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John Mitchell; James McDonagh ; Neetika Nath. Rob Lowe; Richard Marchese Robinson . RF-Score: a Machine Learning Scoring Function for Protein-Ligand Binding Affinities . Ballester, P.J. & Mitchell, J.B.O. (2010) Bioinformatics 26, 1169-1175 .

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slide1

John Mitchell; James McDonagh; NeetikaNath

Rob Lowe; Richard Marchese Robinson

rf score a machine learning scoring function for protein ligand binding affinities
RF-Score: a Machine Learning Scoring Functionfor Protein-Ligand Binding Affinities
  • Ballester, P.J. & Mitchell, J.B.O. (2010) Bioinformatics 26, 1169-1175
slide4

Calculating the affinities of protein-ligand complexes:

  • For docking
  • For post-processing docking hits
  • For virtual screening
  • For lead optimisation
  • For 3D QSAR
  • Within series of related complexes
  • For any general complex
  • Absolute (hard!)
  • Relative
  • A difficult, unsolved problem.
slide6

Three existing approaches …

2. Empirical Functions

slide7

Three existing approaches …

2. Empirical Functions

slide9

How knowledge-based scoring functions have worked …

  • P-L complexes from PDB
  • Assign atoms to types
  • Find histograms of type-type distances
  • Convert to an ‘energy’
  • Add up the energies from all P-L atom pairs
slide12

This conversion of the histogram into an energy function uses a “reverse Boltzmann” methodology.

  • Thus it “assumes” that the atoms of protein and ligand are independent particles in equilibrium at temperature T.
  • For a variety of reasons, these are poor assumptions …
slide13

Molecular connectivity: atom-atom distances are miles from being independent.

  • Excluded volume effects.
  • No physical basis for assuming such an equilibrium.
  • Changes in structure with T are small and not like those implied by the Boltzmann distribution.
slide14

We thought about this …

… and wrote a paper saying

“It’s not true, but it sort of works”

slide15

We thought about this …

… and wrote a paper saying

“It’s not true, but it sort of works”

slide17

Instead of assuming a formula that relates the distance distribution to the binding free energy …

  • … use machine learning to learn the relationship from known structures and binding affinities.
slide18

Instead of assuming a formula that relates the distance distribution to the binding free energy …

  • … use machine learning to learn the relationship from known structures and binding affinities.
  • And persuade someone to pay for it!
slide19

Random Forest

Predicted binding affinity

random forest
Random Forest

● Introduced by Briemann and Cutler (2001)

● Development of Decision Trees (Recursive Partitioning):

● Dataset is partitioned into consecutively smaller subsets

● Each partition is based upon the value of one descriptor

● The descriptor used at each split is

selected so as to optimise splitting

● Bootstrap sample of N objects chosen from the N available objects with replacement

slide21

 The Random Forest is a just forest of randomly generated decision trees …

… whose outputs are averaged to give the final prediction

building rf score
Building RF-Score

PDBbind 2007

building rf score1
Building RF-Score

PDBbind 2007

validation results pdbbind set
Validation results: PDBbind set

 Following method of Cheng et al. JCIM 49, 1079 (2009)

 Independent test set PDBbind core 2007, 195 complexes from 65 clusters

validation results pdbbind set1
Validation results: PDBbind set
  • RF-Score outperforms competitor scoring functions, at least on our test
  • RF-Score is available for free from our group website
slide26

John Mitchell; James McDonagh; NeetikaNath

Rob Lowe; Richard Marchese Robinson

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