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Chiquito J Crasto

Publications and source records attributed to Chiquito J Crasto.

3 recordsLinked to original sources

Structural activation pathways from dynamic olfactory receptor-odorant interactions.

We have simulated an odor ligand's dynamic behavior in the binding region of an olfactory receptor (OR). Our short timescale computational studies (up to 200 ps) have helped identify unprecedented postdocking ligand behavior of ligands. From in vacuo molecular dynamics simulations of interactions between models of rat OR I7 and 10 aldehyde ligands, we have identified a dissociative pathway along which the ligand exits and enters the OR-binding pocket--a transit event. The ligand's transit through the receptor's binding region may mark the beginning of a signal transduction cascade leading to odor recognition. We have graphically traced the rotameric changes in key OR amino acid side chains during the transit. Our results have helped substantiate or refute previously held notions of amino acid contribution to ligand stability in the binding pocket. Our observations of ligand activity when compared to those of experimental (electroolfactogram response) OR-activation studies provide a view to predicting the stability of ligands in the binding pocket as a precursor to OR activation by the ligand.

Aldehydes↗

Text mining neuroscience journal articles to populate neuroscience databases.

We have developed a program NeuroText to populate the neuroscience databases in SenseLab (http://senselab.med.yale.edu/senselab) by mining the natural language text of neuroscience articles. NeuroText uses a two-step approach to identify relevant articles. The first step (pre-processing), aimed at 100% sensitivity, identifies abstracts containing database keywords. In the second step, potentially relevant abstracts identified in the first step are processed for specificity dictated by database architecture, and neuroscience, lexical and semantic contexts. NeuroText results were presented to the experts for validation using a dynamically generated interface that also allows expert-validated articles to be automatically deposited into the databases. Of the test set of 912 articles, 735 were rejected at the pre-processing step. For the remaining articles, the accuracy of predicting database-relevant articles was 85%. Twenty-two articles were erroneously identified. NeuroText deferred decisions on 29 articles to the expert. A comparison of NeuroText results versus the experts' analyses revealed that the program failed to correctly identify articles' relevance due to concepts that did not yet exist in the knowledgebase or due to vaguely presented information in the abstracts. NeuroText uses two "evolution" techniques (supervised and unsupervised) that play an important role in the continual improvement of the retrieval results. Software that uses the NeuroText approach can facilitate the creation of curated, special-interest, bibliography databases.

Abstracting and Indexing↗

Creating knowledgebases to text-mine PUBMED articles using clustering techniques.

Knowledgebase-mediated text-mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing

Algorithms↗