Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants

The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain (“cancer mutants”). Activity can be restored by second-site suppressor mutations (“rescue mutants”). This paper relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of clusters at a certain RMSD cutoff, was computed by clustering over 0.7 µs of explicitly solvated all-atom MD simulations. For wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53 functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r2 = 0.77) with reported values of experimentally measured ΔΔG protein thermodynamic stability. Interpreting the number of clusters as a measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not. This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L

Specific Recognition of p53 Tetramers by Peptides Derived from p53 Interacting Proteins

Oligomerization plays a major role in regulating the activity of many proteins, and in modulating their interactions. p53 is a homotetrameric transcription factor that has a pivotal role in tumor suppression. Its tetramerization domain is contained within its C-terminal domain, which is a site for numerous protein-protein interactions. Those can either depend on or regulate p53 oligomerization. Here we screened an array of peptides derived from proteins known to bind the tetrameric p53 C-terminal domain (p53CTD) and identified ten binding peptides. We quantitatively characterized their binding to p53CTD using fluorescence anisotropy. The peptides bound tetrameric p53CTD with micromolar affinities. Despite the high charge of the binding peptides, electrostatics contributed only mildly to the interactions. NMR studies indicated that the peptides bound p53CTD at defined sites. The most significant chemical shift deviations were observed for the peptides WS100B(81–92), which bound directly to the p53 tetramerization domain, and PKCα(281–295), which stabilized p53CTD in circular dichroism thermal denaturation studies. Using analytical ultracentrifugation, we found that several of the peptides bound preferentially to p53 tetramers. Our results indicate that the protein-protein interactions of p53 are dependent on the oligomerization state of p53. We conclude that peptides may be used to regulate the oligomerization of p53

Purinergic signalling and immune cells

This review article provides a historical perspective on the role of purinergic signalling in the regulation of various subsets of immune cells from early discoveries to current understanding. It is now recognised that adenosine 5'-triphosphate (ATP) and other nucleotides are released from cells following stress or injury. They can act on virtually all subsets of immune cells through a spectrum of P2X ligand-gated ion channels and G protein-coupled P2Y receptors. Furthermore, ATP is rapidly degraded into adenosine by ectonucleotidases such as CD39 and CD73, and adenosine exerts additional regulatory effects through its own receptors. The resulting effect ranges from stimulation to tolerance depending on the amount and time courses of nucleotides released, and the balance between ATP and adenosine. This review identifies the various receptors involved in the different subsets of immune cells and their effects on the function of these cells

Engineering chimeric thermostable GH7 cellobiohydrolases in Saccharomyces cerevisiae

We report here the effect of adding different types of carbohydrate-binding modules (CBM) to a single-module GH7 family cellobiohydrolase Cel7A from a thermophilic fungus Talaromyces emersonii (TeCel7A). Both bacterial and fungal CBMs derived from families 1, 2 and 3, all reported to bind to crystalline cellulose, were used. Chimeric cellobiohydrolases with an additional S-S bridge in the catalytic module of TeCel7A were also made. All the fusion proteins were secreted in active form and in good yields by Saccharomyces cerevisiae. The purified chimeric enzymes bound to cellulose clearly better than the catalytic module alone and demonstrated high thermal stability, having unfolding temperatures (T m) ranging from 72 °C to 77 °C. The highest activity enhancement on microcrystalline cellulose could be gained by a fusion with a bacterial CBM3 derived from Clostridium thermocellum cellulosomal- scaffolding protein CipA. The two CBM3 fusion enzymes tested were more active than the reference enzyme Trichoderma reesei Cel7A both at moderate (45 °C and 55 °C) and at high temperatures (60 °C and 65 °C), the hydrolysis yields being two- to three-fold better at 60 °C, and six- to seven-fold better at 65 °C. The best enzyme variant was also tested on a lignocellulosic feedstock hydrolysis, which demonstrated its potency in biomass hydrolysis even at 70 °C.</p

Stability of p53 Homologs

<div><p>Most proteins have not evolved for maximal thermal stability. Some are only marginally stable, as for example, the DNA-binding domains of p53 and its homologs, whose kinetic and thermodynamic stabilities are strongly correlated. Here, we applied high-throughput methods using a real-time PCR thermocycler to study the stability of several full-length orthologs and paralogs of the p53 family of transcription factors, which have diverse functions, ranging from tumour suppression to control of developmental processes. From isothermal denaturation fluorimetry and differential scanning fluorimetry, we found that full-length proteins showed the same correlation between kinetic and thermodynamic stability as their isolated DNA-binding domains. The stabilities of the full-length p53 orthologs were marginal and correlated with the temperature of their organism, paralleling the stability of the isolated DNA-binding domains. Additionally, the paralogs p63 and p73 were significantly more stable and long-lived than p53. The short half-life of p53 orthologs and the greater persistence of the paralogs may be biologically relevant.</p> </div