Sebastian Schneider

In Silico Prediction of Binding Sites on Proteins

The majority of biological processes involve the association of proteins or binding of other ligands to proteins. The accurate prediction of putative binding sites on the protein surface can be very helpful for rational drug design on target proteins of medical relevance, for predicting the geometry of protein-protein as well as protein-ligand complexes and for evaluating the tendency of proteins to aggregate or oligomerize. A variety of computational methods to rapidly predict protein-protein binding interfaces or binding sites for small drug-like molecules have been developed in recent years. The principles of methods available for protein interface and pocket detection are summarized, including approaches based on sequence conservation, as well as geometric and physicochemical surface properties. The performance of several Webaccessible methods for ligand binding site prediction has been compared using protein structures in bound and unbound conformation and homology modeled proteins. All methods tested gave very promising predictions even on unbound and homology modeled protein structures, thus indicating that current methods are robust in relation to modest conformational changes associated with the ligand binding process.

Atomic resolution model of the antibody Fc interaction with the complement C1q component

The globular C1q heterotrimer is a subunit of the C1 complement factor. Binding of the C1q subunit to the constant (Fc) part of antibody molecules is a first step and key event of complement activation. Although three-dimensional structures of C1q and antibody Fc subunits have been determined experimentally no atomic resolution structure of the C1q-Fc complex is known so far. Based on systematic protein-protein docking searches and Molecular Dynamics simulations a structural model of the C1q-IgG1-Fc-binding geometry has been obtained. The structural model is compatible with available experimental data on the interaction between the two partner proteins. It predicts a binding geometry that involves mainly the B-subunit of the C1q-trimer and both subunits of the IgG1-Fc-dimer with small conformational adjustments with respect to the unbound partners to achieve high surface complementarity. In addition to several charge-charge and polar contacts in the rim region of the interface it also involves nonpolar contacts between the two proteins and is compatible with the carbohydrate moiety of the Fc subunit. The model for the complex structure provides a working model for rationalizing available biochemical data on this important interaction and can form the basis for the design of Fc variants with a greater capacity to activate the complement system for example on binding to cancer cells or other target structures.

Anti insulin-like growth factor I receptor immunoliposomes: a single formulation combining two anticancer treatments with enhanced therapeutic efficiency

CONTEXT Through overexpression and aberrant activation in many human tumors, the IGF system plays a key role in tumor development and tumor cell proliferation. Different strategies targeting IGF-I receptor (IGFI-R) have been developed, and recent studies demonstrated that combined treatments with cytostatic drugs enhance the potency of anti-IGFI-R therapies. OBJECTIVE The objective of the study was to examine the IGFI-R expression status in neuroendocrine tumors of the gastroenteropancreatic system (GEP-NETs) in comparison with healthy tissues and use potential overexpression as a target for novel anti-IGFI-R immunoliposomes. EXPERIMENTAL DESIGN A human tumor tissue array and samples from different normal tissues were investigated by immunohistochemistry. An IGFI-R antagonistic antibody (1H7) was coupled to the surface of sterically stabilized liposomes loaded with doxorubicin. Cell lines from different tumor entities were investigated for liposomal association studies in vitro. For in vivo experiments, neuroendocrine tumor xenografts were used for evaluation of pharmacokinetic and therapeutic properties of the novel compound. RESULTS Immunohistochemistry revealed significant IGFI-R overexpression in all investigated GEP-NETs (n = 59; staining index, 229.1 +/- 3.1%) in comparison with normal tissues (115.7 +/- 3.7%). Furthermore, anti-IGFI-R immunoliposomes displayed specific tumor cell association (44.2 +/- 1.6% vs. IgG liposomes, 0.8 +/- 0.3%; P < 0.0001) and internalization in human neuroendocrine tumor cells in vitro and superior antitumor efficacy in vivo (life span 31.5 +/- 2.2 d vs. untreated control, 19 +/- 0.6, P = 0.008). CONCLUSION IGFI-R overexpression seems to be a common characteristic of otherwise heterogenous NETs. Novel anti-IGFI-R immunoliposomes have been developed and successfully tested in a preclinical model for human GEP-NETs. Moreover in vitro experiments indicate that usage of this agent could also present a promising approach for other tumor entities.

Intracellular FRET analysis of lipid/DNA complexes using flow cytometry and fluorescence imaging techniques

Gene therapy is a promising therapeutic concept for a large number of incurable diseases. Lipid/DNA complexes (lipoplexes) are used to deliver genes into cells. However, while large efforts have been made to investigate the fate of lipoplexes once inside the cell, the rate of intracellular dissociation is still largely unknown. Analysis of the dissociation rates of DNA from lipid/DNA complexes is crucial for the evaluation of a gene delivery systems efficiency. This study introduces a new fluorescence resonance energy transfer (FRET) approach for the intracellular dissociation analysis of lipid/DNA complexes. Here, the labeling of both complex components, DNA as well as lipid, reveals whether DNA is still associated with the lipid or has dissociated. In this study the kinetic properties of complex dissociation were consistently measured with flow cytometry and fluorescence microscopy, and indicated that most complexes were dissociated after 24h in A-10 cells.

ATTRACT and PTools: open source programs for protein-protein docking.

The prediction of the structure of protein-protein complexes based on structures or structural models of isolated partners is of increasing importance for structural biology and bioinformatics. The ATTRACT program can be used to perform systematic docking searches based on docking energy minimization. It is part of the object-oriented PTools library written in Python and C++. The library contains various routines to manipulate protein structures, to prepare and perform docking searches as well as analyzing docking results. It also intended to facilitate further methodological developments in the area of macromolecular docking that can be easily integrated. Here, we describe the application of PTools to perform systematic docking searches and to analyze the results. In addition, the possibility to perform multi-component docking will also be presented.

Combining geometric pocket detection and desolvation properties to detect putative ligand binding sites on proteins

The accurate identification of cavities that can bind ligands on the surface of proteins is of major importance for the characterization of the function of proteins based on its structure. In addition it can be helpful for rational structure-based drug design on target proteins of medical relevance and for evaluating the tendency of proteins to aggregate or oligomerize. A new approach termed dPredGB to detect and evaluate putative binding cavities on protein surfaces has been developed. In contrast to existing prediction methods that are based on purely geometric features of binding sites or on possible direct interactions with a putative binding partner the dPredGB approach combines rapid geometric detection with an evaluation of the desolvation properties of the putative binding pocket. It has been tested on a variety of proteins known to bind ligands in bound and unbound conformations. The approach outperforms most available methods and offers also the spatial characterization of the desolvation properties of a binding region. On a test set of proteins the method identifies in 69% of the unbound cases and 85% of the bound cases the known ligand binding cavity as the top ranking prediction. Possibilities to improve the prediction performance even further are also discussed.

Scoring optimisation of unbound protein–protein docking including protein binding site predictions

The prediction of the structure of the protein–protein complex is of great importance to better understand molecular recognition processes. During systematic protein–protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse‐grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures. Copyright

Spectral Bio-Imaging and Confocal Imaging of the Intracellular Distribution of Lipoplexes

The intracellular distribution of nanoparticular drug delivery systems is very complex, but its investigation yields high potential for further development and optimization of these systems.In the following chapter, we introduce the application of fluorescent imaging techniques in order to highlight uptake and cellular processing of nanoparticular drug delivery systems (e.g., liposomal drug delivery systems). We selected a combination of different protocols for the staining of the most important endocytic compartments and organelles. The presented imaging systems are appropriate to detect liposomal drug delivery systems localized in these cellular structures.

Flexible Protein-Protein Docking

Biological processes almost always involve protein-protein interactions. Understanding the function of protein-protein interactions requires knowledge of the structure of the corresponding protein-protein complexes. The experimental structure determination by Xray crystallography requires purification of large amounts of proteins. In addition, it is necessary to crystallize the proteins in the native complex which may not be feasible for all known interacting proteins. Multi-protein complexes mediate many cellular functions and are in a dynamic equilibrium with the isolated components or sub-complexes (Gavin et al., 2002; Rual et al., 2005). In particular, complexes of weakly or transiently interacting protein partners are often not stable enough to allow experimental structure determination at high (atomic) resolution. Experimental studies on detecting all protein-protein interactions in a cell indicate numerous possible interactions ranging from few to several hundred possible binding partners for one protein (Gavin et al., 2002; Rual et al., 2005). A full understanding of cellular functions requires structural knowledge of all these interactions. In the foreseeable future it will not be possible to determine the structure of all detected proteinprotein interactions experimentally at high resolution. Structural modeling and structure prediction is therefore of increasing importance to obtain at least realistic structural models of complexes (Bonvin, 2006; Andrusier et al., 2008; Vajda & Kozakov, 2009; Zacharias, 2010). If the structure of the isolated protein partners or of closely related proteins is available it is possible to use a variety of computational docking methods to generate putative complex structures. The driving force for the protein binding process corresponds to the associated change in free energy which depends on the structural and physicochemical properties of the protein partners. The “lock and key” concept of binding proposed by E. Fischer (Fischer, 1894) emphasizes the importance of optimal sterical complementarity at binding interfaces as a decisive factor to achieve high affinity and specificity. However, proteins and other interacting biomolecules are not rigid but can undergo a variety of motions even at physiological temperatures. The induced fit concept has evolved based on the observation that binding can result in significant conformational changes of partner molecules (Koshland, 1958). Within this concept protein partners induce conformational changes during the binding process that are required for specific complex formation. It should be emphasized, that in principle all possible molecular recognition processes require a certain degree of conformational adaptation. In recent years extensions of the induced-fit concept,

Der 3D-Skizzierer — Unscharfes digitales Skizzieren in einer Virtual Reality Umgebung

Entwickler verwenden bei der Losungssuche vor allem Handskizzen. CAD-Systeme hindern sie eher an der kreativen Losungssuche. Der 3D-Skizzierer schliest diese Lucke. Er bietet die Moglichkeit, 3-dimensionale Skizzen digital zu erstellen.