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CD and MD. What’s my problem with MD?. Its development has been manifestly unscientific I ts answers (numbers, trajectories, minima) are as unreliable (or more) than simpler methods

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what s my problem with md
What’s my problem with MD?
  • Its development has been manifestly unscientific
  • Its answers (numbers, trajectories, minima) are as unreliable (or more) than simpler methods
  • 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
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 importance- Actually agreeing (numerically) with experiment

Adding consonance- “It’s an idea generator”

Changing the dissonance- Reparameterizing

(+ Effort Justification Paradigm)

am i cd
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

slide5
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
How Galileo Transformed Science

Think something up

  • Resolution
  • Demonstration
  • Experiment

See if it matches available evidence

Think of a new experiment to test it

(to differentiate from old theories)

a galilean value scale for experiments
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
  • 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

Better

a galilean value scale for experiments1
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
  • Unanticipated Retrospective Data
    • SAMPL solvation energies
  • 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

Vast Majority

Better

prospective without controls
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
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
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
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 error1
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 error2
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 truth tm
Fallacy of Selection:The Tanimoto of TruthTM

An Event Happened

An Event Didn’t

Reality

Predictions

ToT = Events that happened and were predicted

Events predicted or happened

the tanimoto of truth
The Tanimoto of Truth

The Tanimoto of Truth

An Event Happened

An Event Didn’t

Reality

Predictions

Published

Especially by Academia

the tanimoto of truth1
The Tanimoto of Truth

The Tanimoto of Truth

An Event Happened

An Event Didn’t

Reality

Predictions

“File Drawer” False Positives

Especially by Industry

the tanimoto of truth2
The Tanimoto of Truth

The Tanimoto of Truth

An Event Happened

An Event Didn’t

Reality

Predictions

False Negatives- Parameterize till publishable

Especially by Academia

the tanimoto of truth3
The Tanimoto of Truth

The Tanimoto of Truth

An Event Happened

An Event Didn’t

Reality

Predictions

True Negatives- Not sexy, “Hempel’s Ravens”

Largely ignored by Academia & Industry

the tanimoto of truth4
The Tanimoto of Truth

The Tanimoto of 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
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 f ables of physics
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

pKa, Tautomers

Finite temperature, MD & Stat Mech

Ergoticity?

The illusion of a ‘perfect” ForceField (that ≠ QM)

typical ff thinking polarization
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
Polarization of Dipoles

-|+

-|+

D

-

+

-|+

-|+

-

+

-

+

-

+

-

+

-

E0

Favorable

Epol

-|+

+|-

D

-

-

-|+

+|-

-

-

-

-

-

-

-

-

-

E0

Unfavorable

Epol

scaling vs polarization
Scaling vs Polarization

Scaling dipoles can only be accurate on average

(with parameterization) not locally!

slide26

Ah, but then there’s AMOEBA

EPIC

Quantum

mechanics

PID

AMOEBA

(“PB”!)

(Jean-Francois Truchon)

Kim Sharp:

JF

applications cation p
Applications: cation-p

Acetylcholinesterase

JF

hydrogen bonds formamide dimer
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.

slide34

Fitting to the electron density

Denny Elking, Tom Darden

slide35

Or……

Increase Dipole from

1.85D to 2.56D

details details
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
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
FKBP-12

Unanticipated Retrospective Data?

fkbp 12 yet again
FKBP-12 Yet Again

Retrospective Data that shapes the theory

contributions to affinity
Contributions to Affinity

VdW

Desolvation

Entropy

Discrete Waters

Coulombic

Polarization

Buried Area

correlations to affinity
Correlations to Affinity

Shape

Buried Area

Entropy

Polarization

VdW

Coulombic

Discrete Waters

Desolvation

Electrostatics

e g vdw
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
E.g. Coulombic

Coulombic Interaction

Brown & Muchmore, JCIM, 2007, (47) 4

Urokinase

slide45

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

DHFR

my observation over 20 years
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)
sampl4 50 solvation energies
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
Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs- D. E. Shaw
  • Where they bind
  • - Confirmed by mutagenesis
  • 2) A surprise in how they bind
  • -pi-charge interactions
  • -not charge-charge
  • 3) Cause of allostery:
    • Charge
    • Binding pocket width
    • -Confirmed by synthesis
slide50
IMHO
  • Where they bind
  • - Confirmed by mutagenesis
  • 2) How they bind
  • -pi-charge interactions
  • -not charge-charge
  • 3) Cause of allostery:
    • Charge
    • Binding pocket width
    • -Confirmed by synthesis

Docking with Glide did almost

as well. Confirmation is WEAK.

2) THIS IS NOT A SURPRISE!

3)

(i) Already known & follows charge

multiplicity exactly.

(ii) –ONE CMPD (better than most!)

slide51
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
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 shoichet relative binding energies in a cavity
Simple System: Shoichet- Relative 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
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
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
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 w ere d one
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
And how I wish they were done:Maximal Disinformation Testing
  • FIRST calculate for two or more methods, e.g. polarization vs static, PB vs MD, MD vs MM
  • Prospectively measure those systems that most distinguish methods- mutual disinformation
  • Adapt theories- no one’s perfect!
  • Repeat steps 1,2 & 3
  • Does a prediction ‘gap’ persist?

E.g. Keplervs Epicycles.

final thoughts
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