David Michalovich

ChEMBL: a large-scale bioactivity database for drug discovery

ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb.

Protein sequence analysis in silico: application of structure-based bioinformatics to genomic initiatives.

The current pace of high-throughput genome sequencing programs coupled with high-throughput functional genomic screens has provided researchers with a bewildering array of sequence and biological data to contend with. Identification of proteins of interest from a particular biological study requires the application of bioinformatic tools to process and prioritise the data. From a protein function standpoint, transfer of annotation from known proteins to a novel target is currently the only practical way to convert vast quantities of raw sequence data into meaningful information. New bioinformatics tools now provide more sophisticated methods to transfer functional annotation, integrating sequence, family profile and structural search methodology. The importance of these approaches to medical research is increasing as we move to annotate the proteome through functional and structural genomic efforts.

Bioinformatic approaches to assigning protein function from novel sequence data.

The current pace of functional genomic initiatives and genome sequencing projects has provided researchers with a bewildering array of sequence and biological data to analyze. The disease system-driven approach to identifying key genes frequently identifies nucleotide and protein sequences for which the gene and protein function are not known in sufficient detail to allow informed follow-up. Using a range of bioinformatic tools and sequence-based clues, most of unassigned sequences can now be annotated. This chapter takes as an example an unannotated expressed sequence tag, describing how to identify its related gene, and how to annotate the encoded protein using sequence, profile, and structure-based annotation methodologies.