Molecular simulations of carbohydrate-protein complexes

I. Generation and validation of a free-energy model for carbohydrate binding.
Carbohy-drates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the stru-cture-based design of carbohydrate-based ligands. To this end, we assembled a diverse data set of 316 carbohydrate–protein crystal structures with known binding affinity. We eval-uated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as empirical combinations thereof. Unfortunately, the tested func-tions were not capable of reproducing carbohydrates binding affinities in our complexes. To simplify the complex free-energy surface of carbohydrate–protein systems, we classified the studied complexes according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classi-fication scheme reproduced binding affinities in the carbohydrate data set with an r2 of 0.69 and root-mean-squared-error of 1.36 kcal/mol. The improvement in model performance underlines the significance of the differences in the local micro-environments of carbo-hydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and surface exposure.
II. Simulating the binding of Lewis-type ligands to DC-SIGN.
Dendritic cells (DCs) have the function of presenting antigens to other processing cells of the immune system, particularly T-cells. DC-SIGN (DC-specific intercellular adhesion molecule-3-grabbing non-integrin) is one of the major receptors on DCs involved in the uptake of pathogens and has gained increasing interest over the last decade as it is crucially involved in infections caused by HIV-1, Ebola virus, Mycobacterium tuberculosis, and various other pathogens. High-mannosylated N-glycans or L-Fuc-containing trisaccharide motifs such as the Lewis (Le) blood group antigens Lea and Lex, which are surface components of these microorganisms, mediate binding to DC-SIGN. Crystallographic data for DC-SIGN in complex with a Lex-containing pentasaccharide suggest that the terminal sugar residues, L-Fuc and D-Gal, are predominantly involved in binding. We elucidated the interaction of DC-SIGN with Lea and Lex bearing two different aglycones. Binding assays together with STD NMR analysis, molecular modeling and mutagenesis studies revealed distinct binding modes dependent on the nature of the aglycone. Introduction of phenyl aglycones at the Le trisaccharides offers the establishment of an additional hydrophobic contact with Phe313 in the binding site of DC-SIGN, which entails a switch of the binding mode. Based on this information a new series of DC-SIGN antagonists can be designed.
III. Developing a molecular modeling toolbox for medicinal chemists.
In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand–protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.