Rational Design of Iron Oxide Binding Peptide Tags.

Owing to their extraordinary magnetic properties and low-cost production, iron oxide nanoparticles (IONs) are in the focus of research. In order to better understand interactions of IONs with biomolecules, a tool for the prediction of the propensity of different peptides to interact with IONs is of great value. We present an effective implicit surface model (EISM), which includes several interaction models. Electrostatic interactions, van der Waals interactions, and entropic effects are considered for the theoretical calculations. However, the most important parameter, a surface accessible area force field contribution term, derives directly from experimental results on the interactions of IONs and peptides. Data from binding experiments of ION agglomerates to different peptides immobilized on cellulose membranes have been used to parameterize the model. The work was carried out under defined environmental conditions; hence, effects because of changes, for example structure or solubility by changing the surroundings, are not included. EISM enables researchers to predict the binding of peptides to IONs, which we then verify with further peptide array experiments in an iterative optimization process also presented here. Negatively charged peptides were identified as best binders for IONs in Tris buffer. Furthermore, we investigated the constitution of peptides and how the amount and position of several amino acid side chains affect peptide-binding. The incorporation of glycine leads to higher binding scores compared to the incorporation of cysteine in negatively charged peptides.

A multifrequency virtual spectrometer for complex bio-organic systems: vibronic and environmental effects on the UV/Vis spectrum of chlorophyll a.

The subtle interplay of several different effects means that the interpretation and analysis of experimental spectra in terms of structural and dynamic characteristics is a challenging task. In this context, theoretical studies can be helpful, and as such, computational spectroscopy is rapidly evolving from a highly specialized research field toward a versatile and widespread tool. However, in the case of electronic spectra (e.g. UV/Vis, circular dichroism, photoelectron, and X-ray spectra), the most commonly used methods still rely on the computation of vertical excitation energies, which are further convoluted to simulate line shapes. Such treatment completely neglects the influence of nuclear motions, despite the well-recognized notion that a proper account of vibronic effects is often mandatory to correctly interpret experimental findings. Development and validation of improved models rooted into density functional theory (DFT) and its time-dependent extension (TD-DFT) is of course instrumental for the optimal balance between reliability and favorable scaling with the number of electrons. However, the implementation of easy-to-use and effective procedures to simulate vibrationally resolved electronic spectra, and their availability to a wide community of users, is at least equally important for reliable simulations of spectral line shapes for compounds of biological and technological interest. Here, such an approach has been applied to the study of the UV/Vis spectra of chlorophyll a. The results show that properly tailored approaches are feasible for state-of-the-art computational spectroscopy studies, and allow, with affordable computational resources, vibrational and environmental effects on the spectral line shapes to be taken into account for large systems.

Modelling peptide adsorption energies on gold surfaces with an effective implicit solvent and surface model.

The interaction of proteins and peptides with inorganic surfaces is relevant in a wide array of technological applications. A rational approach to design peptides for specific surfaces would build on amino-acid and surface specific interaction models, which are difficult to characterize experimentally or by modeling. Even with such a model at hand, the large number of possible sequences and the large conformation space of peptides make comparative simulations challenging. Here we present a computational protocol, the effective implicit surface model (EISM), for efficient in silico evaluation of the binding affinity trends of peptides on parameterized surface, with a specific application to the widely studied gold surface. In EISM the peptide surface interactions are modeled with an amino-acid and surface specific implicit solvent model, which permits rapid exploration of the peptide conformational degrees of freedom. We demonstrate the parametrization of the model and compare the results with all-atom simulations and experimental results for specific peptides.

Experimental characterization and simulation of amino acid and peptide interactions with inorganic materials.

Inspired by nature, many applications and new materials benefit from the interplay of inorganic materials and biomolecules. A fundamental understanding of complex organic-inorganic interactions would improve the controlled production of nanomaterials and biosensors to the development of biocompatible implants for the human body. Although widely exploited in applications, the interaction of amino acids and peptides with most inorganic surfaces is not fully understood. To date, precisely characterizing complex surfaces of inorganic materials and analyzing surface-biomolecule interactions remain challenging both experimentally and computationally. This article reviews several approaches to characterizing biomolecule-surface interactions and illustrates the advantages and disadvantages of the methods presented. First, we explain how the adsorption mechanism of amino acids/peptides to inorganic surfaces can be determined and how thermodynamic and kinetic process constants can be obtained. Second, we demonstrate how this data can be used to develop models for peptide-surface interactions. The understanding and simulation of such interactions constitute a basis for developing molecules with high affinity binding domains in proteins for bioprocess engineering and future biomedical technologies.

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.