CD
and MD
What’s my problem with MD?
1. Its development has been manifestly unscientific
2. Its answers (numbers, trajectories, minima) are as
unreliable (or more) than simpler methods
3. Yet its manifest societal advantages- “physics”,
movies, CPU time, complexity, jargon- lead to
cognitive dissonance (hopeful thinking) concerning
its actual value to drug discovery
CD: Cognitive Dissonance
Wikipedia:Cognitive dissonance theory explains human behavior by positing
that people have a bias to seek consonance between their expectations
and reality. According to Festinger, people engage in a process he termed
"dissonance reduction," which can be achieved in one of three ways:
lowering the importance of one of the discordant factors, adding consonant
elements, or changing one of the dissonant factors. This bias sheds light
on otherwise puzzling, irrational, and even destructive behavior.
Lowering importanceAdding consonanceChanging the dissonance-
Actually agreeing (numerically) with experiment
“It’s an idea generator”
Reparameterizing
(+ Effort Justification Paradigm)
AM I CD?
• Came from Barry’s Lab (the Great PB MD Wars)
• Don’t sell MD (perhaps I’m jealous)
Why should you believe me?
-Don’t write/ need grants
-Don’t need tenure
-PB is not a significant OE income stream
-Been observing MD for > 25 years
-I hired an MD guy (who I sent to China!)
-I manifestly want this to be a better industry
Also..
•
•
•
•
•
•
The fastest PB- DelPhi, ZAP
The fastest surfacing algorithms- GRASP, ZAP
The fastest 3D shape alignment- ROCS, FastROCS
The fastest conformer generator- OMEGA
The fastest, non-stochastic docker- FRED
The fastest (accurate) Surface Area, RMSD, AM1, protein
pka, proton placement..
• If I wanted to do MD, mine would rock
• I believe the effort/reward ratio is (way) too low
How Galileo Transformed Science
Think something up
1. Resolution
See if it matches available evidence
2. Demonstration
1. Experiment
Think of a new experiment to test it
(to differentiate from old theories)
A Galilean Value Scale for Experiments
• Retrospective Data that shapes the theory
– MD, Most of molecular modeling, economics
• Prospective without Controls
– Rich Friesner, Xavier Barril
Better
• Unanticipated Retrospective Data
– SAMPL solvation energies
• Prospective designed with NULL model Controls
– Bertrand Garcia Moreno, protein pKa Collective
– Lyall Isaacs, SAMPL host-guest
• Prospective to distinguish from Best-of-Class Controls
– Nobody
A Galilean Value Scale for Experiments
• Retrospective Data that shapes the theory
– MD, Most of molecular modeling, economics
Vast Majority
• Prospective without Controls
– Rich Friesner, Xavier Barril
• Unanticipated Retrospective Data
– SAMPL solvation energies
Better
• Prospective designed with NULL model Controls
– Lyall Isaacs, SAMPL host-guest
– Bertrand Garcia Moreno, protein pKa Collective
• Prospective to distinguish against Best-of-Class Controls
– Nobody
Prospective Without Controls
• Surgeons coming up with new procedures
– Osteoarthritis & Arthroscopic knee surgery
• US Foreign policy
– Just do something, claim success when it works,
bury it when it doesn’t
• Anecdotal stories
– The “hot hand” phenomena
– I did “X”, it worked.
I did “X”, it worked
Two chief fallacies
(i) Fallacy of Composition
-What else did you actually do
(ii) Fallacy of Selection
-File Drawer effect (False Positives)
-Parameterization (implicit or explicit)
to the result (False Negatives)
Fallacy of Composition
• Method X, e.g. MD, is but one part of a
multipart process (filtering, chemists
inspection, database bias)- success is claimed
for X alone
• The same procedure with X replaced with a
different method is never done/ presented
Example of Composition Error
• We predicted affinity with MM/QM and “It
Worked”
• Was QM getting you anything?
• Did you do MM with QM-level charges,
multipoles? MM alone? A scoring function?
Example of Composition Error
• We used a polarizable force field and got
these results for the (SAMPL4) host-guest
systems. “It Worked”, so polarization worked.
• Did you also try it without polarization? With
better quality charges? With equivalent CPU
time but without polarization (more
sampling)?
Example of Composition Error
• We ran MD for a bit, looked at how the ligands
wiggled and designed six drugs (Christopher
Bayly & others at Merck Frosst)
• Did you compare to MM? To other simple
heuristics? Without any chemists input?
• It’s not “Science” until someone else does it
Fallacy of Selection:
The Tanimoto of TruthTM
An Event Happened
Reality
Predictions
An Event Didn’t
0
1
1
0
1
0
0
1
0
1
1
1
1
0
0
1
0
1
0
0
ToT = Events that happened and were predicted
Events predicted or happened
The
TheTanimoto
Tanimotoof
ofTruth
Truth
An Event Happened
Reality
Predictions
An Event Didn’t
0
1
1
0
1
0
0
1
0
1
1
1
1
0
0
1
0
1
0
0
Published
Especially by Academia
The
TheTanimoto
Tanimotoof
ofTruth
Truth
An Event Happened
Reality
Predictions
An Event Didn’t
0
1
1
0
1
0
0
1
0
1
1
1
1
0
0
1
0
1
0
0
“File Drawer” False Positives
Especially by Industry
The
TheTanimoto
Tanimotoof
ofTruth
Truth
An Event Happened
Reality
Predictions
An Event Didn’t
0
1
1
0
1
0
0
1
0
1
1
1
1
0
0
1
0
1
0
0
False Negatives- Parameterize till publishable
Especially by Academia
The
TheTanimoto
Tanimotoof
ofTruth
Truth
An Event Happened
Reality
Predictions
An Event Didn’t
0
1
1
0
1
0
0
1
0
1
1
1
1
0
0
1
0
1
0
0
True Negatives- Not sexy, “Hempel’s Ravens”
Largely ignored by Academia & Industry
The
TheTanimoto
Tanimotoof
ofTruth
Truth
• “Similarity” methods, Docking, Machine Learning
• All are judged by some kind of ToT
• Quantification for MD ‘events’? Never.
• MD is mostly uncontrolled, anecdotal & unscientific
Psychology,
Philosophy,
Social Dynamics
Underlying Physics,
Examination of
Successes
Molecular Dynamics:
Types of Applications
1) Global sampling- thermodynamic averages
-FEP etc. Absolute or Relative Energies
2) Simulate time evolution (movies)
-D.E. Shaw, Vijay Pande- Mechanism
3) Local sampling (thermally accessible barriers)
-Bayly & co., WaterMap, MM/PBSA. Qualitative
Assessment
Thermodynamic energies and
Fables of Physics
“We all know that if we had the perfect force field and simulated
for an infinite time, we’d get the right answer”- Woody Sherman,
ACS San Francisco, March 24th, 2010
1) pKa, Tautomers
2) Finite temperature, MD & Stat Mech
3) Ergoticity?
4) The illusion of a ‘perfect” ForceField (that ≠ QM)
Typical FF Thinking: Polarization
• Polarization is tricky
• But it makes dipoles bigger, e.g. water
– 1.85D (vacuum)  2.5~2.6D (condensed phase)
• So therefore increase charges by ~15%
– E.g. use HF-6-31G*
• Now molecules are roughly correct
Polarization of Dipoles
-|+
E

0
-|+
E

0
-|+
D-
+

-|+
+
-
+
+
+
Favorable
+|Unfavorable
-
-|+
-
Epol
-

-|+
D
-
-
-
-
-
-
+|-
-
-
Epol
Scaling vs Polarization
Alignment
Scaling Charges
Polarization
Favorable
Unfavorable
Lowers Energy
Lowers Energy
Raises Energy
Lowers Energy
Scaling dipoles can only be accurate on average
(with parameterization) not locally!
Ah, but then there’s AMOEBA
EPIC
Quantum
mechanics
(“PB”!)
5
PID
AMOEBA
5
-75
-50
-25
0
25
50
4
3
5
-10
-75
-50
-25
0
25
50
4
3
0
+
2
-10
-75
-50
-25
0
25
50
4
3
+
2
0
-10
0
+
2
-75
-75
1
1
0
C
H
C
0
+
-3
C
H
C
H
0
-1
+
-2
-3
-4
-4
-5
-5
-5
-4
-3
-2
-1
0
1
2
3
4
5
X (angstrom)
-75
10
Y (angstrom)
Y (angstrom)
Y (angstrom)
-1
-2
1
10
H
C
H
C
H
10
-1
+
-2
-3
-4
-5
-4
-3
-2
-1
0
X (angstrom)
1
2
3
4
5
-5
-5
-4
-3
-2
-1
0
1
2
3
4
5
X (angstrom)
(Jean-Francois Truchon)
Kim Sharp:
JF
Applications: cation-p
0
Acetylcholinesterase
-10
-20
O
O
N+
NH
-30
B3LYP Li+
B3LYP Na+
B3LYP K+
polarizable (DRESP, P2E)
non-polarizable (RESP)
-40
-50
-60
-70
Electrostatic i
2.0
2.5
3.0
3.5
4.0
4.5
Cation / benzene distance (angstrom)
JF
Hydrogen Bonds: Formamide dimer
“ Close agreement between the orientation dependence of hydrogen bonds
observed in protein structures and quantum mechanical calculations”
A. V. Morozov, T. Kortemme, K. Tsemekhman and D. Baker,
PNAS, Volume 101, page 6946, 2004.
Method
δ(HA)
ψ
ϴ
X
DFT
1.94
112.34
159.43
-177.51
MP2
1.97
110.49
155.33
-179.49
HF
2.10
138.16
170.94
-179.54
CHARMM27
1.82
170.25
170.83
-106.83
OPLS-AA
1.75
165.04
175.61
145.12
MM3-2000
1.98
121.16
161.07
149.63
PDB
1.93
115.00
175.00
175.00
Geometry optimizations starting from
the Baker MP2 minimum
Geometry optimizations starting from
the Baker MP2 minimum
Geometry optimizations starting from
the second MP2 minimum
Geometry optimizations starting from
the second MP2 minimum
Ah, but then there’s AMOEBA
R(O..H) (Å)
0
E_ele (kcal/mol)
1.9
2.2
2.5
2.8
3.1
3.4
3.7
-3
QM*
*CCSD/aug-cc-pVTZ
Pt. Charge
Pt. Octupole
-6
-9
Fitting to the electron density
Denny Elking, Tom Darden
Model
Point Monopole
Point Dipole
Point Quadrupole
Point Octupole
Exponential Monopole
Exponential Dipole
Exponential Quadrupole
Exponential Octupole
CCSD/aug-cc-pVTZ
Electrostatic Energy (kcal/mol)
-4.33
-5.81
-6.36
-6.31
-7.68
-8.32
-8.52
-8.18
-8.23
Or……
Model
Point Monopole
Point Dipole
Point Quadrupole
Point Octupole
Exponential Monopole
Exponential Dipole
Exponential Quadrupole
Exponential Octupole
CCSD/aug-cc-pVTZ
Electrostatic Energy (kcal/mol)
-4.33
-5.81
-6.36
-6.31
-7.68
-8.32
-8.52
-8.18
-8.23
Increase Dipole from
1.85D to 2.56D
Details, Details..
1) Just incorporate Volume Terms (PB)
2) And all those other terms:
- Exchange interactions
- VdW anisotropy
- pKa & Tautomers
- Cross-terms between valence and non-bonded
- Three (N) body terms….
Eventually it’ll be right! Woody’ll be right.
Inconceivable it can’t ever be right. (Wolynes)
Concrete MD Examples
• Binding Energies- Shirts
- Also Solvation (Simpler system)
• Protein Trajectories- Shaw
- Also Peptides (Simpler systems)
• “Minimization” – Shoichet
- Is a simple system
FKBP-12
Unanticipated Retrospective Data?
FKBP-12: Shirts et al
(Thesis)
-5
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
Simulation (kcals/mol)
-7
-8
-9
-10
-11
-12
-13
-14
-15
Experiment (kcals/mol)
FKBP-12 Again
FKBP-12: Shirts et al
(My plot)
-5
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-6
Simulations (Kcals/Mol)
-7
-8
-9
-10
-11
-12
-13
-14
Experiment (Kcals/Mol)
FKBP-12 Yet Again
Retrospective Data that shapes the theory
FKBP-12: Shirts et al
(My plot: No Long Range Correction)
-2
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-3
-4
Simulations (Kcals/Mol)
-5
-6
-7
-8
-9
-10
-11
-12
-13
-14
Experiment (Kcals/Mol)
Contributions to Affinity
Desolvation
Entropy
VdW
Discrete
Waters
Coulombic
Polarization
Buried
Area
Shape
Correlations to Affinity
Buried
Area
Entropy
VdW
Polarization
Coulombic Discrete
Waters
Desolvation
Electrostatics
E.g. VdW
Train on 17 HIV-1 Protease Inhibitors
1) Minimization (MM2X)
2) pIC50=-0.15*Einter-8.1
Prospectively used on 16 more
E.g. Coulombic
• Urokinase
Brown & Muchmore, JCIM, 2007, (47) 4
Coulombic Interaction
E.g. Buried Area
“Fast and Accurate Predictions of Binding Free Energies using MM-PBSA
and MM-GBSA”Rastelli, G., Del Rio, A.,Degliesposti,G., Sgobba, M.
J. Comp. Chem. Vol 31, #4, pg 797-810
Buried Area
MM-PBSA
0
0
-15
-10
-5
0
-10
DHFR
-20
-30
-40
y = 2.339x - 11.308
R² = 0.8494 -50
-60
Experimental Binding (kcal/mol)
-20
Buried Area Energy (kcal/mol)
Predicted Binding (kcal/mol)
-20
-15
-10
-5
0
-1
-2
-3
-4
y = 0.3181x - 0.7159-5
R² = 0.7474
-6
-7
Expt. Binding (kcal/mol)
My observation over 20 years
• For congeneric series, something basic often
correlates, sometime well (VdW, Coulombic)
• For non-congeneric usually nothing works
• If something works for non-congenerics, it’s
usually something basic (mass, buried area)
Simpler System: Solvation
#
Compounds
MD RMSE
kCal/mol
PB RMSE
kCal/mol
1.76 (Me)
SAMPL0
17
1.35 (Vijay)
SAMPL1
56
3.6 (Mobley) 2.2 (Me)
SAMPL2
40
2.4 (Jay)
2.1 (Ben)
SAMPL4: 50 Solvation Energies
My PB Method
Best MD
QM + Specific
Group-wise
Parameterization
Structural basis for modulation of a G-protein-coupled
receptor by allosteric drugs- D. E. Shaw
1) Where they bind
- Confirmed by mutagenesis
2) A surprise in how they bind
-pi-charge interactions
-not charge-charge
3) Cause of allostery:
(i) Charge
(ii) Binding pocket width
-Confirmed by synthesis
IMHO
1) Where they bind
1) Docking with Glide did almost
- Confirmed by mutagenesis as well. Confirmation is WEAK.
2) How they bind
-pi-charge interactions
-not charge-charge
2) THIS IS NOT A SURPRISE!
3) Cause of allostery:
(i) Charge
(ii) Binding pocket width
-Confirmed by synthesis
3)
(i) Already known & follows charge
multiplicity exactly.
(ii) –ONE CMPD (better than most!)
Also..
• Local ionizable residues never (de)protonate
– Binding +3 ligands
• NMS was modeled, not simulated
• Experimental errors claimed are <0.1 kcal in
vivo
Simpler Story- Peptides
• Poly-Ala propensities (2010)
– Have to modify FF to get helicity right
• Side-chain conformation preferences (2012)
– Little agreement between force-fields
– Poor agreement with crystals (2013)
• H-bond geometries (2005)
– Flawed Baker study
• Beta-hairpin simulations (2012)
– Little agreement between force-fields
Simple System: ShoichetRelative binding energies in a cavity
A signal!
Poses selected, not found, so
is this dynamics or minimization?
Maybe not!
NULL MODELS
RMSE from Phenol =
2.5 kcal/mol
RMSE from from Catechol =
1.1 kcal/mol
RMSE of the “NULL” hypothesis = 1.2 kcal/mol
From “closest” Phenol|Catechol = 0.8 kcal/mol
One, Inescapable, Conclusion
• We cannot calculate the energies of protein
microstates with any accuracy
• It is unclear even how bad we are
• Even ranking must be suspect
Of Dubious Value
• Ranking Ligands, Absolute or Relative
• Flexible Docking
• Protein folding to atomic resolution
• Evaluating unfolded states
• Excursions from the crystal structure
So how can we fold (small) proteins?
• Luck- are small proteins self-selectingly robust?
• Some parameterization (Shaw)
• Stability of kinetic pathways might be more
robust than energetics suggest (Pande)
?
But what’s the alternative?
• To Local Minimization
– Sample (MC, Low Mode etc) and minimize
• To Energy evaluation
– Exhaustively sample and minimize
• To time evolution
– Elastic network? Low mode dynamics?
– Run MD!
Experiments I Wish Were Done
• Protein Crystallography
– Predict the room temperature density
• Small molecule NMR
– Predict the dominant low energy conformer
• Protein Electrostatics
– Predict potentials in the active site
• Host-guest systems
– Binding energies, salt effects
And how I wish they were done:
Maximal Disinformation Testing
1. FIRST calculate for two or more methods, e.g.
polarization vs static, PB vs MD, MD vs MM
2. Prospectively measure those systems that most
distinguish methods- mutual disinformation
3. Adapt theories- no one’s perfect!
4. Repeat steps 1,2 & 3
5. Does a prediction ‘gap’ persist?
E.g. Kepler vs Epicycles.
Final Thoughts
• I’d love MD to work! Make my job easier
• It doesn’t. At least not as advertised/ believed
• It’s nature (“physics”, big calculations, movies)
leads to overconfidence
• Until a more scientific approach is adopted it’s
unlikely to get better. GPUs won’t save MD
• What’s needed is Maximal Disinformation
Testing & Model systems