From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library.

Nowadays, computational approaches are an integral part of life science research. Problems related to interpretation of experimental results, data analysis, or visualization tasks highly benefit from the achievements of the digital era. Simulation methods facilitate predictions of physicochemical properties and can assist in understanding macromolecular phenomena. Here, we will give an overview of the methods developed in our group that aim at supporting researchers from all life science areas. Based on state-of-the-art approaches from structural bioinformatics and cheminformatics, we provide software covering a wide range of research questions. Our all-in-one web service platform ProteinsPlus (http://proteins.plus) offers solutions for pocket and druggability prediction, hydrogen placement, structure quality assessment, ensemble generation, protein-protein interaction classification, and 2D-interaction visualization. Additionally, we provide a software package that contains tools targeting cheminformatics problems like file format conversion, molecule data set processing, SMARTS editing, fragment space enumeration, and ligand-based virtual screening. Furthermore, it also includes structural bioinformatics solutions for inverse screening, binding site alignment, and searching interaction patterns across structure libraries. The software package is available at http://software.zbh.uni-hamburg.de.

FSees: Customized Enumeration of Chemical Subspaces with Limited Main Memory Consumption.

In the search for new marketable drugs, new ideas are required constantly. Particularly with regard to challenging targets and previously patented chemical space, designing novel molecules is crucial. This demands efficient and innovative computational tools to generate libraries of promising molecules. Here we present an efficient method to generate such libraries by systematically enumerating all molecules in a specific chemical space. This space is defined by a fragment space and a set of user-defined physicochemical properties (e.g., molecular weight, tPSA, number of H-bond donors and acceptors, or predicted logP). In order to enumerate a very large number of molecules, our algorithm uses file-based data structures instead of memory-based ones, thus overcoming the limitations of computer main memory. The resulting chemical library can be used as a starting point for computational lead-finding technologies, like similarity searching, pharmacophore mapping, docking, or virtual screening. We applied the algorithm in different scenarios, thus creating numerous target-specific libraries. Furthermore, we generated a fragment space from all approved drugs in DrugBank and enumerated it with lead-like constraints, thus generating 0.5 billion molecules in the molecular weight range 250-350.

The Structure-Function Linkage Database.

The Structure-Function Linkage Database (SFLD, http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies. Members of such superfamilies are diverse in their overall reactions yet share a common ancestor and some conserved active site features associated with conserved functional attributes such as a partial reaction. Thus, despite their different functions, members of these superfamilies 'look alike', making them easy to misannotate. To address this complexity and enable rational transfer of functional features to unknowns only for those members for which we have sufficient functional information, we subdivide superfamily members into subgroups using sequence information, and lastly into families, sets of enzymes known to catalyze the same reaction using the same mechanistic strategy. Browsing and searching options in the SFLD provide access to all of these levels. The SFLD offers manually curated as well as automatically classified superfamily sets, both accompanied by search and download options for all hierarchical levels. Additional information includes multiple sequence alignments, tab-separated files of functional and other attributes, and sequence similarity networks. The latter provide a new and intuitively powerful way to visualize functional trends mapped to the context of sequence similarity.

RosettaBackrub--a web server for flexible backbone protein structure modeling and design.

The RosettaBackrub server (http://kortemmelab.ucsf.edu/backrub) implements the Backrub method, derived from observations of alternative conformations in high-resolution protein crystal structures, for flexible backbone protein modeling. Backrub modeling is applied to three related applications using the Rosetta program for structure prediction and design: (I) modeling of structures of point mutations, (II) generating protein conformational ensembles and designing sequences consistent with these conformations and (III) predicting tolerated sequences at protein-protein interfaces. The three protocols have been validated on experimental data. Starting from a user-provided single input protein structure in PDB format, the server generates near-native conformational ensembles. The predicted conformations and sequences can be used for different applications, such as to guide mutagenesis experiments, for ensemble-docking approaches or to generate sequence libraries for protein design.

Graph Measures Reveal Fine Structure of Complexes Forming in Multiparticle Simulations.

Modern simulation techniques are beginning to be used to study the dynamic assembly and disassembly of multiprotein systems. In these many-particle simulations it can be very tedious to monitor the formation of specific structures such as fully assembled protein complexes or virus capsids above a background of monomers and partial complexes. However, such analyses can be performed conveniently when the spatial configuration is mapped onto a dynamically updated interaction graph. On the example of Monte Carlo simulations of spherical particles with either isotropic or directed mutual attractions, we demonstrate that this combined strategy allows for an efficient and also detailed analysis of complex formation in many-particle systems.

Molecular stochastic simulations of chromatophore vesicles from Rhodobacter sphaeroides.

A kinetic model is presented for photosynthetic processes under varying illumination based on the recently introduced steady state model of the photosynthetic chromatophore vesicles of the purple bacterium Rhodobacter sphaeroides. A stochastic simulation system is built up from independent copies of the different transmembrane proteins, each encapsulating its own set of binding sites and internal states. The proteins are then connected through pools for each of the metabolites. A number of steady state and time-dependent scenarios are presented showing that even under steady state conditions the stochastic model exhibits a different behavior than a continuous description. We find that the electronic coupling between the light harvesting complexes increases the efficiency of the core complexes which eventually allows the bacteria to bridge short illumination outages at already lower light intensities. Some new experiments are proposed by which the DeltapH dependent characteristic of the bc(1) complex or the proton buffering capacity of the vesicle could be determined.