Chemical Information Bulletin, Volume 60, Number 2, Fall 2008 Page: 32 of 56
52 p. ; 28 cm.View a full description of this periodical.
Extracted Text
The following text was automatically extracted from the image on this page using optical character recognition software:
CINF 21. Cheminformatics analysis of HIV-1
protease mutations. Gene M. Ko',
gko@ rohan.sdsu.edu, A. Srinivas Reddy~,
astreddy @g mail. coi, Sunil Kumar9,
skunmar@s malil.sdcsu. edu, and Rajni Gargl,
iclarg @ csusm. edu. (1) Computational Science
Research Center, San Diego State University, 5500
Camnpanile Drive, San Diego, CA 92182-1245, (2)
Electrical alnd Computer Engineering Department,
San Diego State University, San Diego, CA 92182-
1309
Mutations that arise in HIV-1 protease after exposure
to various HIV-I protease inhibitors have proved to be
a difficult aspect in the treatment of HIV. The crystal
structures of 52 HIV-I proteases complexed with FDA
approved protease inhibitors from the Protein Data
Bank (PDB) were studied. The information reported
by the PDB for each crystal structure has been found
to be error prone due to the nature of the PDB
verification process. Incorrect structural classifications
reported by the database may lead to potential
structures being overlooked during the dataset
collection process. The inconsistent mutation
information also leads to incorrect data parameters in
one's own research. Each of the 52 structures was
aligned against the wild-type HXB2 HIV-1 protease
strain to create a baseline sequence from which
mutations can be identified. The mutations were
mapped according to their bound ligand in an attempt
to analyze the mutations for each protease inhibitor.
CINF 22. Extracting chemical protein interactions
from literature using natural language processing
methods. Dazhi Jiao, djiao@indiana.edu, School of
Informnatics, Indiana University at Bloomington, Wells
Library 043, Bloomington, IN 47405, and David J
Wild, djlwild@ indiana.edu, School of Informatics,
Indicana University, Bloomington, IN 47408
This poster describes the development of a system to
automatically build database and entity representation
of chemical protein interactions based on information
extracted from abstracts of journal articles, using
machine learning and natural language processing
methods. In this system, abstracts related to proteins
and chemical interactions are preprocessed using
named entity recognition methods to identify chemical
names and protein names. Chemical structures are also
attached to chemical names for future processing. Thetexts are then syntactically analyzed, and grammatical
relationships between constituents of the sentences are
generated. Then interactions between proteins and
chemicals are extracted by identifying certain
keywords, together with the protein and chemical
names based on the dependency graph. The extracted
information, including the chemical compounds, their
structures, the proteins, and the interactions between
chemicals and proteins are stored in a database for
retrieval and further analysis. The information are also
represented based on biological ontologies for
molecular interaction networks. In this poster, the
training process to build certain components of the
system, problems encountered during the system
creation, and the creation of the database and ontology
based representation will be discussed in detail.
CINF 23. Hierarchical screening with multiple
receptor structures to target the nonnucleoside
binding site of HIV-1 reverse transcriptase. Sara
E. Nichols', sara.nichols@yale.edu, Christopher
Bailey', Robert Domaoal~, Ligong Wang-, Karen S.
Anderson~, and William L. Jorgensent- ,
william.jorgensen @yale. edu. (1) Interdepartmental
program in Computational Biology and
Bioinfornatics, Yale University, New Haen, CT
06511, (2) Department of Pharmacology, Yale
Medical School, (3) Interdepartmental program in
Computational Biology and Bioinfornmatics,
Department of Chemistry, Yale University
At present, multiple protein targets are being
investigated in order to suppress the retrovirus HIV-1.
The target of our study, reverse transcriptase (RT),
translates the single stranded RNA of HIV into DNA.
There are notable entries in the Protein Data Bank of
an alternative conformation of residue 181, located at
the non-nucleoside binding site of RT, which is
different from the most common bound conformations.
The significance of Y181 interactions with the ligand
are confirmed by the resistance conferred upon Y181C
mutation. These interactions are integral to inhibitor
activity and the alternative conformation provides new
information about the dynamic nature of the binding
site. Our study uses this knowledge to screen a large
database, specifically targeting inhibitors which can
accommodate the conformational variations of residue
181. Since there is a lack of standard protocol for
flexible receptor docking in the literature, we present a
case study of RT which compromises between speed30
Chemical Information Bulletin, Vol. 60, No 2 (Fall) 2008
http://www.acscinf.org
Upcoming Pages
Here’s what’s next.
Search Inside
This issue can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Periodical.
American Chemical Society. Division of Chemical Information. Chemical Information Bulletin, Volume 60, Number 2, Fall 2008, periodical, Autumn 2008; Philadelphia, Pennsylvania. (https://digital.library.unt.edu/ark:/67531/metadc11506/m1/32/: accessed April 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .