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Research Overview

The current research is focussed on the understanding, prediction, and modulation of interactions involving biological macromolecules. We apply and develop techniques grounded in bioinformatics, computational biophysics, and computational pharmaceutical chemistry such as docking, molecular dynamics simulations, free energy estimations, and methods to characterize the flexibility of protein and RNA structures. Three main research areas can be distinguished:


I. Structure, function, and molecular recognition investigated by molecular dynamics simulations


Molecular dynamics simulations are a powerful tool to obtain structural insights into the dynamics of biomacromolecules on an atomic level and thereby complement experimental findings. With this method we want to understand the underlying mechanisms of RNA- and protein-ligand interactions, the protein synthesis in the ribosome, as well as the role of conformational heterogeneity in molecular recognition.


Antibiotics binding and protein voyage in the ribosomal exit tunnel

Ribosomes are large ribonucleoprotein complexes and the protein synthesis machines of the cell. After peptide bond formation, the nascent polypeptides leave the peptidyl transferase center (PTC) via the ribosomal exit tunnel (RET), which spans the entire large ribosomal subunit. The ribosome is also one of the major targets within the cell for antimicrobials, many of which execute their inhibitory effect by binding at the PTC or in the RET. We use molecular dynamics (MD) simulations to obtain insights in atomic detail into RET dynamics and the protein voyage, and combine MD with free energy calculations [16,18,98] to investigate antibiotics binding. Furthermore, to understand how RNA molecules undergo large but controlled conformational changes to achieve important functional roles, concepts of graph theory and rigidity theory are applied to a topological network representation of  the ribosome to investigate static properties of the RET [33,40,41,45].


The large ribosomal subunit.


Structure of a riboswitch bound to a ligand.

Understanding and predicting the dynamics and function of RNA and proteins


Small RNAs, such as the HIV-1 Trans Activation Response (TAR) element and riboswitches, as well as proteins, such as α5β1-integrin, are subject to conformational changes upon binding of ligands or changes in their environment. Correctly describing and predicting such conformational changes is key for linking the biomolecules' structure and function.  In the case of TAR RNA, MD and constrained geometrical simulations are performed on the unbound structure in order to test whether bound conformations can be generated that can be successfully used for docking [51]. For α5β1-integrin, recent experimental findings suggested an activation by high urea concentrations and presence of certain bile acids. MD simulations at such conditions indeed showed conformational changes that precede unbending of the integrin structure, which is necessary for the activation [52,89]. At present, we use MD simulations to derive structural features of bile acid derivatives that are required for activation of as α5β1-integrin. In order to characterize the unbound state of riboswitches, we currently use MD simulations to gain deeper insights into the riboswitch's conformational heterogeneity and binding pathways. Moreover, we perform MD simulations to investigate the mechanisms of regulation and deactivation of the glutamine synthetase and the phosphate transfer of pyruvate phosphate dikinase as potential drug targets.

Binding free energy and entropy calculations



Understanding the binding properties of ligands, proteins, and nucleic acids is essential for designing new molecules with good binding properties. Based on conformational ensembles from molecular dynamics simulations, we determine the binding affinity of complex components by free energy calculations according to thermodynamic integration, MM-PB(GS)SA [81], and linear interaction energy approaches as well as by entropy evaluation methods. An analysis of the binding free energy of self-paired fluorinated benzene bases that are known to increasingly stabilize RNA duplexes with increasing number of fluorine atoms provided atomic level insights into the determinants of stability differences [36]. To facilitate the setup of binding free energy calculations according to the above approaches for sets of ligands tipically investigated in the lead optimization, we developed a workflow program [85]. By conducing such binding free energy calculations we aim at identifying procedures and strategies that allow to correctly rank potential drug candidates according to their binding affinity. In this context, correctly and efficiently estimating translational, rotational, and configurational entropy contributions to the binding free energy is still a holy grail [18]. Therefore, we aim at developing an efficient and accurate tool for estimating differences in the rotational and translational component of binding entropy.


   Correlation plot between experimentally determined and calculated binding free energies with 95% confidence bands (dotted lines).


II. Modulating protein-protein interactions and analysing protein binding pockets

Protein-ligand and protein-protein interactions play an important role for the function of living organisms. Thus, one of the research areas of our group is focussed on analyzing regions where these interactions occur. In particular, we focus on aspects of plasticity and "ligandability" of protein-protein interfaces and ligand binding sites and protein function prediction by binding pocket comparison.

Structural and energetic determinants of protein-protein binding


At present, the design of small-molecule protein-protein interactions modulators (PPIM) encounters at least two challenges [71]. First, protein-protein interfaces are rather flat and usually lack a distinct binding pocket. Second, due to the size of protein-protein interfaces interactions that are favorable for binding can be widely distributed. We address these challenges based on the protein-protein complex structure. Therefore, we sample accessible conformations by molecular dynamics (MD) simulation [21] and the fast geometrical simulation method FRODA [26], predict the opening of transient binding pockets in the interface, identify "hot spot" residues that make important interactions, and and use these predictions to guide the design of [77] and screen for small-molecule PPII [73,97].



Predicted hot spots and pockets can guide PPII identification [73].



Artistic representation of a teroxazole-based peptidomimetic PPII [77].


Knowledge-driven modulation of protein-protein interactions


We established a structure-based strategy to identify PPIIs. First, hot spots of protein-protein binding are identified by MM-PBSA free energy decomposition [18] and knowledge-based interactions potentials (DrugScorePPI[dsPPI]) [49]. Experimental validation by alanine mutations and hot spot-bearing inhibitory peptides allow an early on druggability assessment. Then, PPIIs are identified by hot spot-based similarity screening and design of peptidomimetics. Finally, the suggested PPIIs can be investigated by molecular docking and free energy calculations. We currently focus on important pharmacological targets such as IL2/IL2Rα [73], the HSP90 [105] dimer, and the NHR2 homotetramer [47,97]. But, as the suggested strategy requires only a protein/protein complex structure, it is applicable to a large number of protein/protein interactions.

Characterizing protein binding sites to predict druggability and protein function

Since the shape, volume, and size of binding pockets usually varies, up to now there is no standard definition what constitutes a binding pocket, and descriptors allowing an in-depth characterization of binding pockets are lacking. Thus, we aim at developing novel approaches to characterize protein binding pockets based on knowledge-based potential fields [4,23]. The resulting binding site descriptors can be used to predict target ligandability, i.e. the ability of a binding pocket to bind small molecules with high affinity [12]. We also develop tools for deriving structure based quantitative structure activity relationships for one or multiple binding pocket conformations [14,31,76]. These methods can for example be used in lead optimization processes. Additionally, the characterization of binding pockets allows protein function de-orphanization by comparing binding pockets of multiple proteins and transferring the function of the most similar protein to the protein with unknown function [80]. Additionally, we maintain an inhouse binding interface (BIF) database that allows a large-scale exploration of protein-protein interactions that putatively can be inhibited by helix mimetics.


HIV-1 protease binding site in complex with darunavir and
its corresponding knowledge-based interaction fields.


III. Modeling flexibility and plasticity of biomolecules / Development of novel docking approaches

Modeling protein flexibility and plasticity is important for understanding the function of biological systems. Given a network representation of a macromelcular structure, its intrinsic flexibility can be efficiently identified by determining the number and spatial distribution of bond-rotational degrees of freedom in the network. The resulting rigid cluster decomposition can be further used as input of coarse graininig schemes such as normal mode analysis to model biomolecular mobility. This information can then be incorporated into docking algorithms to efficiently consider receptor flexibility.

Protein rigidity, thermal adaptation, and allostery

Biomacromolecules require a balance of flexibility and rigidity to achieve their diverse functional roles. Hence, it is desirable to have a precise knowledge about what can move and how. The flexibility for a given biomacromolecule can be analyzed within a few seconds by a graph theory-based approach as implemented in the FIRST program by Thorpe et al. FIRST was successfully applied to analyze changes in protein flexibility upon complex formation [19] and to characterize the flexibility of RNA structures [33,41]. For further quantifying biomacromolecular stability, the Constraint Network Analysis (CNA) program package has been developed in our group [93] . Working as a front- and backend to FIRST, CNA (I) carries out thermal unfolding simulations that also considers the temperature-dependence of hydrophobic interaction Working as a front- and backend to FIRST, CNA (I) carries out thermal unfolding simulations that also considers the temperature-dependence of hydrophobic interaction [67], (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles [67] or by using the concept of fuzzy noncovalent constraints [96], and (III) computes a set of global and local indices for quantifying biomacromolecular stability [86]. CNA was successfully applied to investigate the stability of proteins from mesophilic and thermophilic organisms [37,55,67]. Currently, the CNA program package is used for predicting the coupling between allosteric sites by identifying physically connected pathways of residues in proteins. A web server for CNA [94] is available here.


   Illustration of the Constraint Network Analysis (CNA) approach for characterizing biomacromolecular flexibility.


Example of an elastic grid-based protein-docking based on
conformational adaption of eglin c and subtilisin Carlsberg.

Fully-flexible docking based on an elastic grid representation of potential fields

Most current docking approaches consider the receptor as rigid. However, this simplification can lead to the failure of docking attempts in those cases where proteins or nucleic acids undergo conformational changes during complex formation [28,35,51]. Thus, we develop a fully-flexible docking approach that uses an elastic grid-based representation of potential fields as objective function. The elastic grid thereby adapts to movements occurring in one or both of the binding partners. Several knowledge-based scoring functions have been developed in the group for describing protein-protein [50,56], protein-ligand [4] and protein-RNA [30] interactions. In total, we expect to get to a docking approach that yields accurate results in cases of conformational variability of the macromolecules, yet is still efficient [42,57].

Multi-scale modeling of macromolecular conformational transitions

Modeling conformational transitions of macromolecules is computationally challenging. Recently, coarse-grained normal mode approaches based on elastic network theory have emerged as efficient alternatives for investigating large-scale conformational changes. It has been shown that functionally important conformational changes of proteins can be described by low frequency normal modes, which are robust and insensitive to higher coarse-graining [53]. Accordingly, we have introduced a three-step approach for multi-scale modeling of macromolecular conformational changes. The first two steps are based on recent developments in rigidity and elastic network theory (termed Rigid Cluster Normal Mode Analysis) [24,26]. In the final step, the recently introduced idea of constrained geometric simulations of diffusive motions in proteins is extended. New macromolecule conformers are generated by deforming the structure along low-energy normal mode directions predicted by RCNMA plus random direction components [58].


Three-step approach for multi-scale modeling
of macromolecular conformational changes.


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