Thermodynamic Integration with Enhanced Sampling (TIES) A. P. Bhati, S. Wan, D. W. Wright and P. V. Coveney [email protected]Centre for Computational Science Department of Chemistry University College London 16 th May 2017 Agastya P. Bhati (CCS, UCL) TIES 16 th May 2017 1 21
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Thermodynamic Integration with EnhancedSampling (TIES)
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 2 21
Overview
Brief introduction to the binding affinity and methods to calculate it
Importance of being able to predict binding affinities reliably
Issues with the traditional in silico approaches
Ensemble simulation based approach: TIES
Success story of TIES: Case studies
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 3 21
Overview
Brief introduction to the binding affinity and methods to calculate it
Importance of being able to predict binding affinities reliably
Issues with the traditional in silico approaches
Ensemble simulation based approach: TIES
Success story of TIES: Case studies
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 3 21
Overview
Brief introduction to the binding affinity and methods to calculate it
Importance of being able to predict binding affinities reliably
Issues with the traditional in silico approaches
Ensemble simulation based approach: TIES
Success story of TIES: Case studies
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 3 21
Overview
Brief introduction to the binding affinity and methods to calculate it
Importance of being able to predict binding affinities reliably
Issues with the traditional in silico approaches
Ensemble simulation based approach: TIES
Success story of TIES: Case studies
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 3 21
Overview
Brief introduction to the binding affinity and methods to calculate it
Importance of being able to predict binding affinities reliably
Issues with the traditional in silico approaches
Ensemble simulation based approach: TIES
Success story of TIES: Case studies
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 3 21
Molecular Dynamics (MD) Simulations
An experiment is usually made on a macroscopic sample that containsan extremely large number of atoms or molecules sampling anenormous number of conformations.
In MD simulation, macroscopic properties corresponding to experimentalobservables are defined in terms of ensemble averages.1
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 4 21
Molecular Dynamics (MD) Simulations
An experiment is usually made on a macroscopic sample that containsan extremely large number of atoms or molecules sampling anenormous number of conformations.In MD simulation, macroscopic properties corresponding to experimentalobservables are defined in terms of ensemble averages.1
Free energy is such a measurement
1Coveney & Wan, PCCP, 2016, 18, 30236-30240, DOI: 10.1039/C6CP02349EAgastya P. Bhati (CCS, UCL) TIES 16th May 2017 4 21
Molecular Dynamics (MD) Simulations
An experiment is usually made on a macroscopic sample that containsan extremely large number of atoms or molecules sampling anenormous number of conformations.In MD simulation, macroscopic properties corresponding to experimentalobservables are defined in terms of ensemble averages.1
Free energy is such a measurement1Coveney & Wan, PCCP, 2016, 18, 30236-30240, DOI: 10.1039/C6CP02349E
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 4 21
Binding Affinity
How does it help us?
Ligand binding driven by changes in the Gibbs free energy
The more negative the ∆G the stronger the binding
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 5 21
Binding Affinity
How does it help us?
Ligand binding driven by changes in the Gibbs free energy
The more negative the ∆G the stronger the bindingAgastya P. Bhati (CCS, UCL) TIES 16th May 2017 5 21
In silico free energy calculation methods
Docking methods
Linear Interaction method
MMPBSA+NMODE
Thermodynamic integration
Free energy perturbation (EXP, BAR,MBAR)
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 6 21
Thermodynamic Integration
Hybrid potential function:
H(λ) = λHA + (1 − λ)HB
The coupling parameter, λ, defines theprogress of a system along the path, B toA, as λ is changed from 0 to 1.
∆G =
1∫0
⟨∂H(λ)
∂λ
⟩λ
dλ Alchemical mutation from B(left) to A (right)
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 7 21
Relative Binding Affinity
Relative binding affinity calculations with alchemical mutation: make useof thermodynamic cycle to calculate binding free energy differences
∆∆Gbinding = ∆Gbindingligand2 − ∆Gbinding
ligand1 = ∆Galchligand − ∆Galch
complex
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 8 21
Relative Binding Affinity
Relative binding affinity calculations with alchemical mutation: make useof thermodynamic cycle to calculate binding free energy differences
∆∆Gbinding = ∆Gbindingligand2 − ∆Gbinding
ligand1 = ∆Galchligand − ∆Galch
complex
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 8 21
Relative Binding Affinity
Relative binding affinity calculations with alchemical mutation: make useof thermodynamic cycle to calculate binding free energy differences
∆∆Gbinding = ∆Gbindingligand2 − ∆Gbinding
ligand1 = ∆Galchligand − ∆Galch
complex
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 8 21
Application of binding affinity prediction
Drug designing: Lead optimisation
Searching for a needle in a haystack
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 9 21
Application of binding affinity prediction
Drug designing: Lead optimisationSearching for a needle in a haystack
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 9 21
Application of binding affinity prediction
Drug designing: Lead optimisationSearching for a needle in a haystack
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 9 21
Application of binding affinity prediction
Drug designing: Lead optimisationSearching for a needle in a haystack
High-ThroughputScreening (HTS)
HTS can test thousandsof compounds per day
Cost of HTS issubstantial: $1-10 percompound
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 9 21
Application of binding affinity prediction
Drug designing: Lead optimisationSearching for a needle in a haystack
Virtual Screening:Systematic computer-basedprediction of binding affinityof compounds to proteins
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 9 21
Issues with the available in silico methods
Theories exist - Then predictions are possible, and in principle, weshould be able to apply existing methods in drug screening domains
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 10 21
Issues with the available in silico methods
Theories exist - Then predictions are possible, and in principle, weshould be able to apply existing methods in drug screening domains
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 10 21
Single vs Ensemble MD simulations
The binding free energy and potential energy derivative can vary widely(up to 12 kcal/mol) between two single simulations.
Single simulation: not reproducible, unscientific!
L1Q-LI9 ligand transformation bound to CDK22 Drug-HIV1 protease3
The energy/energy derivatives from ensemble simulations follow welldefined Gaussian distributions.
Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 11 21
Thermodynamic Integration with EnhancedSampling (TIES)
Binding Affinity Calculator (BAC) is a software toolkit which automatesthe implementation of TIES (and ESMACS) methods for binding affinitycalculations
1Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 12 21
Thermodynamic Integration with EnhancedSampling (TIES)
Binding Affinity Calculator (BAC) is a software toolkit which automatesthe implementation of TIES (and ESMACS) methods for binding affinitycalculations
1Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 12 21
TIES: Convergence of errors
Variation of error with different parameters
L1Q-LI9 ligand transformation bound to CDK2
1Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 13 21
TIES: Biomolecular systems studiedCrystal structures of the protein from the Protein Data Bank
CDK2 MCL1PTP1B
Thrombin TYK2Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 14 21
TIES predictions
1Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 15 21
TIES predictions
1Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 15 21
TIES reproducibility
Reproducibility of TIES predictions for transformation between ligandsbound to CDK2
1Bhati, Wan, Wright & Coveney, JCTC, (2017), DOI: 10.1021/acs.jctc.6b00979Agastya P. Bhati (CCS, UCL) TIES 16th May 2017 16 21