Rationalization of gene regulation by a eukaryotic transcription factor: calculation of regulatory region occupancy from predicted binding affinities

The antigen-binding fragments (Fab) of antibodies are powerful tools in clinical therapy, molecular diagnostics and basic research. However, their principal applications require pure recombinant molecules in large amounts, which are challenging to obtain. Severe limitations in yield, folding and functionality are commonly encountered in bacterial production of Fab fragments. Secretion into the oxidizing periplasm generally results in low yield, whereas expression in the reducing cytoplasmic environment produces unfolded or non-functional protein. We hypothesized that an impaired reducing environment of the cytoplasm would permit correctly folded, functional cytoplasmic expression of Fabs with high yield. We used the Escherichia coli strain FA113, which has no activity of both thioredoxin and glutathione reductase, and thus has an oxidizing cytoplasmic environment. With the newly constructed vector pFAB1 we tested the cytoplasmic expression of two Fab fragments, which recognize the integral membrane protein NhaA, a bacterial Na(+)/H(+) antiporter. These antibodies differ in terms of DNA sequence and stability. Both antibody fragments were produced to very high yields (10-30 mg/l from bacterial cultures at an A(600 nm)=1.2-1.3). This is a factor 50-250 times higher than any other reported over-expression strategy for Fab fragments and currently represents the highest production rate ever been reported for antibody Fab fragments in bacteria grown to similar cell densities. The fragments are fully functional and can be efficiently purified by His-tag chromatography. Expression of active Fab fragments in the bacterial cytoplasm unlocks the possibility of using antibody specific targeting in an intracellular environment. Such a capacity opens new perspectives for investigating metabolic and regulatory pathways in vivo and also provides a powerful selection system for functional genomics.     

Integrated microfluidic approach for quantitative high-throughput measurements of transcription factor binding affinities

Protein binding to DNA is a fundamental process in gene regulation. Methodologies such as ChIP-Seq and mapping of DNase I hypersensitive sites provide global information on this regulation in vivo. In vitro methodologies provide valuable complementary information on protein–DNA specificities. However, current methods still do not measure absolute binding affinities. There is a real need for large-scale quantitative protein–DNA affinity measurements. We developed QPID, a microfluidic application for measuring protein–DNA affinities. A single run is equivalent to 4096 gel-shift experiments. Using QPID, we characterized the different affinities of ATF1, c-Jun, c-Fos and AP-1 to the CRE consensus motif and CRE half-site in two different genomic sequences on a single device. We discovered that binding of ATF1, but not of AP-1, to the CRE half-site is highly affected by its genomic context. This effect was highly correlated with ATF1 ChIP-seq and PBM experiments. Next, we characterized the affinities of ATF1 and ATF3 to 128 genomic CRE and CRE half-site sequences. Our affinity measurements explained that in vivo binding differences between ATF1 and ATF3 to CRE and CRE half-sites are partially mediated by differences in the minor groove width. We believe that QPID would become a central tool for quantitative characterization of biophysical aspects affecting protein–DNA binding.     

Statistical tests for natural selection on regulatory regions based on the strength of transcription factor binding sites

Background  

Although cis -regulatory changes play an important role in evolution, it remains difficult to establish the contribution of natural selection to regulatory differences between species. For protein coding regions, powerful tests of natural selection have been developed based on comparisons of synonymous and non-synonymous substitutions, and analogous tests for regulatory regions would be of great utility.     

Molecular modeling of cytochrome P450 1A1: enzyme-substrate interactions and substrate binding affinities

Human cytochrome P450 1A1, which is present in lungs, plays an important role in the metabolic activation of chemical carcinogens, and in particular, is thought to be linked to lung cancer. The mechanism of carcinogenesis is related to the enzyme's ability to oxidize highly toxic compounds, such as polycyclic aromatic hydrocarbons (PAHs), to their carcinogenic derivatives. In order to better understand P450 1A1 function, a homology model of this enzyme has been constructed. The model has been based on the structure of P450 2C5, the first mammalian P450 to be crystallized. The coordinates of the model have been calculated using a consensus strategy, and the resulting structure has been evaluated with the ProStat and Profiles-3D programs. P450 1A1 substrates, such as benzo[a]pyrene, ethoxyresorufin and methoxyresorufin, were then docked into the active site of the model, and key amino acid residues able to interact with the substrate, have been identified. The analysis of enzyme-substrate interactions indicated that hydrophobic interactions are mainly responsible for binding of these substrates in the active site. Moreover, the non-bond enzyme-substrate interaction energy for ethoxyresorufin was lower than that for methoxyresorufin, which is consistent with higher activity of 1A1 towards the former substrate. Key residue Val-382 may play an important role in these interactions. Additionally, we performed binding free energy calculations for the three substrates. The obtained values were similar to those observed experimentally, which suggests that this approach might be useful for prediction of binding constants.     

A novel empirical free energy function that explains and predicts protein-protein binding affinities

A free energy function can be defined as a mathematical expression that relates macroscopic free energy changes to microscopic or molecular properties. Free energy functions can be used to explain and predict the affinity of a ligand for a protein and to score and discriminate between native and non-native binding modes. However, there is a natural tension between developing a function fast enough to solve the scoring problem but rigorous enough to explain and predict binding affinities. Here, we present a novel, physics-based free energy function that is computationally inexpensive, yet explanatory and predictive. The function results from a derivation that assumes the cost of polar desolvation can be ignored and that includes a unique and implicit treatment of interfacial water-bridged interactions. The function was parameterized on an internally consistent, high quality training set giving R2=0.97 and Q2=0.91. We used the function to blindly and successfully predict binding affinities for a diverse test set of 31 wild-type protein-protein and protein-peptide complexes (R2=0.79, rmsd=1.2 kcal mol(-1)). The function performed very well in direct comparison with a recently described knowledge-based potential and the function appears to be transferable. Our results indicate that our function is well suited for solving a wide range of protein/peptide design and discovery problems.