Evolution of Organic Solvent-Resistant DNA Polymerases.

The introduction of thermostable polymerases revolutionized the polymerase chain reaction (PCR) and biotechnology. However, many GC-rich genes cannot be PCR-amplified with high efficiency in water, irrespective of temperature. Although polar organic cosolvents can enhance nucleic acid polymerization and amplification by destabilizing duplex DNA and secondary structures, nature has not selected for the evolution of solvent-tolerant polymerase enzymes. Here, we used ultrahigh-throughput droplet-based selection and deep sequencing along with computational free-energy and binding affinity calculations to evolve Taq polymerase to generate enzymes that are both stable and highly active in the presence of organic cosolvents, resulting in up to 10% solvent resistance and over 100-fold increase in stability at 97.5 °C in the presence of 1,4-butanediol, as well as tolerance to up to 10 times higher concentrations of the potent cosolvents sulfolane and 2-pyrrolidone. Using these polymerases, we successfully amplified a broad spectrum of GC-rich templates containing regions with over 90% GC content, including templates recalcitrant to amplification with existing polymerases, even in the presence of cosolvents. We also demonstrated dramatically reduced GC bias in the amplification of genes with widely varying GC content in quantitative polymerase chain reaction (qPCR). By expanding the scope of solvent systems compatible with nucleic acid polymerization, these organic solvent-resistant polymerases enable a dramatic reduction of sequence bias not achievable through thermal resistance alone, with significant implications for a wide range of applications including sequencing and synthetic biology in mixed aqueous-organic media.

Improving aqueous solubility of paclitaxel with polysarcosine-b-poly(γ-benzyl glutamate) nanoparticles.

New stealth amphiphilic copolymers based on polysarcosine (PSar) rather than poly(ethylene glycol) (PEG) have gained more attention for their use as excipients in nanomedicine. In this study, several polysarcosine-b-poly(γ-benzyl glutamate) (PSar-b-PGluOBn) block copolymers were synthesized by ring opening polymerization (ROP) of the respective N-carboxyanhydrides (NCAs) and were characterized by Fourier-transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (1H NMR) and size-exclusion chromatography (SEC). Copolymers had different PGluOBn block configuration (racemic L/D, pure L or pure D), degrees of polymerization of PSar between 28 and 76 and PGluOBn between 9 and 93, molar masses (Mn) between 5.0 and 24.6 kg.mol-1 and dispersities (D) lower than 1.4. Nanoparticles of PSar-b-PGluOBn loaded with paclitaxel (PTX), a hydrophobic anti-cancer drug, were obtained by nanoprecipitation. Their hydrodynamic diameter (Dh) ranged from 27 to 118 nm with polydispersity indexes (PDI) between 0.01 and 0.20, as determined by dynamic light scattering (DLS). Their morphology was more spherical for copolymers with a racemic L/D PGluOBn block configuration synthesized at 5 °C. PTX loading efficiency was between 63 and 92 % and loading contents between 7 and 15 %. Using PSar-b-PGluOBn copolymers as excipients, PTX apparent water-solubility was significantly improved by a factor up to 6600 to 660 µg.mL-1.

Identification of a novel peptide ligand for the cancer-specific receptor mutation EGFRvIII using high-throughput sequencing of phage-selected peptides.

We report here the selection and characterization of a novel peptide ligand using phage display targeted against the cancer-specific epidermal growth factor tyrosine kinase receptor mutation variant III (EGFRvIII). This receptor is expressed in several kinds of cancer: ovarian cancer, breast cancer and glioblastoma, but not in normal tissues. A 12-mer random peptide library was screened against EGFRvIII. Phage-selected peptides were sequenced in high-throughput by next generation sequencing (NGS), and their diversity was studied to identify highly abundant clones expected to bind with the highest affinities to EGFRvIII. The enriched peptides were characterized and their binding capacity towards stable cell lines expressing EGFRvIII, EGFR wild type (EGFR WT), or a low endogenous level of EGFR WT was confirmed by flow cytometry analysis. The best peptide candidate, VLGREEWSTSYW, was synthesized, and its binding specificity towards EGFRvIII was validated in vitro. Additionally, computational docking analysis suggested that the identified peptide binds selectively to EGFRvIII. The novel VLGREEWSTSYW peptide is thus a promising EGFRvIII-targeting agent for future applications in cancer diagnosis and therapy.

Discovery of novel compounds as potent activators of Sirt3.

Among the sirtuin enzymes, Sirt3 is one of the most important deacetylases as it regulates acetylation levels in mitochondria, which are linked to the metabolism of multiple organs and therefore involved in many types of age-related human diseases such as cancer, heart diseases and metabolic diseases. Given the dearth of direct activators of Sirt3, the identification of new modulators could be a key step in the development of new therapeutics. Here we report the discovery of Sirt3 modulators, including activators, through the use of DNA encoded library technology (DEL) and computational high-throughput screening methodologies. Top hits from both screenings against Sirt3 were evaluated according to their activity and affinity. Our best activator is more potent than the previously reported activator Honokiol. Docking studies suggest that our activators identified from virtual screening interact with Sirt3 at a site similar to Honokiol, whereas the activators identified from DEL selection interact with Sirt3 at an atypical site. Our results establish the attractiveness of these high-throughput screening technologies in identifying novel and potent Sirt3 activators and, therefore, in associated therapeutic applications.

Biophysical characterization of hit compounds for mechanism-based enzyme activation.

Across all families of enzymes, only a dozen or so distinct classes of non-natural small molecule activators have been characterized, with only four known modes of activation among them. All of these modes of activation rely on naturally evolved binding sites that trigger global conformational changes. Among the enzymes that are of greatest interest for small molecule activation are the seven sirtuin enzymes, nicotinamide adenine dinucleotide (NAD+)-dependent protein deacylases that play a central role in the regulation of healthspan and lifespan in organisms ranging from yeast to mammals. However, there is currently no understanding of how to design sirtuin-activating compounds beyond allosteric activators of SIRT1-catalyzed reactions that are limited to particular substrates. Here, we introduce a general mode of sirtuin activation that is distinct from the known modes of enzyme activation. Based on the conserved mechanism of sirtuin-catalyzed deacylation reactions, we establish biophysical properties of small molecule modulators that can in principle result in enzyme activation for diverse sirtuins and substrates. Building upon this framework, we propose strategies for the identification, characterization and evolution of hits for mechanism-based enzyme activating compounds.

Molecular system identification for enzyme directed evolution and design.

The rational design of chemical catalysts requires methods for the measurement of free energy differences in the catalytic mechanism for any given catalyst Hamiltonian. The scope of experimental learning algorithms that can be applied to catalyst design would also be expanded by the availability of such methods. Methods for catalyst characterization typically either estimate apparent kinetic parameters that do not necessarily correspond to free energy differences in the catalytic mechanism or measure individual free energy differences that are not sufficient for establishing the relationship between the potential energy surface and catalytic activity. Moreover, in order to enhance the duty cycle of catalyst design, statistically efficient methods for the estimation of the complete set of free energy differences relevant to the catalytic activity based on high-throughput measurements are preferred. In this paper, we present a theoretical and algorithmic system identification framework for the optimal estimation of free energy differences in solution phase catalysts, with a focus on one- and two-substrate enzymes. This framework, which can be automated using programmable logic, prescribes a choice of feasible experimental measurements and manipulated input variables that identify the complete set of free energy differences relevant to the catalytic activity and minimize the uncertainty in these free energy estimates for each successive Hamiltonian design. The framework also employs decision-theoretic logic to determine when model reduction can be applied to improve the duty cycle of high-throughput catalyst design. Automation of the algorithm using fluidic control systems is proposed, and applications of the framework to the problem of enzyme design are discussed.

Dynamics and control of DNA sequence amplification.

DNA amplification is the process of replication of a specified DNA sequence in vitro through time-dependent manipulation of its external environment. A theoretical framework for determination of the optimal dynamic operating conditions of DNA amplification reactions, for any specified amplification objective, is presented based on first-principles biophysical modeling and control theory. Amplification of DNA is formulated as a problem in control theory with optimal solutions that can differ considerably from strategies typically used in practice. Using the Polymerase Chain Reaction as an example, sequence-dependent biophysical models for DNA amplification are cast as control systems, wherein the dynamics of the reaction are controlled by a manipulated input variable. Using these control systems, we demonstrate that there exists an optimal temperature cycling strategy for geometric amplification of any DNA sequence and formulate optimal control problems that can be used to derive the optimal temperature profile. Strategies for the optimal synthesis of the DNA amplification control trajectory are proposed. Analogous methods can be used to formulate control problems for more advanced amplification objectives corresponding to the design of new types of DNA amplification reactions.

Sequence-dependent biophysical modeling of DNA amplification.

A theoretical framework for prediction of the dynamic evolution of chemical species in DNA amplification reactions, for any specified sequence and operating conditions, is reported. Using the polymerase chain reaction (PCR) as an example, we developed a sequence- and temperature-dependent kinetic model for DNA amplification using first-principles biophysical modeling of DNA hybridization and polymerization. We compare this kinetic model with prior PCR models and discuss the features of our model that are essential for quantitative prediction of DNA amplification efficiency for arbitrary sequences and operating conditions. Using this model, the kinetics of PCR is analyzed. The ability of the model to distinguish between the dynamic evolution of distinct DNA sequences in DNA amplification reactions is demonstrated. The kinetic model is solved for a typical PCR temperature protocol to motivate the need for optimization of the dynamic operating conditions of DNA amplification reactions. It is shown that amplification efficiency is affected by dynamic processes that are not accurately represented in the simplified models of DNA amplification that form the basis of conventional temperature cycling protocols. Based on this analysis, a modified temperature protocol that improves PCR efficiency is suggested. Use of this sequence-dependent kinetic model in a control theoretic framework to determine the optimal dynamic operating conditions of DNA amplification reactions, for any specified amplification objective, is discussed.

Mechanism of inhibition of the human sirtuin enzyme SIRT3 by nicotinamide: computational and experimental studies.

Sirtuins are key regulators of many cellular functions including cell growth, apoptosis, metabolism, and genetic control of age-related diseases. Sirtuins are themselves regulated by their cofactor nicotinamide adenine dinucleotide (NAD+) as well as their reaction product nicotinamide (NAM), the physiological concentrations of which vary during the process of aging. Nicotinamide inhibits sirtuins through the so-called base exchange pathway, wherein rebinding of the reaction product to the enzyme accelerates the reverse reaction. We investigated the mechanism of nicotinamide inhibition of human SIRT3, the major mitochondrial sirtuin deacetylase, in vitro and in silico using experimental kinetic analysis and Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PB(GB)SA) binding affinity calculations with molecular dynamics sampling. Through experimental kinetic studies, we demonstrate that NAM inhibition of SIRT3 involves apparent competition between the inhibitor and the enzyme cofactor NAD+, contrary to the traditional characterization of base exchange as noncompetitive inhibition. We report a model for base exchange inhibition that relates such kinetic properties to physicochemical properties, including the free energies of enzyme-ligand binding, and estimate the latter through the first reported computational binding affinity calculations for SIRT3:NAD+, SIRT3:NAM, and analogous complexes for Sir2. The computational results support our kinetic model, establishing foundations for quantitative modeling of NAD+/NAM regulation of mammalian sirtuins during aging and the computational design of sirtuin activators that operate through alleviation of base exchange inhibition.

Sequence-dependent theory of oligonucleotide hybridization kinetics.

A theoretical approach to the prediction of the sequence and temperature-dependent rate constants for oligonucleotide hybridization reactions has been developed based on the theory of relaxation kinetics. One-sided and two-sided melting reaction mechanisms for oligonucleotide hybridization reactions have been considered, analyzed, modified, and compared to select a physically consistent as well as robust model for prediction of the relaxation times of DNA hybridization reactions that agrees with the experimental evidence. The temperature- and sequence-dependent parameters of the proposed model have been estimated using available experimental data. The relaxation time model that we developed has been combined with the nearest neighbor model of hybridization thermodynamics to estimate the temperature- and sequence-dependent rate constants of an oligonucleotide hybridization reaction. The model-predicted rate constants are compared to experimentally determined rate constants for the same oligonucleotide hybridization reactions. Finally, we consider a few important applications of kinetically controlled DNA hybridization reactions.

Mutagenic evidence for the optimal control of evolutionary dynamics.

Elucidating the fitness measures optimized during the evolution of complex biological systems is a major challenge in evolutionary theory. We present experimental evidence and an analytical framework demonstrating how biochemical networks exploit optimal control strategies in their evolutionary dynamics. Optimal control theory explains a striking pattern of extremization in the redox potentials of electron transport proteins, assuming only that their fitness measure is a control objective functional with bounded controls.

Integrated microelectronic device for label-free nucleic acid amplification and detection.

We present an integrated microelectronic device for amplification and label-free detection of nucleic acids. Amplification by polymerase chain reaction (PCR) is achieved with on-chip metal resistive heaters, temperature sensors, and microfluidic valves. We demonstrate a rapid thermocycling with rates of up to 50 degrees C s(-1) and a PCR product yield equivalent to that of a bench-top system. Amplicons within the PCR product are detected by their intrinsic charge with a silicon field-effect sensor. Similar to existing optical approaches with intercalators such as SYBR Green, our sensing approach can directly detect standard double-stranded PCR product, while in contrast, our sensor does not require labeling reagents. By combining amplification and detection on the same device, we show that the presence or absence of a particular DNA sequence can be determined by converting the analog surface potential output of the field-effect sensor to a simple digital true/false readout.

Label-free microelectronic PCR quantification.

We present a robust and simple method for direct, label-free PCR product quantification using an integrated microelectronic sensor. The field-effect sensor can sequentially detect the intrinsic charge of multiple unprocessed PCR products and does not require sample processing or additional reagents in the PCR mixture. The sensor measures nucleic acid concentration in the PCR relevant range and specifically detects the PCR products over reagents such as Taq polymerase and nucleotide monomers. The sensor can monitor the product concentration at various stages of PCR and can generate a readout that resembles that of a real-time fluorescent measurement using an intercalating dye but without its potential inhibition artifacts. The device is mass-produced using standard semiconductor processes, can be reused for months, and integrates all sensing components directly on-chip. As such, our approach establishes a foundation for the direct integration of PCR-based in vitro biotechnologies with microelectronics.

Sequence optimization and designability of enzyme active sites.

We recently found that many residues in enzyme active sites can be computationally predicted by the optimization of scoring functions based on substrate binding affinity, subject to constraints on the geometry of catalytic residues and protein stability. Here, we explore the generality of this surprising observation. First, the impact of hydrogen-bonding networks necessary for catalysis on the accuracy of sequence optimization is assessed; incorporation of these networks, where relevant, into the set of catalytic constraints is found to be essential. Next, the impact of multiple substrate selectivity on sequence optimization is probed by carrying out independent calculations for complexes of deoxyribonucleoside kinases with various cognate ligands, revealing how simultaneous selection pressures determined active-site sequences of these enzymes. Including previous calculations on simpler enzymes, computational sequence optimization correctly predicts 76% of all active-site residues tested (86% correct, with 93% similar, for naturally conserved residues). In these studies, the ligand is fixed in its native conformation. To assess the applicability of these methods to de novo active-site design, the effect of small ligand motions around the native pose is also examined. Robustness of sequence accuracy for topologically similar poses is demonstrated for selected kinases, but not for a model peptidase. Based on these observations, we introduce the notion of the designability of an enzyme active site, a metric that may be used to guide the search for protein scaffolds suitable for the introduction of de novo activity for a desired chemical reaction.

Computational prediction of native protein ligand-binding and enzyme active site sequences.

Recent studies reveal that the core sequences of many proteins were nearly optimized for stability by natural evolution. Surface residues, by contrast, are not so optimized, presumably because protein function is mediated through surface interactions with other molecules. Here, we sought to determine the extent to which the sequences of protein ligand-binding and enzyme active sites could be predicted by optimization of scoring functions based on protein ligand-binding affinity rather than structural stability. Optimization of binding affinity under constraints on the folding free energy correctly predicted 83% of amino acid residues (94% similar) in the binding sites of two model receptor-ligand complexes, streptavidin-biotin and glucose-binding protein. To explore the applicability of this methodology to enzymes, we applied an identical algorithm to the active sites of diverse enzymes from the peptidase, beta-gal, and nucleotide synthase families. Although simple optimization of binding affinity reproduced the sequences of some enzyme active sites with high precision, imposition of additional, geometric constraints on side-chain conformations based on the catalytic mechanism was required in other cases. With these modifications, our sequence optimization algorithm correctly predicted 78% of residues from all of the enzymes, with 83% similar to native (90% correct, with 95% similar, excluding residues with high variability in multiple sequence alignments). Furthermore, the conformations of the selected side chains were often correctly predicted within crystallographic error. These findings suggest that simple selection pressures may have played a predominant role in determining the sequences of ligand-binding and active sites in proteins.

Nanocrystals modified with peptide nucleic acids (PNAs) for selective self-assembly and DNA detection.

Gold nanocrystals modified with peptide nucleic acids (PNAs) have been prepared and applied to self-assembly and DNA sensing. Experiments with different PNA structural motifs show that (1). the versatility in PNA synthetic design can be used to modulate the electrostatic surface properties of nanocrystals, presenting an opportunity to control assembly rate and aggregate size, (2). short (6 base) PNAs can hybridize effectively while attached to nanoparticles, providing a route to generating materials with small interparticle spacings, and (3). the superior base pair mismatch selectivity of PNAs is further enhanced on nanosurfaces, enabling PNA-modified nanoparticles to act as highly selective nanoscale sensors, as well as synthons for defect-free self-assembly. This last feature was coupled with a substantial change in colloidal stability upon DNA hybridization to develop a novel colorimetric DNA assay that detects the presence of single base imperfections within minutes. Various modes of PNA hybridization, including the first practical application of PNA-PNA interactions, were used to direct the assembly of nanoparticles into macroscopic arrangements. Shorter duplex interconnects and greater specificity in assembly were obtained compared to similar experiments with DNA-modified nanocrystals.

Novel sulfoxides facilitate GC-rich template amplification.

Certain organic solvents, such as DMSO and betaine, have been reported to enhance PCR amplification, particularly for hard-to-amplify high-GC templates. As a result of extensive structure-activity studies between two groups of compounds--amides and sulfones--we have recently discovered several other potent PCR enhancers. Here we describe the effects of a series of different sulfoxides on GC-rich template amplification and report several of these to be exceptionally effective, often outperforming DMSO. We introduce them as novel PCR enhancers. We identify tetramethylene sulfoxide as the most potent sulfur-oxygen compound in the enhancement of PCR amplification and as one of the most potent PCR enhancers currently known.