ABSTRACT
Structural genomics projects are producing protein structure data at an unprecedented rate. In this paper, we present the Target Informatics Platform (TIP), a novel structural informatics approach for amplifying the rapidly expanding body of experimental protein structure information to enhance the discovery and optimization of small molecule protein modulators on a genomic scale. In TIP, existing experimental structure information is augmented using a homology modeling approach, and binding sites across multiple target families are compared using a clique detection algorithm. We report here a detailed analysis of the structural coverage for the set of druggable human targets, highlighting drug target families where the level of structural knowledge is currently quite high, as well as those areas where structural knowledge is sparse. Furthermore, we demonstrate the utility of TIP's intra- and inter-family binding site similarity analysis using a series of retrospective case studies. Our analysis underscores the utility of a structural informatics infrastructure for extracting drug discovery-relevant information from structural data, aiding researchers in the identification of lead discovery and optimization opportunities as well as potential "off-target" liabilities.
Subject(s)
Drug Design , Enzyme Inhibitors/pharmacology , Genome, Human , Informatics , Proteins/chemistry , Databases, Factual , Enzyme Inhibitors/chemistry , Genomics/methods , Humans , Models, Molecular , Molecular Structure , Proteins/genetics , Proteins/metabolism , Structure-Activity RelationshipABSTRACT
Advances in protein crystallography and homology modeling techniques are producing vast amounts of high resolution protein structure data at ever increasing rates. As such, the ability to quickly and easily extract structural similarities is a key tool in discovering important functional relationships. We report on an approach for creating and maintaining a database of pairwise structure alignments for a comprehensive database comprising the PDB and homology models for the human and select pathogen genomes. Our approach consists of a novel, multistage method for determining pairwise structural similarity coupled with an efficient clustering protocol that approximates a full NxN assessment in a fraction of the time. Since biologists are commonly interested in recently released structures, and the homology models built from them, an automatically updating database of structural alignments has great value. Our approach yields a querying system that allows scientists to retrieve databank-wide protein structure similarities as easily as retrieving protein sequence similarities via BLAST or PSI-BLAST. Basic, noncommercial access to the database can be requested at https://tip.eidogen-sertanty.com/.
Subject(s)
Databases, Protein , Protein Conformation , Models, ChemicalABSTRACT
MOTIVATION: Background distribution statistics for profile-based sequence alignment algorithms cannot be calculated analytically, and hence such algorithms must resort to measuring the significance of an alignment score by assessing its location among a distribution of background alignment scores. The Gumbel parameters that describe this background distribution are usually pre-computed for a limited number of scoring systems, gap schemes, and sequence lengths and compositions. The use of such look-ups is known to introduce errors, which compromise the significance assessment of a remote homology relationship. One solution is to estimate the background distribution for each pair of interest by generating a large number of sequence shuffles and use the distribution of their scores to approximate the parameters of the underlying extreme value distribution. This is computationally very expensive, as a large number of shuffles are needed to precisely estimate the score statistics. RESULTS: Convergent Island Statistics (CIS) is a computationally efficient solution to the problem of calculating the Gumbel distribution parameters for an arbitrary pair of sequences and an arbitrary set of gap and scoring schemes. The basic idea behind our method is to recognize the lack of similarity for any pair of sequences early in the shuffling process and thus save on the search time. The method is particularly useful in the context of profile-profile alignment algorithms where the normalization of alignment scores has traditionally been a challenging task. CONTACT: aleksandar@eidogen.com SUPPLEMENTARY INFORMATION: http://www.eidogen-sertanty.com/Documents/convergent_island_stats_sup.pdf.
