Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery: Bridging Experiments and Computations - September 10-14, 2014, Istanbul, Turkey - page 86

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Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery Poster Session I
27-POS
Board 27
Discovery of a Cryptic Druggable Pocket in Human p53
Ozlem Demir
1
, Linda V. Hall
3,4
, Faezeh Salehi
2,3
, Scott Rychnovsky
7
, Peter Kaiser
3,4,5
, Richard
H. Lathrop
2,3,5
, Vicki Feher
1,10
, Rommie E. Amaro
1,8,9
.
1
University of California, San Diego, La Jolla, CA, USA,
2
University of California, Irvine,
Irvine, CA, USA,
3
University of California, Irvine, Irvine, CA, USA,
4
University of California,
Irvine, Irvine, CA, USA,
5
University of California, Irvine, Irvine, CA, USA,
6
University of
California, Irvine, Irvine, CA, USA,
7
University of California, Irvine, Irvine, CA, USA,
8
University of California, San Diego, La Jolla, CA, USA,
9
University of California, San Diego,
La Jolla, CA, USA,
10
University of California, San Diego, La Jolla, CA, USA.
The tumor suppressor p53 has a major role in the defense of a cell against cancer. Cancer can
only proceed after p53 or its pathways are inactivated by mutations. Thus, reactivation of mutant
p53 with small-molecules have been a long-standing idea for potential cancer treatment. We
explored the dynamic ensemble of many p53 mutants as well as the wild-type protein using
molecular dynamics (MD) simulations using NAMD suite. In the MD-generated ensembles of
p53 mutants, we have identified a transiently open binding pocket occluded in the available
crystal structures. Virtual screening against different conformations of this cryptic pocket
identified 45 promising compounds among which stictic acid emerged as a potential p53
reactivation compound. Stictic acid demonstrated dose-dependent p21 activation in human
osteosarcoma cells with R175H mutant of p53. Encouraged by this result, we have performed
virtual screening of 1.7 million compounds against a single pocket-open conformation of p53.
Among the top 1% the compounds with the highest binding scores, we used machine learning to
select a set of 138 diverse compounds out of which 15 compounds (~11% hit rate) were found to
be potential p53 reactivation compounds by biological assays. Our findings highlight this cryptic
druggable pocket as a promising pharmaceutical target for p53 reactivation.
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