Capturing structure-activity relationships from chemogenomic spaces.

Modeling off-target effects is one major goal of chemical biology, particularly in its applications to drug discovery. Here, we describe a new approach that allows the extraction of structure-activity relationships from large chemogenomic spaces starting from a single chemical structure. Several public source databases, offering a vast amount of data on structure and activity for a large number of different targets, have been investigated for their usefulness in automated structure-activity relationships (SAR) extraction. SAR tables were constructed by assembling similar structures around each query structure that have an activity record for a particular target. Quantitative series enrichment analysis (QSEA) was applied to these SAR tables to identify trends and to transform these trends into topomer CoMFA models. Overall more than 1700 SAR tables with topomer CoMFA models have been obtained from the ChEMBL, PubChem, and ChemBank databases. These models were able to highlight the structural trends associated with various off-target effects of marketed drugs, including cases where other structural similarity metrics would not have detected an off-target effect. These results indicate the usefulness of the QSEA approach, particularly whenever applicable with public databases, in providing a new means, beyond a simple similarity between ligand structures, to capture SAR trends and thereby contribute to success in drug discovery.

Multiple molecular dynamics simulations of TEM beta-lactamase: dynamics and water binding of the omega-loop.

The Omega-loop of TEM beta-lactamase is involved in substrate recognition and catalysis. Its dynamical properties and interaction with water molecules were investigated by performing multiple molecular dynamics simulations of up to 50 ns. Protein flexibility was assessed by calculating the root mean-square fluctuations and the generalized order parameter, S(2). The residues in secondary structure elements are highly ordered, whereas loop regions are more flexible, which is in agreement with previous experimental observations. Interestingly, the Omega-loop (residues 161-179) is rigid with order parameters similar to secondary structure elements, with the exception of the tip of the loop (residues 173-177) that has a considerably higher flexibility and performs an opening and closing motion on the 50-ns timescale. The rigidity of the main part of the Omega-loop is mediated by stabilizing and highly conserved water bridges inside a cavity lined by the Omega-loop and residues 65-69 of the protein core. In contrast, the flexible tip of the Omega-loop lacks these interactions. Hydration of the cavity and exchange of the water molecules with the bulk solvent occurs via two pathways: the flexible tip that serves as a door to the cavity, and a temporary water channel involving the side chain of Arg(164).

The Lactamase Engineering Database: a critical survey of TEM sequences in public databases.

BACKGROUND: TEM beta-lactamases are the main cause for resistance against beta-lactam antibiotics. Sequence information about TEM beta-lactamases is mainly found in the NCBI peptide database and TEM mutation table at http://www.lahey.org/Studies/temtable.asp. While the TEM mutation table is manually curated by experts in the lactamase field, who guarantee reliable and consistent information, the rapidly growing sequence and annotation information from the NCBI peptide database is sometimes inconsistent. Therefore, the Lactamase Engineering Database has been developed to collect the TEM beta-lactamase sequences from the NCBI peptide database and the TEM mutation table, systematically compare sequence information and naming, identify inconsistencies, and thus provide a versatile tool for reconciliation of data and for an investigation of the sequence-function relationship. DESCRIPTION: The LacED currently provides 2399 sequence entries and 37 structure entries. Sequence information on 150 different TEM beta-lactamases was derived from the TEM mutation table which provides a unique number to each protein classified as TEM beta-lactamase. 293 TEM-like proteins were found in the NCBI protein database, but only 113 TEM beta-lactamase were common to both data sets. The 180 TEM beta-lactamases from the NCBI protein database which have not yet been assigned to a TEM number fall in three classes: (1) 89 proteins from microbial organisms and 35 proteins from cloning or expression vectors had a new mutation profile; (2) 55 proteins had inconsistent annotation in terms of TEM assignment or reported mutation profile; (3) 39 proteins are fragments. The LacED is web accessible at http://www.LacED.uni-stuttgart.de and contains multisequence alignments, structure information and reconciled annotation of TEM beta-lactamases. The LacED is weekly updated and supplies all data for download. CONCLUSION: The Lactamase Engineering Database enables a systematic analysis of TEM beta-lactamase sequence and annotation data from different data sources, and thus provides a valuable tool to identify inconsistencies in sequences from the NCBI peptide database, to detect TEM beta-lactamases with a novel mutation profile, and to identify new amino acid positions at which mutations can occur.

Conserved water molecules stabilize the Omega-loop in class A beta-lactamases.

A set of 49 high-resolution (