The importance of taking into account protein flexibility in drug de
sign and virtual ligand
screening (VS) ha
s been widely debated in the literature, and molecular dynamic
s (MD) ha
s been recognized a
s one of the mo
st powerful tool
s for inve
stigating intrin
sic protein dynamic
s. Neverthele
ss, deciphering the amount of information hidden in MD
simulation
s and recognizing a
significant minimal
set of
state
s to be u
sed in virtual
screening experiment
s can be quite complicated. Here we pre
sent an integrated MD鈥揊LAP (molecular dynamic
s鈥揻ingerprint
s for ligand and protein
s) approach, compri
sing a pipeline of molecular dynamic
s, clu
stering and linear di
scriminant analy
si
s, for enhancing accuracy and efficacy in VS campaign
s. We fir
st extracted a limited number of repre
sentative
structure
s from ten
s of nano
second
s of MD trajectorie
s by mean
s of the k-medoid
s clu
stering algorithm a
s implemented in the BiKi Life Science Suite (
ss="extLink">http://www.bikitech.com [accessed July 21, 2015]). Then, instead of applying arbitrary selection criteria, that is, RMSD, pharmacophore properties, or enrichment performances, we allowed the linear discriminant analysis algorithm implemented in FLAP (scovery.com" class="extLink">http://www.moldiscovery.com [accessed July 21, 2015]) to automatically choose the best performing conformational states among medoids and X-ray structures. Retrospective virtual screenings confirmed that ensemble receptor protocols outperform single rigid receptor approaches, proved that computationally generated conformations comprise the same quantity/quality of information included in X-ray structures, and pointed to the MD鈥揊LAP approach as a valuable tool for improving VS performances.