Wet-spinning of amyloid protein nanofibers into multifunctional high-performance biofibers.

Amyloid nanofibers derived from hen egg white lysozyme were processed into macroscopic fibers in a wet-spinning process based on interfacial polyion complexation using a polyanionic polysaccharide as cross-linker. As a result of their amyloid nanostructure, the hierarchically self-assembled protein fibers have a stiffness of up to 14 GPa and a tensile strength of up to 326 MPa. Fine-tuning of the polyelectrolytic interactions via pH allows to trigger the release of small molecules, as demonstrated with riboflavin-5'-phophate. The amyloid fibrils, highly oriented within the gellan gum matrix, were mineralized with calcium phosphate, mimicking the fibrolamellar structure of bone. The formed mineral crystals are highly oriented along the nanofibers, thus resulting in a 9-fold increase in fiber stiffness.

SERS opens a new way in aptasensor for protein recognition with high sensitivity and selectivity

SERS aptasensors for protein recognition based on Au nanoparticles labeled with aptamers and Raman reporters have been developed, which opens a new way for protein recognition of high sensitivity and selectivity.

High resolution mass spectrometric alveolar proteomics: Identification of surfactant protein SP-A and SP-D modifications in proteinosis and cystic fibrosis patients

In the present study, one- and two-dimensional gel electrophoresis combined with high resolution Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) have been applied as powerful approaches for the proteome analysis of surfactant proteins SP-A and SP-D, including identification of structurally modified and truncation forms, in bronchoalveolar lavage fluid from patients with cystic fibrosis, chronic bronchitis and pulmonary alveolar proteinosis. Highly sensitive micro preparation techniques were developed for matrix-assisted laser desorption/ionization (MALDI) FT-ICR MS analysis which provided the identification of surfactant proteins at very low levels. Owing to the high resolution, FT-ICR MS was found to provide substantial advantages for the structural identification of surfactant proteins from complex biological matrices with high mass determination accuracy. Several protein bands corresponding to SP-A and SP-D were identified by MALDI-FT-ICR MS after electrophoretic separation by one- and two-dimensional gel electrophoresis, and provided the identification of structural modifications (hydroxy-proline) and degradation products.

Drug-protein interaction studied by technique of microdialysis combined with high performance liquid chromatography

Drug-protein binding is an important process in determining the activity and fate of a pharmaceutical agent once it has entered the body. This review examines the method of microdialysis combined with high-performance liquid chromatography (HPLC) that has been developed;by ours to study such interactions, in which the microdialysis was applied to sample the free drug in the mixed solution of drug with protein, and HPLC to quantify the concentration of free drug in the microdialysate. This technique has successfully been used for determining various types of binding interactions between the low affinity drugs, high affinity drugs and enantiomers to HSA. For the case of competitive binding of two drugs to a protein in solution, a displacement equation has been derived and examined with four nonsteroidal anti-inflammatory drugs and HSA as model drugs and protein, respectively. Microdialysis with HPLC was adopted to determine simultaneously the free solute and displacing agent in drug-protein solutions. The method is able to locate the binding site and determine affinity constants even up to 10(7) L/mol accurately.

Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes.

The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.