Prediction of specific biomolecule adsorption on silica surfaces as a function of pH and particle size

Silica nanostructures are biologically available and find wide applications for drug delivery, catalysts, separation processes, and composites. However, specific adsorption of biomolecules on silica surfaces and control in biomimetic synthesis remain largely unpredictable. In this contribution, the variability and control of peptide adsorption on silica nanoparticle surfaces is explained as a function of pH, particle diameter, and peptide electrostatic charge using molecular dynamics simulations with the CHARMM-INTERFACE force field. Adsorption free energies and specific binding residues are analyzed in molecular detail, providing experimentally elusive, atomic-level information on the complex dynamics of aqueous electric double layers in contact with biological molecules. Tunable contributions to adsorption are described in the context of specific silica surface chemistry, including ion pairing, hydrogen bonds, hydrophobic interactions, and conformation effects. Remarkable agreement is found for computed peptide binding as a function of pH and particle size versus experimental adsorption isotherms and zeta-potentials. Representative surface models were built using characterization of the silica surfaces by TEM, SEM, BET, TGA, ζ-potential, and surface titration measurements. The results show that the recently introduced interatomic potentials (Emami et al. Chem. Mater. 2014, 26, 2647) enable computational screening of a limitless number of silica interfaces to predict the binding of drugs, cell receptors, polymers, surfactants, and gases under realistic solution conditions at the scale of 1 to 100 nm. The highly specific binding outcomes underline the significance of the surface chemistry, pH, and topography

Scintigraphic evaluation of oesophageal transit during radiotherapy to the mediastinum

Background: To quantitatively evaluate radiation-induced impaired oesophageal transit with oesophageal transit scintigraphy and to assess the relationships between acute oesophagitis symptoms and dysmotility.\ud \ud Methods: Between January 1996 and November 1998, 11 patients affected by non-small-cell carcinoma of the lung not directly involving the oesophagus, requiring adjuvant external beam radiotherapy (RT) to the mediastinum were enrolled. Oesophageal transit scans with liquid and semisolid bolus were performed at three pre-defined times: before (T0) and during radiation at 10 Gy (T1) and 30 Gy (T2). Two parameters were obtained for evaluation: 1) mean transit time (MTT); and 2) ratio between peak activity and residual activity at 40 seconds (ER-40s). Acute radiation toxicity was scored according to the joint EORTC-RTOG criteria. Mean values with standard deviation were calculated for all parameters. Analysis of variance (ANOVA) tests and paired t-Tests for all values were performed.\ud \ud Results: An increase in the ER-40s from T0 to T1 or T2 was seen in 9 of 11 patients (82%). The mean ER-40s value for all patients increased from 0.8306 (T0) to 0.8612 (T1) and 0.8658 (T2). These differences were statistically significant (p < 0.05) in two paired t-Tests at T0 versus T2 time: overall mean ER-40s and upright ER-40s (p = 0.041 and p = 0.032, respectively). Seven patients (63%) showed a slight increase in the mean MTT value during irradiation but no statistically significant differences in MTT parameters were found between T0, T1 and T2 (p > 0.05).\ud \ud Conclusion: Using oesophageal scintigraphy we were able to detect early alterations of oesophageal transit during the third week of thoracic RT

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest -and hope -that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited -in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors

The Wnt-dependent signaling pathways as target in oncology drug discovery

Our current understanding of the Wnt-dependent signaling pathways is mainly based on studies performed in a number of model organisms including, Xenopus, Drosophila melanogaster, Caenorhabditis elegans and mammals. These studies clearly indicate that the Wnt-dependent signaling pathways are conserved through evolution and control many events during embryonic development. Wnt pathways have been shown to regulate cell proliferation, morphology, motility as well as cell fate. The increasing interest of the scientific community, over the last decade, in the Wnt-dependent signaling pathways is supported by the documented importance of these pathways in a broad range of physiological conditions and disease states. For instance, it has been shown that inappropriate regulation and activation of these pathways is associated with several pathological disorders including cancer, retinopathy, tetra-amelia and bone and cartilage disease such as arthritis. In addition, several components of the Wnt-dependent signaling pathways appear to play important roles in diseases such as Alzheimer’s disease, schizophrenia, bipolar disorder and in the emerging field of stem cell research. In this review, we wish to present a focused overview of the function of the Wnt-dependent signaling pathways and their role in oncogenesis and cancer development. We also want to provide information on a selection of potential drug targets within these pathways for oncology drug discovery, and summarize current data on approaches, including the development of small-molecule inhibitors, that have shown relevant effects on the Wnt-dependent signaling pathways

Binding patterns of homo-peptides on bare magnetic nanoparticles: insights into environmental dependence

Magnetic nanoparticles (MNP) are intensively investigated for applications in nanomedicine, catalysis and biotechnology, where their interaction with peptides and proteins plays an important role. However, the characterisation of the interaction of individual amino acids with MNP remains challenging. Here, we classify the affinity of 20 amino acid homo-hexamers to unmodified iron oxide nanoparticles using peptide arrays in a variety of conditions as a basis to identify and rationally design selectively binding peptides. The choice of buffer system is shown to strongly influence the availability of peptide binding sites on the MNP surface. We find that under certain buffer conditions peptides of different charges can bind the MNP and that the relative strength of the interactions can be modulated by changing the buffer. We further present a model for the competition between the buffer and the MNP’s electrostatically binding to the adsorption sites. Thereby, we demonstrate that the charge distribution on the surface can be used to correlate the binding of positively and negatively charged peptides to the MNP. This analysis enables us to engineer the binding of MNP on peptides and contribute to better understand the bio-nano interactions, a step towards the design of affinity tags for advanced biomaterials