ci5b00338_si_001.pdf (1.08 MB)
Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment
journal contribution
posted on 2015-07-29, 00:00 authored by Matthew
P. Baumgartner, Carlos J. CamachoThe 2013/2014 Community Structure–Activity
Resource (CSAR)
challenge was designed to prospectively validate advancement in the
field of docking and scoring receptor–small molecule interactions.
Purely computational methods have been found to be quite limiting.
Thus, the challenges assessed methods that combined both experimental
data and computational approaches. Here, we describe our contribution
to solve three important challenges in rational drug discovery: rank-ordering
protein primary sequences based on affinity to a compound, determining
close-to-native bound conformations out of a set of decoy poses, and
rank-ordering sets of congeneric compounds based on affinity to a
given protein. We showed that the most significant contribution to
a meaningful enrichment of native-like models was the identification
of the best receptor structure for docking and scoring. Depending
on the target, the optimal receptor for cross-docking and scoring
was identified by a self-consistent docking approach that used the
Vina scoring function, by aligning compounds to the closest cocrystal
or by selecting the cocrystal receptor with the largest pocket. For
tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric
binding compounds cross-docked to the optimal receptor resulted in
a R2 = 0.67; whereas, using any other
of the 13 receptor structures led to almost no enrichment of native-like
complex structures. Furthermore, although redocking predicted lower
RMSDs relative to the bound structures, the ranking based on multiple
receptor structures did not improve the correlation coefficient. Our
predictions highlight the role of rational structure-based modeling
in maximizing the outcome of virtual screening, as well as limitations
scoring multiple receptors.