I. Structure, Function, and Molecular Recognition Investigated by Molecular Dynamics Simulations
A substantial proportion of the research carried out in our group is based on unbiased and biased molecular dynamics simulations at the atomistic level. To translate the molecular motion obtained from these simulations to meaningful insights into protein structural dynamics and function, we make use of different levels of free energy calculations as post-processing techniques: We perform binding free energy calculations via the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) [81] and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) [81,108] approaches, computational alanine scanning of protein-protein interfaces using the DrugScorePPI approach [164], umbrella sampling and potential of mean force (PMF) calculations using the Weighted Histogram Analysis Method (WHAM) [145,152], as well as thermodynamic integration (TI) computations [108]. We closely collaborate with researchers from a variety of experimental disciplines to refine and to corroborate our computational results, and to explain experimental observations on the atomistic level.
Soluble Proteins
In a collaborative approach, we predicted structural intermediates along the reaction cycle of Pyruvate Phosphate Dikinase (PPDK) [145] by computing configurational free energies and confirmed the existence of these intermediates in a series of crystal structures. Employing a similar methodology, we later provided evidence for an alternate binding change mechanism in PPDK, which provided the first explanation, why this enzyme is only active as a dimer [152]. As to further applications on soluble proteins, MD simulations also proved an indispensable tool in the systematic identification and characterization of peptidic Hsp90 dimerization inhibitors [127].
Membrane Proteins
Over the course of the last years, simulations of membrane proteins have become a routine application in our group [162,160,153,151,138]. Our studies on the G protein-coupled receptor (GPCR) TGR5 are another example of how simulations and experiment can fruitfully complement each other: We used MD simulations to narrow down possible multimerization patterns of that receptor [160,138], and to determine that the C-terminus of this GPCR controls membrane trafficking predominantly via its secondary structure [103]. Assisted by FRET measurements, we also provided an explanation on the atomistic level for the prothrombotic phenotype of platelets that carry the HPA-1b polymorphism in αIIbβ3 integrins [162]. We currently perform explicit solvent, explicit membrane MD simulations of the potassium/sodium hyperpolarization-activated cyclic nucleotide-gated ion channel 2 (HCN2) to gain insights into how binding of cyclic nucleotides regulates channel conductivity on a structural level.
RNA and DNA
The ribosome is one of the major targets for antimicrobial substances, many of which exert their inhibitory effect by binding at the peptidyl transferase center or the ribosomal exit tunnel. We combined MD simulations with free energy calculations [98,119] to study the species selectivity of oxazolidinone antibiotics and to explain how a well-described double mutation in the large ribosomal subunit affects oxazolidinone binding. As to other RNA-containing systems, MD simulations provided insights why ligand binding to the guanine sensing riboswitch aptamer domain (Gsw) is aggravated in the absence of Mg2+ ions, and to detect that the Gsw loop region is dynamically coupled to the ligand binding site [148]. To be able to accurately predict RNA-ligand interactions, we developed a knowledge-based scoring function [30], DrugScoreRNA, which was based on the formalism of the DrugScore approach [4,12].
Binding Free Energy and Entropy Calculations
To facilitate the setup of binding free energy calculations according to the MM-PBSA, MM-GBSA and linear interaction energy (LIE) approaches for sets of ligands typically investigated in lead optimization, we developed the workflow program FEW and extended it to membrane systems [85,114]. By conducting 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]. To this end, we developed an efficient tool to approximate configurational entropy changes in terms of the reduction in translational and rotational freedom of the ligand upon protein-ligand binding [141].
