Download Binding of Mutant Tat Peptides to TAR RNA as a Model for

Binding of Mutant Tat Peptides to TAR RNA as a Model for Developing
Hierarchical Methods Utilizing RNA-Protein Statistical Potentials
Phuc Tran, Thanh Le, Artem Shosnikov, Takayuki Kimura and Brooke Lustig*
Department of Chemistry, San Jose State University, 1 Washington Street, San Jose, California,
95132.
Interactions between viral encoded regulatory proteins and RNA target sequences control gene
expression of lentiviruses, including human immunodeficiency virus (HIV). Bovine
immunodeficiency virus (BIV) provides a simpler model of interaction between the viral
transactivator protein (Tat) and trans-activation response RNA element (TAR), using Tat
peptides binding to TAR RNA fragments. Besides offering insights into clinical approaches to
treatment, this complex offers a simple model system to better understand RNA-protein
interactions via theoretical consideration of their flexibility, utilizing literature-based binding
assays and NMR. The resulting characterization of the hinge region of BIV TAR-Tat complex
has been implemented, where K75 and R78 are considered possible residue positions for
substitution by glycines. The resulting substitutions allow alternative binding structures and
RNA-protein contacts. Molecular modeling with SCWRL4 and NPDOCK indicates that the
double substitution G75 and G78 results in a very different 14-mer peptide structure excluded
from the major groove of the RNA and the single substituted G75 peptide appears more stable
than the native. However these modeling methods are by design constrained, a result of the very
extensive search space associated with RNA-protein complexes. To test the robustness of these
initial computational studies more extensive hierarchical modeling is required. This includes the
application of statistical potentials describing coarse-grained features in a contact of a nucleotide
with an amino acid as opposed to all-atom descriptions. We have developed an updated set of
such potentials using BIV TAR-Tat as an initial model for application and validation.
Phuc Tran [email protected]
Thanh Le [email protected]
Artem Soshnikov [email protected]
takayuki kimura [email protected]
*Brooke Lustig [email protected]