can free energies really be free? alchemical free energy calculations in the cloud

62 Pages·2017·5.33 MB·English

can free energies really be free?
alchemical free energy
calculations in the cloud
John D. Chodera
MSKCC Computational Biology Program
http://www.choderalab.org
Slides available at http://www.choderalab.org
DISCLOSURES:

Scientific Advisory Board, Schrödinger
All opinions/views are my own. OpenEye CUP XVII - 8 Mar 2017 - Santa Fe, NM
How accurate does one need to be
to have an impact on drug discovery?
4 Shirts, Mobley, and Brown
binding free energy gain in lead optimization synthesis
0.7
affinity gain
Unassisted Distribution
Abbott medicinal chemists
d 0.6 Screened with 0.5 kcal/mol noise
0.5 kcal/mol accuracy goal of 1 log unit
n
o S1.c0r kecealn/meodl a wcciuthra c1y.0 kcal/mol noise
p
Screened with 2.0 kcal/mol noise
m 2.0 kcal/mol accuracy
o 0.5 1.4 kcal/mol
C
8x
reduction in time

f
o
to goal
s
i 0.4
s
e
h
t 5x
n
y
0.3
S

f
o

y
t
i
l 0.2
i 3x
b
a
b
o
r
P 0.1
1x
0
-2 0 2 4
Binding Energy (kcal/mol)
A 2 kcal/mol error in prioritizing lead synthesis would speed lead optimization by 3x
but even 10% improvements would be of tremendous benefit
Fig. 1.1. Distribution of drug a⇤nities of the chemist’s predictions (blue) compared
M. R. Shirts, D. L. Mobley and Scott P. Brown. "Free energy calculations in structure-based drug design",
to the distribution of drug a⇤nities after selection by computer with computation
in Drug Design: Structure- and Ligand-Based Approaches, pgs. 61-86, 2010.
error � = 0.5 (purple), � = 1.0 (pink), and � = 2.0 (red). The shaded area represents
the total probability of a proposed modification with a⇤nity gain greater than 1.4
kcal/mol. In many situations, Even with moderate error, a reliable method of
filtering compounds could significantly improve the e⇤cency of synthesis in lead
optimization.
one round of synthesis. With 1.0 kcal/mol error, we still have 36% chance
of achieving the goal with the first molecule synthesized, for about a 5 fold
decrease in median number synthesized. Surprisingly, even with 2 kcal/mol
computational noise the time to the goal is reduced about threefold. Simi-
lar computations can be done with large numbers of computer evaluations;
unsurprisingly, the more computational evaluations can be done, the more
computational noise can be tolerated and still yield useful time savings. For
example, even with 2 kcal/mol error, if 100 molecules can be screened, num-
ber of molecules required to be synthesized is reduced eightfold, similar to
the results for 10 molecules and 0.5 kcal/mol error.
Even relatively small numbers of moderately accurate computer predic-
tions may be able to give significant advantage in the pharmaceutical work
flow; 100 screened molecules with 2 kcal/mol noise or 10 screens with 1
kcal/mol noise in our example process could reduce the number of molecules
required to be synthesized by almost an order of magnitude. Clearly, these
calculations assume the simulations are not biased against active compounds,
and errors that are highly dependent on the binding system would result in
less reliable advantages. But physically based prediction methods should in
What details are crucial
for accuracy?
NATURE
Born-Opppenheimer approximation
Limited treatment of QM (DFT or semiempirical)
Implicit treatment of some electrons
QM/MM
Implicit representation of all electrons
POLARIZABLE
MM
Neglect of polarization
Representation of multipolar moments
by fixed charges
MM
entropy
Rigid receptor
enthalpy
Rigid or semi-rigid ligand
Single-configuration scoring
conformational
Simple solvation model
heterogeneity
DOCKING
What details are crucial
for accuracy?
NATURE
Born-Opppenheimer approximation
Limited treatment of QM (DFT or semiempirical)
Implicit treatment of some electrons
if insufficiently accurate,
systematically add detail
QM/MM
Implicit representation of all electrons
POLARIZABLE
MM
Neglect of polarization
Representation of multipolar moments
V K r − r 2 K θ − θ 2
(q) = ( ) + ( )
by fixed charges ! !
r eq θ eq
bonds angles
MM
V A B q q
n − ij − ij i j
+ [1 + cos(nφ γ)] + +
12 6
Rigid receptor
2 R R ϵR
if accurate enough,
Rigid or semi-rigid ligand " ij ij ij #
dihedrals
i<j
Single-configuration scoring ! !
systematically
Simple solvation model
remove detail
molecular mechanics potential energy
DOCKING
How can we compute a binding affinity
including relevant statistical mechanics?
unbound
bound
time
⌧
K unbound
d dissociation
K
P + L PL
↵
d
constant
/ ⌧
bound
How can we compute a binding affinity
including relevant statistical mechanics?
ANTON
$50M special-purpose supercomputer from D.E. Shaw Research
David E. Shaw
Src:dasatanib
(4 us simulation)
For typical drug off-rates (10-4 s-1),
reliable calculation of binding affinities would require hour trajectories,
requiring ~106 years to simulate.
Shan Y, Kim ET, Eastwood MP, Dror RO, Seeliger MA, Shaw DE. JACS 133:9181, 2011.
�H(x)
Z = e dx (1)
�
⌅
1
�F = � ln Z (2)
�
�
�H(x)
e
�
�(F H(x))
P (x) = = e (3)
�
Z
P (x) exp �(F H (x))
A A A
�(�F �H(x))
alchemical f=ree en�ergy= cealculations pro(4)vide a
�
P (x) exp �(F H (x))
B B B
�
rigorous way to efficiently compute binding affinities
P (x)
A
= exp (�(�F �E(x)) (5)
P (x) multi�ple simulations of alchemical intermediates
B
∆G
bind
P (�E
P + L PL A
= exp (�(�F �E)) (6)
P ( �E) �
B
�
��F ��E(x)
P (x) = P (x)e (7)
�
A B
P + ø Pø
restraint imposition discharging steric decoupling noninteracting
�(�F �E(x))
1 = (e (8)
�
B
Requires orders of magnitude less effort than simulating direct association process,
� 1 ��E⇥(x)
�F = � ln e (9)
� �
� B
but still includes all enthalpic/entropic contributions to binding free energy.
� ⇥
N 1
Z Z Z Z
�
1 N 1 2 3 N Z = dx e−βU(x)
�F = � ln = � ln = �F (10)
1 N � � n n+1 n
!
� Z � Z · Z · · · Z
⇥ ⇥
1 1 2 N 1
n=1
�
⇤
Pioneering work from many: McCammon, van Gunsteren, Kollman, Jorgensen, Chipot, Roux, Boresch, Fujitani, Pande, Shirts, Swope, Christ, Mobley, and many more
Recent review: Chodera, Mobley, Shirts, Dixon, Branson, Pande. Curr Opin Struct Biol 21:150, 2011.
1
alchemical methods IN PRINCIPLE ALSO PROVIDE
ACCESS TO many other useful properties
partition coefficients (logP, logD) and permeabilities
lipitor
selectivity for subtypes or related targets/off-targets
clomifene ERα/β
lead optimization of affinity and selectivity
susceptibility to resistance mutations
also solubilities, polymorphs, etc.
ampicillin β-lactamase
YANK: An open-source, community-oriented platform for

GPU-accelerated free energy calculations
http://www.getyank.org
$1400

NVIDIA GTX Titan X PASCAL
11 single-precision TFLOP/s
OpenMM speedup (GTX Titan X Pascal) over 12-core Xeon X5650 CPU for DHFR
method natoms gromacs CPU OpenMM GPU speedup
GB/SA 2,489 2.54 ns/day 863 ns/day 340x
RF 23,558 18.8 ns/day 607 ns/day 32x
PME 23,558 6.96 ns/day 384 ns/day 55x
http://openmm.org
gromacs benchmarks from http://biowulf.nih.gov/apps/gromacs-gpu.html
A free (libre) open-source, extensible platform for free energy calculations,
algorithmic experimentation, and ligand design
Markov chain Monte Carlo (MCMC) PROVIDES A FLEXIBLE
FRAMEWORK FOR enhancements
composite MCMC sampling scheme for one alchemical state
protonation tautomeric sidechain ...
GHMC
other MC
state MC state MC NCMC
conformational
chemical effects enhanced sampling schemes
sampling
MCMC is combined with replica exchange schemes to decrease correlation times
propagation mixing
state 1
state 2
... ... ... ...
state 3
X
state 4