Biomedical subjects

Daniel R Caffrey

Publications and source records attributed to Daniel R Caffrey.

6 recordsLinked to original sources

PubMed2007

Structure-based maximal affinity model predicts small-molecule druggability.

Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.

Algorithms↗

PubMed2005

Of mice and man: TLR11 (finally) finds profilin.

Toll-like receptors (TLRs) are primordial pathogen-recognition proteins that function as sentinels for the innate immune system. One of the TLR mysteries relates to TLR11, a receptor present in mice, but not humans, and known to recognize uropathogenic Escherichia coli. The first defined ligand for TLR11 has now been described as a profilin-like protein from Toxoplasma gondii. This discovery potentially gives us important clues as to how a gene expressed in mice, but not humans, actually relates to human infectious diseases.

Animals↗

PubMed2004

Are protein-protein interfaces more conserved in sequence than the rest of the protein surface?

Protein interfaces are thought to be distinguishable from the rest of the protein surface by their greater degree of residue conservation. We test the validity of this approach on an expanded set of 64 protein-protein interfaces using conservation scores derived from two multiple sequence alignment types, one of close homologs/orthologs and one of diverse homologs/paralogs. Overall, we find that the interface is slightly more conserved than the rest of the protein surface when using either alignment type, with alignments of diverse homologs showing marginally better discrimination. However, using a novel surface-patch definition, we find that the interface is rarely significantly more conserved than other surface patches when using either alignment type. When an interface is among the most conserved surface patches, it tends to be part of an enzyme active site. The most conserved surface patch overlaps with 39% (+/- 28%) and 36% (+/- 28%) of the actual interface for diverse and close homologs, respectively. Contrary to results obtained from smaller data sets, this work indicates that residue conservation is rarely sufficient for complete and accurate prediction of protein interfaces. Finally, we find that obligate interfaces differ from transient interfaces in that the former have significantly fewer alignment gaps at the interface than the rest of the protein surface, as well as having buried interface residues that are more conserved than partially buried interface residues.

Amino Acid Sequence↗

PubMed2003

LPS-TLR4 signaling to IRF-3/7 and NF-kappaB involves the toll adapters TRAM and TRIF.

Toll-IL-1-resistance (TIR) domain-containing adaptor-inducing IFN-beta (TRIF)-related adaptor molecule (TRAM) is the fourth TIR domain-containing adaptor protein to be described that participates in Toll receptor signaling. Like TRIF, TRAM activates interferon regulatory factor (IRF)-3, IRF-7, and NF-kappaB-dependent signaling pathways. Toll-like receptor (TLR)3 and 4 activate these pathways to induce IFN-alpha/beta, regulated on activation, normal T cell expressed and secreted (RANTES), and gamma interferon-inducible protein 10 (IP-10) expression independently of the adaptor protein myeloid differentiation factor 88 (MyD88). Dominant negative and siRNA studies performed here demonstrate that TRIF functions downstream of both the TLR3 (dsRNA) and TLR4 (LPS) signaling pathways, whereas the function of TRAM is restricted to the TLR4 pathway. TRAM interacts with TRIF, MyD88 adaptor-like protein (Mal)/TIRAP, and TLR4 but not with TLR3. These studies suggest that TRIF and TRAM both function in LPS-TLR4 signaling to regulate the MyD88-independent pathway during the innate immune response to LPS.

Adaptor Proteins, Vesicular Transport↗

PubMed2003

Wrapping up BLAST and other applications for use on Unix clusters.

UNLABELLED: We have developed two programs that speed up common bioinformatic applications by spreading them across a UNIX cluster.(1) BLAST.pm, a new module for the 'MOLLUSC' package. (2) WRAPID, a simple tool for parallelizing large numbers of small instances of programs such as BLAST, FASTA and CLUSTALW. AVAILABILITY: The packages were developed in Perl on a 20-node Linux cluster and are provided together with a configuration script and documentation. They can be freely downloaded from http://wolfe.gen.tcd.ie/wrapper.

Computer Communication Networks↗

PubMed2002

Improved database searches for orthologous sequences by conditioning on outgroup sequences.

MOTIVATION: Searches of biological sequence databases are usually focussed on distinguishing significant from random matches. However, the increasing abundance of related sequences on databases present a second challenge: to distinguish the evolutionarily most closely related sequences (often orthologues) from more distantly related homologues. This is particularly important when searching a database of partial sequences, where short orthologous sequences from a non-conserved region will score much more poorly than non-orthologous (outgroup) sequences from a conserved region. RESULTS: Such inferences are shown to be improved by conditioning the search results on the scores of an outgroup sequence. The log-odds score for each target sequence identified on the database has the log-odds score of the outgroup sequence subtracted from it. A test group of Caenorhabditis elegans kinase sequences and their identified C.elegans outgroups were searched against a test database of human Expressed Sequence Tag (EST) sequences, where the sets of true target sequences were known in advance. The outgroup conditioned method was shown to identify 58% more true positives ahead of the first false positive, compared to the straightforward search without an outgroup. A test dataset of 151 proteins drawn from the C.elegans genome, where the putative 'outgroup' was assigned automatically, similarly found 50% more true positives using outgroup conditioning. Thus, outgroup conditioning provides a means to improve the results of database searching with little increase in the search computation time.

Algorithms↗