Daniel Seeliger

Ligand docking and binding site analysis with PyMOL and Autodock/Vina

Docking of small molecule compounds into the binding site of a receptor and estimating the binding affinity of the complex is an important part of the structure-based drug design process. For a thorough understanding of the structural principles that determine the strength of a protein/ligand complex both, an accurate and fast docking protocol and the ability to visualize binding geometries and interactions are mandatory. Here we present an interface between the popular molecular graphics system PyMOL and the molecular docking suites Autodock and Vina and demonstrate how the combination of docking and visualization can aid structure-based drug design efforts.

Protein Thermostability Calculations Using Alchemical Free Energy Simulations

Thermal stability of proteins is crucial for both biotechnological and therapeutic applications. Rational protein engineering therefore frequently aims at increasing thermal stability by introducing stabilizing mutations. The accurate prediction of the thermodynamic consequences caused by mutations, however, is highly challenging as thermal stability changes are caused by alterations in the free energy of folding. Growing computational power, however, increasingly allows us to use alchemical free energy simulations, such as free energy perturbation or thermodynamic integration, to calculate free energy differences with relatively high accuracy. In this article, we present an automated protocol for setting up alchemical free energy calculations for mutations of naturally occurring amino acids (except for proline) that allows an unprecedented, automated screening of large mutant libraries. To validate the developed protocol, we calculated thermodynamic stability differences for 109 mutations in the microbial Ribonuclease Barnase. The obtained quantitative agreement with experimental data illustrates the potential of the approach in protein engineering and design.

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.

Discovery of novel human aquaporin-1 blockers.

Human aquaporin-1 (hAQP1) is a water channel found in many tissues and potentially involved in several human pathologies. Selective inhibitors of hAQP1 are discussed as novel treatment opportunities for glaucoma, brain edema, inflammatory pain, and certain types of cancer. However, only very few potent and chemically attractive blockers have been reported to date. In this study we present three novel hAQP1 blockers that have been identified by virtual screening and inhibit water flux through hAQP1 in Xenopus laevis oocyte swelling assays at low micromolar concentrations. The newly discovered compounds display no chemical similarity to hitherto known hAQP1 blockers and bind at the extracellular entrance of the channel, close to the ar/R selectivity filter. Furthermore, mutagenesis studies showed that Lys36, which is not conserved among the hAQP family, is crucially involved in binding and renders the discovered compounds suitable as leads for the development of selective hAQP1 inhibitors.

What can we learn from molecular dynamics simulations for GPCR drug design

Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).

New Soft-Core Potential Function for Molecular Dynamics Based Alchemical Free Energy Calculations

The fields of rational drug design and protein engineering benefit from accurate free energy calculations based on molecular dynamics simulations. A thermodynamic integration scheme is often used to calculate changes in the free energy of a system by integrating the change of the systems Hamiltonian with respect to a coupling parameter. These methods exploit nonphysical pathways over thermodynamic cycles involving particle introduction and annihilation. Such alchemical transitions require the modification of the classical nonbonded potential energy terms by applying soft-core potential functions to avoid singularity points. In this work, we propose a novel formulation for a soft-core potential to be applied in nonequilibrium free energy calculations that alleviates singularities, numerical instabilities, and additional minima in the potential energy for all combinations of nonbonded interactions at all intermediate alchemical states. The method was validated by application to (a) the free energy calculations of a closed thermodynamic cycle, (b) the mutation influence on protein thermostability, (c) calculations of small ligand solvation free energies, and (d) the estimation of binding free energies of trypsin inhibitors. The results show that the novel soft-core function provides a robust and accurate general purpose solution to alchemical free energy calculations.

Quantitative Assessment of Protein Interaction with Methyl-Lysine Analogues by Hybrid Computational and Experimental Approaches

In cases where binding ligands of proteins are not easily available, structural analogues are often used. For example, in the analysis of proteins recognizing different methyl-lysine residues in histones, methyl-lysine analogues based on methyl-amino-alkylated cysteine residues have been introduced. Whether these are close enough to justify quantitative interpretation of binding experiments is however questionable. To systematically address this issue, we developed, applied, and assessed a hybrid computational/experimental approach that extracts the binding free energy difference between the native ligand (methyl-lysine) and the analogue (methyl-amino-alkylated cysteine) from a thermodynamic cycle. Our results indicate that measured and calculated binding differences are in very good agreement and therefore allow the correction of measured affinities of the analogues. We suggest that quantitative binding parameters for defined ligands in general can be derived by this method with remarkable accuracy.

tCONCOORD‐GUI: Visually supported conformational sampling of bioactive molecules

Conformational flexibility of bioactive molecules poses a major challenge to computational biology. tCONCOORD generates structure ensembles based on geometrical considerations and has been successfully applied to predict protein conformational flexibility and essential degrees of freedom. We have now developed a graphical user interface (GUI) for tCONCOORD, which substantially facilitates the simulation setup and provides valuable insights into the structure analysis and constraint definition process in tCONCOORD. Moreover, users can influence the constraint definition process by interactively turning interactions on and off, defining completely rigid or flexible regions, or by applying artifical constraints that cause a biased sampling of the conformational space. This interface offers a versatile environment not only for the setup and analysis of tCONCOORD simulations, but also for molecular modeling and structure analysis in general. Both tCONCOORD * and the tCONCOORD‐GUI † are distributed freely.

The Thermodynamic Influence of Trapped Water Molecules on a Protein-Ligand Interaction.

Water molecules doing time: Atomic-resolution crystal structures of the PPIase domain of cyclophilin G, alone and in complex with cyclosporin A, and together with MD simulations and calorimetry, reveal how trapped water molecules influence the thermodynamic profile of a protein-ligand interaction.

pmx: Automated protein structure and topology generation for alchemical perturbations.

Computational protein design requires methods to accurately estimate free energy changes in protein stability or binding upon an amino acid mutation. From the different approaches available, molecular dynamics‐based alchemical free energy calculations are unique in their accuracy and solid theoretical basis. The challenge in using these methods lies in the need to generate hybrid structures and topologies representing two physical states of a system. A custom made hybrid topology may prove useful for a particular mutation of interest, however, a high throughput mutation analysis calls for a more general approach. In this work, we present an automated procedure to generate hybrid structures and topologies for the amino acid mutations in all commonly used force fields. The described software is compatible with the Gromacs simulation package. The mutation libraries are readily supported for five force fields, namely Amber99SB, Amber99SB*‐ILDN, OPLS‐AA/L, Charmm22*, and Charmm36.