified Hin(139-190). In the presence of dithiothreitol (DTT), Fe•PB-β-OH-His-Hin(139-190) cleaves DNA with specificity remarkably similar to that of Fe•EDTA-Hin(139-190), although with lower efficiency. Analysis of the cleavage pattern suggests that DNA cleavage is mediated through a diffusible species, in contrast with cleavage by bleomycin, which occurs through a non-diffusible oxidant.

Algorithmic challenges in green data centers

With data centers being the supporting infrastructure for a wide range of IT services, their efficiency has become a big concern to operators, as well as to society, for both economic and environmental reasons. The goal of this thesis is to design energy-efficient algorithms that reduce energy cost while minimizing compromise to service. We focus on the algorithmic challenges at different levels of energy optimization across the data center stack. The algorithmic challenge at the device level is to improve the energy efficiency of a single computational device via techniques such as job scheduling and speed scaling. We analyze the common speed scaling algorithms in both the worst-case model and stochastic model to answer some fundamental issues in the design of speed scaling algorithms. The algorithmic challenge at the local data center level is to dynamically allocate resources (e.g., servers) and to dispatch the workload in a data center. We develop an online algorithm to make a data center more power-proportional by dynamically adapting the number of active servers. The algorithmic challenge at the global data center level is to dispatch the workload across multiple data centers, considering the geographical diversity of electricity price, availability of renewable energy, and network propagation delay. We propose algorithms to jointly optimize routing and provisioning in an online manner. Motivated by the above online decision problems, we move on to study a general class of online problem named "smoothed online convex optimization", which seeks to minimize the sum of a sequence of convex functions when "smooth" solutions are preferred. This model allows us to bridge different research communities and help us get a more fundamental understanding of general online decision problems.

Towards conditional RNAi : shape and sequence transduction with small conditional RNAs

RNA interference (RNAi) is a powerful biological pathway allowing for sequence-specific knockdown of any gene of interest. While RNAi is a proven tool for probing gene function in biological circuits, it is limited by being constitutively ON and executes the logical operation: silence gene Y. To provide greater control over post-transcriptional gene silencing, we propose engineering a biological logic gate to implement “conditional RNAi.” Such a logic gate would silence gene Y only upon the expression of gene X, a completely unrelated gene, executing the logic: if gene X is transcribed, silence independent gene Y. Silencing of gene Y could be confined to a specific time and/or tissue by appropriately selecting gene X.

To implement the logic of conditional RNAi, we present the design and experimental validation of three nucleic acid self-assembly mechanisms which detect a sub-sequence of mRNA X and produce a Dicer substrate specific to gene Y. We introduce small conditional RNAs (scRNAs) to execute the signal transduction under isothermal conditions. scRNAs are small RNAs which change conformation, leading to both shape and sequence signal transduction, in response to hybridization to an input nucleic acid target. While all three conditional RNAi mechanisms execute the same logical operation, they explore various design alternatives for nucleic acid self-assembly pathways, including the use of duplex and monomer scRNAs, stable versus metastable reactants, multiple methods of nucleation, and 3-way and 4-way branch migration.

We demonstrate the isothermal execution of the conditional RNAi mechanisms in a test tube with recombinant Dicer. These mechanisms execute the logic: if mRNA X is detected, produce a Dicer substrate targeting independent mRNA Y. Only the final Dicer substrate, not the scRNA reactants or intermediates, is efficiently processed by Dicer. Additional work in human whole-cell extracts and a model tissue-culture system delves into both the promise and challenge of implementing conditional RNAi in vivo.

Sequence specific complexation of b DNA at sites containing g,c base pairs

A series of eight related analogs of distamycin A has been synthesized. Footprinting and affinity cleaving reveal that only two of the analogs, pyridine-2- car box amide-netropsin (2-Py N) and 1-methylimidazole-2-carboxamide-netrops in (2-ImN), bind to DNA with a specificity different from that of the parent compound. A new class of sites, represented by a TGACT sequence, is a strong site for 2-PyN binding, and the major recognition site for 2-ImN on DNA. Both compounds recognize the G•C bp specifically, although A's and T's in the site may be interchanged without penalty. Additional A•T bp outside the binding site increase the binding affinity. The compounds bind in the minor groove of the DNA sequence, but protect both grooves from dimethylsulfate. The binding evidence suggests that 2-PyN or 2-ImN binding induces a DNA conformational change.

In order to understand this sequence specific complexation better, the Ackers quantitative footprinting method for measuring individual site affinity constants has been extended to small molecules. MPE•Fe(II) cleavage reactions over a 10^5 range of free ligand concentrations are analyzed by gel electrophoresis. The decrease in cleavage is calculated by densitometry of a gel autoradiogram. The apparent fraction of DNA bound is then calculated from the amount of cleavage protection. The data is fitted to a theoretical curve using non-linear least squares techniques. Affinity constants at four individual sites are determined simultaneously. The distamycin A analog binds solely at A•T rich sites. Affinities range from 10^(6)- 10^(7)M^(-1) The data for parent compound D fit closely to a monomeric binding curve. 2-PyN binds both A•T sites and the TGTCA site with an apparent affinity constant of 10^(5) M^(-1). 2-ImN binds A•T sites with affinities less than 5 x 10^(4) M^(-1). The affinity of 2-ImN for the TGTCA site does not change significantly from the 2-PyN value. At the TGTCA site, the experimental data fit a dimeric binding curve better than a monomeric curve. Both 2-PyN and 2-ImN have substantially lower DNA affinities than closely related compounds.

In order to probe the requirements of this new binding site, fourteen other derivatives have been synthesized and tested. All compounds that recognize the TGTCA site have a heterocyclic aromatic nitrogen ortho to the N or C-terminal amide of the netropsin subunit. Specificity is strongly affected by the overall length of the small molecule. Only compounds that consist of at least three aromatic rings linked by amides exhibit TGTCA site binding. Specificity is only weakly altered by substitution on the pyridine ring, which correlates best with steric factors. A model is proposed for TGTCA site binding that has as its key feature hydrogen bonding to both G's by the small molecule. The specificity is determined by the sequence dependence of the distance between G's.

One derivative of 2-PyN exhibits pH dependent sequence specificity. At low pH, 4-dimethylaminopyridine-2-carboxamide-netropsin binds tightly to A•T sites. At high pH, 4-Me_(2)NPyN binds most tightly to the TGTCA site. In aqueous solution, this compound protonates at the pyridine nitrogen at pH 6. Thus presence of the protonated form correlates with A•T specificity.

The binding site of a class of eukaryotic transcriptional activators typified by yeast protein GCN4 and the mammalian oncogene Jun contains a strong 2-ImN binding site. Specificity requirements for the protein and small molecule are similar. GCN4 and 2-lmN bind simultaneously to the same binding site. GCN4 alters the cleavage pattern of 2-ImN-EDTA derivative at only one of its binding sites. The details of the interaction suggest that GCN4 alters the conformation of an AAAAAAA sequence adjacent to its binding site. The presence of a yeast counterpart to Jun partially blocks 2-lmN binding. The differences do not appear to be caused by direct interactions between 2-lmN and the proteins, but by induced conformational changes in the DNA protein complex. It is likely that the observed differences in complexation are involved in the varying sequence specificity of these proteins.

Sequence-function relationships in E. coli transcriptional regulation

Understanding how transcriptional regulatory sequence maps to regulatory function remains a difficult problem in regulatory biology. Given a particular DNA sequence for a bacterial promoter region, we would like to be able to say which transcription factors bind there, how strongly they bind, and whether they interact with each other and/or RNA polymerase, with the ultimate objective of integrating knowledge of these parameters into a prediction of gene expression levels. The theoretical framework of statistical thermodynamics provides a useful framework for doing so, enabling us to predict how gene expression levels depend on transcription factor binding energies and concentrations. We used thermodynamic models, coupled with models of the sequence-dependent binding energies of transcription factors and RNAP, to construct a genotype to phenotype map for the level of repression exhibited by the lac promoter, and tested it experimentally using a set of promoter variants from E. coli strains isolated from different natural environments. For this work, we sought to ``reverse engineer'' naturally occurring promoter sequences to understand how variations in promoter sequence affects gene expression. The natural inverse of this approach is to ``forward engineer'' promoter sequences to obtain targeted levels of gene expression. We used a high precision model of RNAP-DNA sequence dependent binding energy, coupled with a thermodynamic model relating binding energy to gene expression, to predictively design and verify a suite of synthetic E. coli promoters whose expression varied over nearly three orders of magnitude.

However, although thermodynamic models enable predictions of mean levels of gene expression, it has become evident that cell-to-cell variability or ``noise'' in gene expression can also play a biologically important role. In order to address this aspect of gene regulation, we developed models based on the chemical master equation framework and used them to explore the noise properties of a number of common E. coli regulatory motifs; these properties included the dependence of the noise on parameters such as transcription factor binding strength and copy number. We then performed experiments in which these parameters were systematically varied and measured the level of variability using mRNA FISH. The results showed a clear dependence of the noise on these parameters, in accord with model predictions.

Finally, one shortcoming of the preceding modeling frameworks is that their applicability is largely limited to systems that are already well-characterized, such as the lac promoter. Motivated by this fact, we used a high throughput promoter mutagenesis assay called Sort-Seq to explore the completely uncharacterized transcriptional regulatory DNA of the E. coli mechanosensitive channel of large conductance (MscL). We identified several candidate transcription factor binding sites, and work is continuing to identify the associated proteins.

Model predictive control for deferrable loads scheduling

Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.

In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.