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C372_L12 - Molecular Modeling Conformational Molecular...

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Molecular Modeling:  Conformational Molecular Field  Analysis (CoMFA) C372 Dr. Kelsey Forsythe
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CoMFA • Cramer and Milne (1979) – Comparison of molecules by alignment and field  generation • Wold (1986) – Proposes using PLS instead of PCA for  overrepresented (1000’s of field non-orthogonal  “variables”) problem (correlate field values with  activities) • Cramer, Patterson and Bunce (1988) – Introduced CoMFA
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CoMFA Assumptions • Activity is directly related to structural  properties of system • Structural properties determined by non- bonding forces
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Outline of CoMFA • Hypothesize mechanism for binding  – Structure of binding site – Most important/difficult • Find equilibrium geometry  • Construct lattice or grid of points  • Compute interaction of probe with molecule at  each point • Apply PLS  • Predict
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CoMFA  Structural Focus  • Hypothesize mechanism for binding  – Structure of active site and/or common pharmacophore  between all compounds – Most important/difficult – Structural errors propagate to later stages • Superpose structures – SEAL • Similarity index
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CoMFA  Structural Focus  Poor alignment Better alignment
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CoMFA Equilibrium Geometry • Find equilibrium geometry  – Ab Initio, Semi-Empirical or Molecular Mechanics – Method depends • Size  • Accuracy
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CoMFA Lattice Construction • Construct lattice or grid of points for field analysis  Steroid (1 representative conformer shown) 14 x 11 x 7 = 1078 points
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CoMFA Field Data Generation • Compute interaction of probe with molecule at  each point – Interaction is typically non-covalent (e.g. non-bonding  forces) • Steric, electrostatic and hydrophobic – Probe depends on interaction • Kim et. al. – H +  (electrostatic) CH 3  (steric) H 2 O (hydrophobic)
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CoMFA Field Data Generation • Compute interaction of probe with molecule at  each point N calc =N grid  * N cmpds * N probes
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Outline of CoMFA • Apply PLS – Problem overrepresented in field variables/descriptors – Sieve most important field components (PCA) – Use in regression 
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