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.

Relative ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design.

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.

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.

The fast and accurate prediction of protein flexibility is one of the major challenges in protein science. Enzyme activity, signal transduction, and ligand binding are dynamic processes involving essential conformational changes ranging from small side chain fluctuations to reorientations of entire domains. In the present work, we describe a reimplementation of the CONCOORD approach, termed tCONCOORD, which allows a computationally efficient sampling of conformational transitions of a protein based on geometrical considerations. Moreover, it allows for the extraction of the essential degrees of freedom, which, in general, are the biologically relevant ones. The method rests on a reliable estimate of the stability of interactions observed in a starting structure, in particular those interactions that change during a conformational transition. Applications to adenylate kinase, calmodulin, aldose reductase, T4-lysozyme, staphylococcal nuclease, and ubiquitin show that experimentally known conformational transitions are faithfully predicted.