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AFMoC Enhances Predictivity of 3D QSAR: A Case Study with DOXP-reductoisomerase K. Silber, P. Heidler, T. Kurz, G. Klebe J. Med. Chem. 48(2005) 3547-3563 Journal Club, Presented by Lei Xie Motivations No other papers read Interested in interface of biology and chemistry

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afmoc enhances predictivity of 3d qsar a case study with doxp reductoisomerase

AFMoC Enhances Predictivity of 3D QSAR: A Case Study with DOXP-reductoisomerase

K. Silber, P. Heidler, T. Kurz, G. Klebe

J. Med. Chem. 48(2005) 3547-3563

Journal Club, Presented by Lei Xie

motivations
Motivations
  • No other papers read
  • Interested in interface of biology and chemistry
  • Inspired by a talk on nuclear receptors in the group meeting
slide3

Motivations

  • No other papers read
  • Interested in interface of biology and chemistry
  • Inspired by a talk in the group meeting
outlines
Outlines
  • Basic of 3D-QSAR
  • Discussion of the paper
  • Ideas derived from the paper
concept of 3d qsar
Concept of 3D QSAR
  • Correlates spatially located features across a chemical series with biological activity
  • Procedures:

1. Selection of active conformation

2. Alignment of conformers

3. 3D field calculation in a box and grids

4. Derivation and validation of models

step 1 selection of active conformation
Step 1: Selection of Active Conformation

O

  • Global minimum-enery conformation
  • Active analogs and pharmacophores
  • Protein-ligand complex
  • Intuitions

N

step2 alignment of conformers
Step2: Alignment of conformers
  • Manuel: pharmacophore or moments of inertia
  • Semi-automatic: overlap of steric and electrostatic
  • Fully-automatic: fragment re-construction
  • No alignment

O

N

step 3 3d field calculation in grids
Step 3: 3D field calculation in grids
  • CoMFA:

Lennard-Jones and

Coulomb interactions

  • CoMSIA:

molecular similarity

O

N

step 4 derivation and validation of models
Step 4: Derivation and validation of models

M1 M2 M3 ………. …… Mn

  • Regression, classification or clustering

in a mxn matrix

  • Challenges in

experimental design,

feature selections and model validations

W1

E1

W2

E2

.

.

.

Wm

Em

Y

slide10

Adaptation of fields for Molecular Comparison (AFMoC)A technique to combine protein-ligand interaction and 3D-QSAR to improve selection of active conformation, alignment quality,affinity prediction

concept of afmoc step 1
Concept of AFMoC: Step 1
  • Active conformations and molecular alignments are determined with protein-ligand docking

O

N

concept of afmoc step 2
Concept of AFMoC: Step 2
  • Decompose the general scoring function at each of grids in the binding pocket
  • Interaction fields are yielded by considering the contribution of the binding ligand to each of the grids

O

N

concept of afmoc step 3
Concept of AFMoC: Step 3

M1 M2 M3 ………. …… Mn

  • Regression with experimental affinity data to obtain binding models for specified protein family
  • Balance between the general and specific scoring function depending on the training data

W1

E1

W2

E2

.

.

.

Wm

Em

Y

dxr structures
DXR structures
  • Challenges in structure-based drug design
  • Contains metal-ligand coordination
  • Requires cofactor binding

- Possesses a large flexible loop

training and testing data
Training and Testing Data
  • 27 for model building
  • 14 for testing
  • Testing data set includes functional groups that are not included in the training data
results regression models
Results: Regression Models
  • CoMFA, CoMSIA, and AFMoC all derived statistically-significant models

Figure 5 (a) Experimentally determined binding affinities versus fitted predictions using the derived 3D QSAR models for the training set. Results are shown applying the optimal number of components, which is 5 for CoMFA ( ) and 4 for CoMSIA ( ), respectively. (b) Experimentally determined binding affinities versus fitted AFMoC predictions for the 27 training set DOXP-reductoisomerase inhibitors. Both experimental and calculated values are shown considering only the part of binding affinity (pIC50PLS) used in PLS analysis ( ) or considering the total binding affinity ( ). In addition to the line of ideal correlation, dashed lines are depicted to indicate deviations of one logarithmic unit from ideal prediction.

results prediction power
Results: Prediction Power
  • CoMFA and CoMSIA fail for ligands comprising functional groups not present in or exhibiting minor structural differences from the training
  • AFMoC is capable to correlate structural changes with affinity
  • General docking methods do not perform as well as AFMoC

a Values are given considering only the pair potentials for the prediction or considering also the solvent-accessible surface term (values in parentheses).b Squared correlation coefficient.c In logarithmic units.d Values are given for the optimal number of components, which are 4 for AFMoC, 5 for CoMFA, and 4 for CoMSIA.e Values in italics denote lacking correlation.

results general vs adapted
Results: General vs. Adapted
  • Best mixture of general and adapted model depends on the available training data.

Figure 8 Dependence of squared correlation coefficient (r2) for AFMoC models on the mixing coefficient between original DrugScore potentials ( = 0) and specifically adapted AFMoC fields (PLS-model, = 1).

summary on the paper
Summary on the Paper
  • Protein structure information, even not perfect, is valuable for 3D-QSAR studies
  • Protein family adapted scoring functions is superior to general one
  • A general framework can be extend to other studies
scopes
Scopes
  • Prediction of protein-ligand binding specificity given a protein sequence and a ligand
procedures
Procedures
  • Decomposition of scoring functions with amino acid residues and identification of “hot spot” with known protein structures for a protein family
  • Derivation of regression or classification models that correlate binding ligands and evolutionary profiles at the hot spot
  • Exploration of genome sequences based on sequence/structure alignments and phylogenetic analysis
  • Extension with functional site characterization and analysis
applications
Applications
  • Drug Discovery

Target verifications, Lead discovery, Drug resistance (HIV, Gleevac), Off-target identifications

  • Protein design
  • Nature product identification
slide25
Data
  • Protein structure (not necessary complex)
  • Binding affinity

Availability

  • PDBBind - Structure abundant families (protein kinase, matrix metalloproteases etc.)
  • Structural genomics – structure
  • NIH chemical genomics initiative – binding affinity
  • High throughput screening and combinatorial library
  • Protein chips and other techonologies