537
15 (How to) Profit from Molecular Dynamics-based Ensemble Docking
107. Kuhn B, Fuchs JE, Reutlinger M, Stahl M, Taylor NR (2011) Rationalizing tight ligand
binding through cooperative interaction networks. J Chem Inf Model 51(12):3180–3198.
doi:10.1021/ci200319e
108. Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner
RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD
datasets: a complete set of glide SP results and selected results for a new scoring function
integrating WaterMap and glide. J Comput-Aided Mol Des 26(6):787–799. doi:10.1007/
s10822-012-9575-9
109. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent
in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831.
doi:10.1021/ja0771033
110. Cummings MD, Arnoult É, Buyck C, Tresadern G, Vos AM, Wegner JK (2011) Preparing
and filtering compound databases for virtual and experimental screening. In: Sotriffer C (ed)
Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim,
pp 35–59
111. Mason JS, Bortolato A, Congreve M, Marshall FH (2012) New insights from structural biology into the druggability of G protein-coupled receptors. Trends Pharmacol Sci 33(5):249–
260. doi:10.1016/j.tips.2012.02.005
112. Smith RD, Dunbar JB Jr, Ung PM-U, Esposito EX, Yang C-Y, Wang S, Carlson HA (2011)
CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51(9):2115–2131. doi:10.1021/ci200269q
113. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring
functions: 1. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput-Aided Mol Des 11(5):425–445.
doi:10.1023/a:1007996124545
114. Bottegoni G, Rocchia W, Rueda M, Abagyan R, Cavalli A (2011) Systematic exploitation
of multiple receptor conformations for virtual ligand screening. PloS One 6(5):e18845.
doi:10.1371/journal.pone.0018845
115. Xu M, Lill MA (2012) Utilizing experimental data for reducing ensemble size in flexibleprotein docking. J Chem Inf Model 52(1):187–198. doi:10.1021/ci200428t
116. Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004) Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 337(5):1161–1182.
doi:10.1016/j.jmb.2004.02.015
117. Eriksson AE, Baase WA, Zhang XJ, Heinz DW, Blaber M, Baldwin EP, Matthews BW
(1992) Response of a protein-structure to cavity-creating mutations and its relation to the
hydrophobic effect. Science 255(5041):178–183. doi:10.1126/science.1553543
118. Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443. doi:10.1021/
jm048972v
119. Minh DDL (2012) Implicit ligand theory: rigorous binding free energies and thermodynamic
expectations from molecular docking. J Chem Phys 137(10). doi:10.1063/1.4751284
120. Feng JA, Marshall GR (2010) SKATE: a docking program that decouples systematic sampling from scoring. J Comput Chem 31(14):2540–2554. doi:10.1002/jcc.21545
121. Korb O, McCabe P, Cole J (2011) The ensemble performance index: an improved measure
for assessing ensemble pose prediction performance. J Chem Inf Model 51(11):2915–2919.
doi:10.1021/ci2002796
122. Nicholls A (2008) What do we know and when do we know it?. J Comput-Aided Mol Des
22(3–4):239–255. doi:10.1007/s10822-008-9170-2
123. Peterson W, Birdsall T, Fox W (1954) The theory of signal detectability. Trans IRE Prof
Group Inform Theory 4(4):171–212
124. Okimoto N, Futatsugi N, Fuji H, Suenaga A, Morimoto G, Yanai R, Ohno Y, Narumi T, Taiji
M (2009) High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations. PloS Comp Biol 5(10):e1000528. doi:10.1371/
journal.pcbi.1000528
15 (How to) Profit from Molecular Dynamics-based Ensemble Docking
107. Kuhn B, Fuchs JE, Reutlinger M, Stahl M, Taylor NR (2011) Rationalizing tight ligand
binding through cooperative interaction networks. J Chem Inf Model 51(12):3180–3198.
doi:10.1021/ci200319e
108. Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner
RA (2012) Docking performance of the glide program as evaluated on the Astex and DUD
datasets: a complete set of glide SP results and selected results for a new scoring function
integrating WaterMap and glide. J Comput-Aided Mol Des 26(6):787–799. doi:10.1007/
s10822-012-9575-9
109. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent
in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831.
doi:10.1021/ja0771033
110. Cummings MD, Arnoult É, Buyck C, Tresadern G, Vos AM, Wegner JK (2011) Preparing
and filtering compound databases for virtual and experimental screening. In: Sotriffer C (ed)
Virtual screening: principals, challenges, and practical guidelines, vol 82. Wiley, Weinheim,
pp 35–59
111. Mason JS, Bortolato A, Congreve M, Marshall FH (2012) New insights from structural biology into the druggability of G protein-coupled receptors. Trends Pharmacol Sci 33(5):249–
260. doi:10.1016/j.tips.2012.02.005
112. Smith RD, Dunbar JB Jr, Ung PM-U, Esposito EX, Yang C-Y, Wang S, Carlson HA (2011)
CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J Chem Inf Model 51(9):2115–2131. doi:10.1021/ci200269q
113. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring
functions: 1. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput-Aided Mol Des 11(5):425–445.
doi:10.1023/a:1007996124545
114. Bottegoni G, Rocchia W, Rueda M, Abagyan R, Cavalli A (2011) Systematic exploitation
of multiple receptor conformations for virtual ligand screening. PloS One 6(5):e18845.
doi:10.1371/journal.pone.0018845
115. Xu M, Lill MA (2012) Utilizing experimental data for reducing ensemble size in flexibleprotein docking. J Chem Inf Model 52(1):187–198. doi:10.1021/ci200428t
116. Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004) Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol 337(5):1161–1182.
doi:10.1016/j.jmb.2004.02.015
117. Eriksson AE, Baase WA, Zhang XJ, Heinz DW, Blaber M, Baldwin EP, Matthews BW
(1992) Response of a protein-structure to cavity-creating mutations and its relation to the
hydrophobic effect. Science 255(5041):178–183. doi:10.1126/science.1553543
118. Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443. doi:10.1021/
jm048972v
119. Minh DDL (2012) Implicit ligand theory: rigorous binding free energies and thermodynamic
expectations from molecular docking. J Chem Phys 137(10). doi:10.1063/1.4751284
120. Feng JA, Marshall GR (2010) SKATE: a docking program that decouples systematic sampling from scoring. J Comput Chem 31(14):2540–2554. doi:10.1002/jcc.21545
121. Korb O, McCabe P, Cole J (2011) The ensemble performance index: an improved measure
for assessing ensemble pose prediction performance. J Chem Inf Model 51(11):2915–2919.
doi:10.1021/ci2002796
122. Nicholls A (2008) What do we know and when do we know it?. J Comput-Aided Mol Des
22(3–4):239–255. doi:10.1007/s10822-008-9170-2
123. Peterson W, Birdsall T, Fox W (1954) The theory of signal detectability. Trans IRE Prof
Group Inform Theory 4(4):171–212
124. Okimoto N, Futatsugi N, Fuji H, Suenaga A, Morimoto G, Yanai R, Ohno Y, Narumi T, Taiji
M (2009) High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations. PloS Comp Biol 5(10):e1000528. doi:10.1371/
journal.pcbi.1000528